| Title: | User Oriented Plotting Functions |
|---|---|
| Description: | Plots with high flexibility and easy handling, including informative regression diagnostics for many models. |
| Authors: | Werner A. Stahel [aut, cre], Martin Maechler [ctb]
|
| Maintainer: | Werner A. Stahel <[email protected]> |
| License: | GPL-2 |
| Version: | 1.2 |
| Built: | 2024-10-25 03:00:01 UTC |
| Source: | https://github.com/cran/plgraphics |
Calculates the sqrt arc sine of x/100, rescaled to be in the unit
interval.
This transformation is useful for analyzing percentages or proportions
of any kind.
asinp(x)asinp(x)
x |
vector of data values |
vector of transformed values
This very simple function is provided in order to simplify
formulas. It has an attribute "inverse" that contains
the inverse function, see example.
Werner A. Stahel, ETH Zurich
asinp(seq(0,100,10)) ( y <- asinp(c(1,50,90,95,99)) ) attr(asinp, "inverse")(y)asinp(seq(0,100,10)) ( y <- asinp(c(1,50,90,95,99)) ) attr(asinp, "inverse")(y)
Adjusts the character size cex to number of observations
charSize(n)charSize(n)
n |
number of observations |
The function simply applies min(1.5/log10(n), 2)
A scalar, defining cex
Werner A. Stahel
charSize(20) for (n in c(10,20,50,100,1000)) print(c(n,charSize(n)))charSize(20) for (n in c(10,20,50,100,1000)) print(c(n,charSize(n)))
Drop values outside a given range
clipat(x, range=NULL, clipped=NULL)clipat(x, range=NULL, clipped=NULL)
x |
vector of data to be clipped at |
range |
range, a numerical vector of 2 elements |
clipped |
if |
As the input x, with pertinent elements dropped or replaced
Werner A, Stahel
clipat(rnorm(10,8,2), c(10,20), clipped=NA)clipat(rnorm(10,8,2), c(10,20), clipped=NA)
Finds colors that are ‘equivalent’ to the colors given as the first argument, but more pale or less pale
colorpale(col = NA, pale = NULL, rgb = FALSE, ...)colorpale(col = NA, pale = NULL, rgb = FALSE, ...)
col |
a color or a vector of colors for which the pale version should be found |
pale |
number between -1 and 1 determining how much paler the result should
be. If |
rgb |
should result be expressed in 'rgb' form? If |
... |
further arguments passed on to |
The function increases rgb coordinates of colors ‘proportionally’: crgb <- t(col2rgb(col)/255); rgb(1 - pale * (1 - crgb))
character vector: names of colors to be used as color argument for graphical functions.
Werner A. Stahel, ETH Zurich
( t.col <- colorpale(c("red","blue")) ) plot(0:6, type="h", col=c("black","red","blue",t.col, colorpale(t.col)), lwd=5)( t.col <- colorpale(c("red","blue")) ) plot(0:6, type="h", col=c("black","red","blue",t.col, colorpale(t.col)), lwd=5)
Vectors of color names to be used, mainly for distinguishing groups
c.colorsc.colors
none
vector of color names
Werner A. Stahel, ETH Zurich
c.colorsc.colors
Calculates quantiles of a conditional distribution, as well
as corresponding random numbers. The condtion is simply to restrict
the distribution (given by dist) to a range (given by
x)
condquant(x, dist = "normal", mu = 0, sigma = 1, randomrange = 0.9)condquant(x, dist = "normal", mu = 0, sigma = 1, randomrange = 0.9)
x |
matrix with 2 columns or vector of length 2 giving the limits for the conditional distribution |
dist |
(unconditional) distribution. Currently, only
|
mu, sigma
|
locarion and scale parameter of the distribution |
randomrange |
random numbers from the conditional distribution
are drawn for the inner |
Matrix consisting of a row for each row of x for which
x[,1] differs from x[,2] and the following columns:
median |
Median |
lowq, uppq
|
lower and upper quartiles |
random |
random number according to the conditional distribution (one for each row) |
prob |
probability of the condition being true |
index |
(row) index of the corresponding entry in the input 'x' |
Attribute distribution comprises the arguments
dist, mu, sigma.
Werner A. Stahel, Seminar for Statistics, ETH Zurich
condquant(cbind(seq(-2,1),c(0,1,Inf,1)))condquant(cbind(seq(-2,1),c(0,1,Inf,1)))
Survival of Premature Infants to be modeled using 5 potential explanatory variables.
data("d.babysurvival") data("d.babysurvGr")data("d.babysurvival") data("d.babysurvGr")
d.babysurvival:
A data frame with 246 observations on the following 6 variables.
Survivalbinary, 1 means the infant survived
Weightbirth weight [g]
Agepregnancy in weeks
Apgar1A score indication the fitness of the infant at birth, scores 0 to 9
Apgar5alternative score
pHblood pH
d.babysurvGr:
Grouped data: Number of Infants that died and survived for each class
of birth weight.
nNumber of infants in the weight class
Survival.0, Survivl.1Number of infants that died and survived, respectively
Weightbirth weight
Hibbard (1986)
data(d.babysurvival) summary(d.babysurvival) rr <- glm(Survival~Weight+Age+Apgar1, data=d.babysurvival, family="binomial") plregr(rr, xvar= ~Age+Apgar1)data(d.babysurvival) summary(d.babysurvival) rr <- glm(Survival~Weight+Age+Apgar1, data=d.babysurvival, family="binomial") plregr(rr, xvar= ~Age+Apgar1)
Standardized fertility measure and socio-economic indicators for
each of 182 districts of Switzerland at about 1888.
This is an extended version of the swiss dataset of standard R.
data("d.birthrates") data("d.birthratesVars")data("d.birthrates") data("d.birthratesVars")
d.birthrates:
A data frame with 182 observations on the following 25 variables.
fertilityCommon standardizedfertility measure, see details
fertTotalAlternative fertility measure
infantMortInfant mortality
catholicpercentage of members of the catholic church
single24percentage of women aged 20-24 who are single
single49percentage of women aged 45-49 who are single
eAgricProportion male labor force in agriculture
eIndustryProportion male labor force in industry
eCommerceProportion male labor force in trade
eTransportProportion male labor force in transportation
eAdminProportion male labor force in public service
germanpercentage of German
frenchpercentage of French
italianpercentage of Italian
romanshpercentage of Romansh
gradeHighProp. high grade in draftees exam
gradeLowPropr. low grade in draftees exma
educHighProp. draftees with > primary educ.
bornLocalProportion living in commune of birth
bornForeignProportion born in foreign country
sexratioSex ratio (M/F)
cantonCanton Name
districtDistrict Name
altitudealtitude in three categories: low, medium, high
languagedominating language: german, french, italian, romansh
d.birthratesVars:
Data.frame that contains the descriptions of the variables just read.
?swiss says:
(paraphrasing Mosteller and Tukey):
Switzerland, in 1888, was entering a period known as the
'demographic transition'; i.e., its fertility was beginning to
fall from the high level typical of underdeveloped countries.
The exact definition of fertility is as follows.
fertility = 100 * B_l/ sum m_i f_i, where
B_l = annual legitimate births,
m_i = the number of married women in age interval i,
and f_i = the fertility Hutterite women in the same age interval.
"Hutterite women" are women in a population that is known to be extremely
fertile.
Stillbirths are included.
https://opr.princeton.edu/archive/pefp/switz.aspx
see source
data(d.birthrates) ## maybe str(d.birthrates) ; plot(d.birthrates) ...data(d.birthrates) ## maybe str(d.birthrates) ; plot(d.birthrates) ...
Blasting causes tremor in buildings, which can lead to damages. This dataset shows the relation between tremor and distance and charge of blasting.
data("d.blast")data("d.blast")
A data frame with 388 observations on the following 7 variables.
noIdentification of the date and time
dateDate in Date format. (The day and month are correct, the year is a wild guess.)
datetimeDate and time in the format '%d.%m. %H:%M'
deviceNumber of measuring device, 1 to 4
chargeCharge of blast
distanceDistance between blasting and location of measurement
tremorTremor energy (target variable)
locationCode for location of the building,
loc1 to loc8
The charge of the blasting should be controled in order to
avoid tremors that exceed a threshold.
This dataset can be used to establish the suitable rule:
For a given distance, how large can charge be in order
to avoid exceedance of the threshold?
Basler and Hoffmann AG, Zurich
data(d.blast) showd(d.blast) plyx(tremor~distance, psize=charge, data=d.blast) rr <- lm(logst(tremor)~location+log10(distance)+log10(charge), data=d.blast) plregr(rr) t.date <- as.POSIXlt(paste("1999",d.blast$datetime,sep="."), format='%Y.%d.%m. %H:%M')data(d.blast) showd(d.blast) plyx(tremor~distance, psize=charge, data=d.blast) rr <- lm(logst(tremor)~location+log10(distance)+log10(charge), data=d.blast) plregr(rr) t.date <- as.POSIXlt(paste("1999",d.blast$datetime,sep="."), format='%Y.%d.%m. %H:%M')
The abundance of cocolith shells can be used to infer environmental conditions in epochs corresponding to earlier epochs. This data set contains the core location, the relative abundance of Gephyrocapsa morphotypes and the sea surface temperatures from all deep see cores used in this study.
data("d.fossileShapes") data("d.fossileSamples")data("d.fossileShapes") data("d.fossileSamples")
d.fossilShapes:
A data frame with 5864 observations on the following 15
variables:
Identification and location of the sample:
SampleIdentification number of the sample
SnameIdentification code
Magnification(technical)
Shape features and recommended transformations:
Anglebridge angle
Length, Widthlengtha and width of the shell
CLength, CWidthlength and width of the 'central area'
Cratioratio between width and length of the central area
sAnglesqrt of Angle
lLengthlog10(Length)
rWidth, rCLength, rCWidthrelative measures,
percentage of Length
CratioCWidth/Clength
ShapeClassshape class as defined in the cited paper,
classes ar
CM < CC < CT < CO < CE < CL
d.fossilSamples:
A data frame with 108 observations on the following 32
variables:
Identification and location:
SampleIdentification number of the sample (as above)
SnameIdentification code
Latitude, LongitudeCoordinates of the location
RegionOcean: Pacific, Atlantic, Indian.Ocean
SDepthsample depth below soil surface [cm]
WDepthWater depth [m]
Nnumber of specimen measured
Shape features as above, averaged.
(This is the reason for introducing transformed variables above:
The transformed values are averaged.)
CM, CC, CT, CO, CE, CLpercentages of shape classes in the sample
Environment:
SSTSea Surface Temperature, mean, [deg C]
SST.Spring, SST.Summer, SST.Fall,
SST.Winter
... in each season
Chlorophyll, lChlorophyllChlorophyll content
[microgram/L] and log10 of it
SalinitySalinity of the sea water
The paradigm of research associated with this dataset is the following: Datasets of this kind are used to establish the relationship between the shell shapes of cocoliths (species Gephyrocapsa) from the most recent sediment layer with actual environmental conditions. This relationship is then used to infer environmental conditions of earlier epochs from the shell shapes from the corresponding layers.
The analysis presented in the paper cited below consisted of first introducing classes of shells based on the shapes and then use the relative abundance of the classes to predict the environmental conditions.
J\"org Bollmann, Jorijntje Henderiks and Bernhard Brabec (2002). Global calibration of Gephyrocapsa coccolith abundance in Holocene sediments for paleotemperature assessment. Paleoceanography, 17(3), 1035
J\"org Bollmann (1997). Morphology and biogeography of Gephyrocapsa coccoliths in Holocene sediments. Marine Micropaleontology, 29, 319-350
data(d.fossileShapes) names(d.fossileShapes) data(d.fossileSamples) plyx(sqrt(Angle) ~ SST, data=d.fossileSamples)data(d.fossileShapes) names(d.fossileShapes) data(d.fossileSamples) plyx(sqrt(Angle) ~ SST, data=d.fossileSamples)
Hourly air pollution measurements from a station in the city center of Zurich, in a courtyard, for the whole year 2016, resulting in 8784 measurements of the two pollution variables ozone and nitrogen dioxyde, the three weather variables temperature, radiation and precipitation, and 8 variables characterizing the date.
pollZH16d is the subset of measurements for hour=15.
data("d.pollZH16")data("d.pollZH16")
A data frame with 8784 observations on the following 13 variables.
datedate of the measurement
hourhour of the measurement
O3Ozone
NO2Nitroge dioxyde
temptemperature
radsolar radiation
precprecipitation
dateshorttwo letter identification of the day. A-L encodes the month; 1-9, a-x encodes the day within month.
weekdayday of the week
monthmonth
sumhalfindicator for summer half year (April to Sept)
sundaylogical: indicator for Sunday
daytypea factor with levels work for working
day, Sat and Sun
Legal threshold for NO2 in the EU:
The threshold of 200 micrograms/m3 must not be exceeded by
more than 18 hourly measurements per year.
Source: Umweltbundesamt, Germany
http://www.umweltbundesamt.de/daten/luftbelastung/stickstoffdioxid-belastung#textpart-2
Bundesamt fur Umwelt (BAFU), Schw. Eidgenossenschaft
https://www.bafu.admin.ch/bafu/de/home/themen/luft/zustand/daten/datenabfrage-nabel.html
The data set has been generated by downloading the files
for the individual variables,
converting the entries with hour==24 to hour==0 of the
following day and restricting the data to year 2016.
data(d.pollZH16) dp <- d.pollZH16 names(dp) dp$date <- gendateaxis(date=dp$date, hour=dp$hour) plyx(O3+NO2~date, data=dp, subset= month=="May", type="l") dp$summer <- dp$month %in% c("Jun","Jul","Aug") dp$daylight <- dp$hour>8 & dp$hour<17 plmatrix(O3~temp+logst(rad)+logst(prec), data=dp, subset = summer & daylight)data(d.pollZH16) dp <- d.pollZH16 names(dp) dp$date <- gendateaxis(date=dp$date, hour=dp$hour) plyx(O3+NO2~date, data=dp, subset= month=="May", type="l") dp$summer <- dp$month %in% c("Jun","Jul","Aug") dp$daylight <- dp$hour>8 & dp$hour<17 plmatrix(O3~temp+logst(rad)+logst(prec), data=dp, subset = summer & daylight)
This time series of chemical concentrations can be used to research the activities of photosynthesis and respiration in a river.
data("d.river")data("d.river")
A time series with 9792 observations (10 minutes interval) on the following 12 variables.
dateDate of the observation, class Date
hourHour
pHpH
O2concentration of Oxygen
O2SOxygen saturation value
TTemperature [deg C]
H2CO3Carbon dioxide concentration in the water
CO2atmCarbon dioxide concentration in the atmosphere
Qflow
susunshine
prprecipitation
raradiation
This is not a time series in the sense of ts of R.
The date-time information is contained in the variables date and
hour.
The measurements have been collected in the river Glatt near Zurich.
data(d.river) range(d.river$date) t.i <- d.river$date < as.Date("2010-03-31") plyx(~date, ~O2, data=d.river, subset=t.i & hour==14, smooth=FALSE) d.river$Date <- gendateaxis(d.river$date, hour=d.river$hour) plyx(O2~Date, data=d.river, subset=t.i, type="l") plyx(O2+T+ra~Date, data=d.river, subset=t.i & hour==14, smooth.par=0.5, smooth.xtrim=0.03, ycol=c(O2="blue",ra="red"))data(d.river) range(d.river$date) t.i <- d.river$date < as.Date("2010-03-31") plyx(~date, ~O2, data=d.river, subset=t.i & hour==14, smooth=FALSE) d.river$Date <- gendateaxis(d.river$date, hour=d.river$hour) plyx(O2~Date, data=d.river, subset=t.i, type="l") plyx(O2+T+ra~Date, data=d.river, subset=t.i & hour==14, smooth.par=0.5, smooth.xtrim=0.03, ycol=c(O2="blue",ra="red"))
Check if formula is valid and, if it contains a
| character, idenitfy regressors and conditional variables
deparseCond(formula)deparseCond(formula)
formula |
A model formula, possibly containing a |
Returns the formula with the following attributes:
y |
"vertical" (response) variable(s) |
x |
"horizontal" (regressor) variable(s) |
a |
(first) conditional variable, if any |
b |
second conditional variable, if any |
This function is typically used for conditional plots and mixed models
Werner A. Stahel
deparseCond(yy ~ xx) deparseCond(yy ~ xx | aa + bb) deparseCond(y1 + y2 ~ x1 + log(x2) | sqrt(quantity)) plyx(Sepal.Width~Sepal.Length | Species, data=iris)deparseCond(yy ~ xx) deparseCond(yy ~ xx | aa + bb) deparseCond(y1 + y2 ~ x1 + log(x2) | sqrt(quantity)) plyx(Sepal.Width~Sepal.Length | Species, data=iris)
The attributes doc and tit describe an object, typically
a data frame or a model. tit should be a short description (title),
doc should contain all documentation useful to identify
the origin and the changes made to the object.
The doc and tit functions set them and extract these
attributes.
doc(x) tit(x) doc(x) <- value tit(x) <- valuedoc(x) tit(x) doc(x) <- value tit(x) <- value
x |
object to which the |
value |
character vector ( |
Plotting and printing functions may search for the tit
attribute or even for the doc attribute, depending on
c.env$docout.
doc(x) <- text will append the existing doc(x) text to
the new text unless its first element equals (the first element
of) text.
(This avoids piling up the same line by unintended multiple call to
doc(x) <- value with the same value.)
If the first element of text equals "^",
the first element of doc(x) is dropped.
tit(x) <- string replaces tit(x) with string.
doc and tit return the respective attributes of object
x
Werner A. Stahel, ETH Zurich
data(d.blast) doc(d.blast) doc(d.blast) <- "I will use this dataset in class soon." doc(d.blast)data(d.blast) doc(d.blast) doc(d.blast) <- "I will use this dataset in class soon." doc(d.blast)
Allows for dropping observations (rows) determined by row names or factor levels from a data.frame or matrix.
dropdata(data, rowid = NULL, incol = "row.names", colid = NULL)dropdata(data, rowid = NULL, incol = "row.names", colid = NULL)
data |
a data.frame of matrix |
rowid |
vector of character strings identifying the rows to be dropped |
incol |
name or index of the column used to identify the observations (rows) |
colid |
vector of character strings identifying the columns to be dropped |
The data.frame or matrix without the dropped observations and/or variables. Attributes are passed on.
Ordinary subsetting by [...,...] drops attributes like
doc or tit. Furthermore, the convenient
way to drop rows or columns by giving negative indices to
[...,...] cannot be used with names of rows or columns.
Werner A. Stahel, ETH Zurich
dd <- data.frame(rbind(a=1:3,b=4:6,c=7:9,d=10:12)) dropdata(dd,"b") dropdata(dd, col="X3") d1 <- dropdata(dd,"d") d2 <- dropdata(d1,"b") naresid(attr(d2,"na.action"),as.matrix(d2)) dropdata(letters, 3:5)dd <- data.frame(rbind(a=1:3,b=4:6,c=7:9,d=10:12)) dropdata(dd,"b") dropdata(dd, col="X3") d1 <- dropdata(dd,"d") d2 <- dropdata(d1,"b") naresid(attr(d2,"na.action"),as.matrix(d2)) dropdata(letters, 3:5)
dropNA returns the vector 'x', without elements that are NA or NaN
or, if 'inf' is TRUE, equal to Inf or -Inf.
replaceNA replaces these values by values from the second argument
dropNA(x, inf = TRUE) replaceNA(x, na, inf = TRUE)dropNA(x, inf = TRUE) replaceNA(x, na, inf = TRUE)
x |
vector from which the non-real values should be dropped or replaced |
na |
replacement or vector from which the replacing values are taken. |
inf |
logical: should 'Inf' and '-Inf' be considered "non-real"? |
For dropNA: Vector containing the 'real' values
of 'x' only
For replaceNA: Vector with 'non-real' values replaced by
the respective elements of na.
The differences to 'na.omit(x)' are: 'Inf' and '-Inf' are also dropped, unless 'inf==FALSE'.\ no attribute 'na.action' is appended.
Werner A. Stahel
dd <- c(1, NA, 0/0, 4, -1/0, 6) dropNA(dd) na.omit(dd) replaceNA(dd, 99) replaceNA(dd, 100+1:6)dd <- c(1, NA, 0/0, 4, -1/0, 6) dropNA(dd) na.omit(dd) replaceNA(dd, 99) replaceNA(dd, 100+1:6)
Determines effects of varying each of the given variables while all
others are held constant. This function is mainly used to produce
plots of residuals versus explanatory variables, also showing
component effects. It can handle a multivariate response fitted by
lm.
fitcomp(object, data = NULL, vars=NULL, transformed=FALSE, se = FALSE, xm = NULL, xfromdata = FALSE, noexpand=NULL, nxcomp = 51)fitcomp(object, data = NULL, vars=NULL, transformed=FALSE, se = FALSE, xm = NULL, xfromdata = FALSE, noexpand=NULL, nxcomp = 51)
object |
a model fit, result of a fitting function |
data |
data frame in which the variables are found.
If not provided, it is obtained from |
vars |
character vector of names of variables for which
components are required. Only variables that appear in |
transformed |
logical: should components be calculated for
transformed explanatory variables? If |
se |
if TRUE, standard errors will be returned |
xm |
named vector of values of the fixed (central) point from
which the individual variables are varied in turn. |
xfromdata |
if TRUE, the components effects will be evaluated for
the data values in |
noexpand |
vector determining which variables should not be “filled in”, probably because they are used like factors. Either a character vector of variable names or a vector of logical or numerical values with names, in which case the names corresponding to positive values will be identified. |
nxcomp |
number of points used for each (quantitative) variable
if |
The component effect is defined as the curve of fitted values
obtained by varying the explanatory variable or term, keeping all the other
variables (terms) at their "central value" xm (the mean of continuous variables
and the mode of factors).
A list consisting of
comp |
component effects. A matrix, unless the response is multivariate, in which case it will be a 3-dimensional array. |
x |
the values of the x variables for which the effects have been calculated |
xm |
the values at which the x variables are held fixed while one of them is varied |
se |
standard errors of the component effects, if required by the
argument |
Werner A. Stahel, ETH Zurich
data(d.blast) t.r <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) t.fc <- fitcomp(t.r,se=TRUE) t.fc$comp[1:10,]data(d.blast) t.r <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) t.fc <- fitcomp(t.r,se=TRUE) t.fc$comp[1:10,]
gendateaxis generates suitable attributes for
plotting a date or time variable.gendate generates a date variable and is an extension of
as.POSIXct.
gendate(date = NULL, year = 2000, month = 1, day = 1, hour = 0, min = 0, sec = 0, data = NULL, format = "y-m-d", origin = NULL) gendateaxis(date = NULL, year = 2000, month = 1, day = 1, hour = 0, min = 0, sec = 0, data = NULL, format = "y-m-d", origin = NULL, ploptions=NULL)gendate(date = NULL, year = 2000, month = 1, day = 1, hour = 0, min = 0, sec = 0, data = NULL, format = "y-m-d", origin = NULL) gendateaxis(date = NULL, year = 2000, month = 1, day = 1, hour = 0, min = 0, sec = 0, data = NULL, format = "y-m-d", origin = NULL, ploptions=NULL)
date |
vector of class |
year, month, day, hour, min, sec
|
numeric vectors giving the
year, month, day of month, hour, minute, second – or the name of such a
variable contained in |
data |
data.frame, where variables can be found |
format |
format for |
origin |
year of origin for dates, defaults to
|
ploptions |
list pl options, generated by |
If hour is fractional, e.g., 6.2, the fraction is respected,
that is, it will be the same as time 06:12.
If min is also given, the fraction of hour is ignored.
Similar for day and min.
If hour is >=24, the day is augmented by
hour%/%24 and the hour is set to hour%%24.
Similar for min and sec.
For gendate, a vector of times in POSIXct format.
For gendateaxis, this is augmented by the attribute
numvalues |
numerical values used for plotting. If years, months or days vary in the data, the units are days. Otherwise, they are hours, minutes, or seconds, depending on the highest category that varies. |
Unless the dates only cover one of the categories (only years differ, or only months, ...), the following plotting attributes are added:
ticksat |
vector where tickmarks are shown.
It contains its own attribute |
ticklabels |
May be years, quarters, month names, days, ... |
ticklabelsat |
vecor of coordinates to place the ticklabels |
label |
equals "", since the time scale makes it clear enough that the axis represents time. |
Werner A. Stahel
## call gendateaxis without 'real' data tt <- gendate(year=rep(2010:2012, each=12), month=rep(1:12, 3)) ta <- gendateaxis(tt) ## ... derived from data data(d.river) d.river$dt <- gendateaxis(date="date", hour="hour", data=d.river) plyx(O2~dt, data=d.river, subset=months(date)!="Sep") plyx(O2~dt, data=d.river[months(d.river$date)!="Sep",]) plyx(O2~dt, data=d.river, subset=1:1000)## call gendateaxis without 'real' data tt <- gendate(year=rep(2010:2012, each=12), month=rep(1:12, 3)) ta <- gendateaxis(tt) ## ... derived from data data(d.river) d.river$dt <- gendateaxis(date="date", hour="hour", data=d.river) plyx(O2~dt, data=d.river, subset=months(date)!="Sep") plyx(O2~dt, data=d.river[months(d.river$date)!="Sep",]) plyx(O2~dt, data=d.river, subset=1:1000)
Generate fits of a smoothing function for multiple y's. Smooths can be calculated within given groups.
gensmooth(x, y, band = FALSE, power = 1, resid = "difference", weight = NULL, plargs=NULL, ploptions=NULL, ...)gensmooth(x, y, band = FALSE, power = 1, resid = "difference", weight = NULL, plargs=NULL, ploptions=NULL, ...)
x |
vector of x values. |
y |
vector or matrix of y values. |
band |
logical: Should a band consisting of low and high smooth
be calculated? It will only be calculated for the first column of |
power |
|
resid |
Which residuals be calculated?
|
weight |
weights of observations, may also be passed by a
variable |
plargs, ploptions
|
result of calling |
... |
Further arguments, passed to the smoothing function. |
This function is useful for generating the smooths enhancing residual plots. It generates a smooth for a single x variable and multiple y's. It is also used to draw smooths from simulated residuals.
NA's in either x or any column of y cause dropping the
observation (equivalent to na.omit).
The smoothing function used to produce the smooth is
smoothRegr, which relies loess, by default.
This may be changed via ploptions(smooth.function = func) where
func is a smoothing function with the same arguments as
smoothRegr.
The result of the smoothing function may carry an attribute
xtrim. This regulates if the fitted values corresponding to
extreme x values will be suppressed when plotting:
The number of extreme x values corresponding to
ploptions("smooth.xtrim") will be multiplied by
this attribute to obtain the number of extreme points suppressed at
each end. If the smoothing function is smoothLm, which fits a
straight line, then trimming is suppressed since this function returns
0 as the xtrim attribute.
If band is TRUE, a vector of "low" and a vector of
"high" smooth values will be calculated for the first column of
y in the following way:
Residuals are calculated as the diference
between the observations and the respective smoothed values hat.$s_i$.
Then a smooth is calculated for the square roots of the positive residuals,
and the squared fitted values are added to the hat.$s_i$.
(The transformation by square roots makes the distribution of the residuals
more symmetric.)
This defines the “high” smooth values.
The construction of the “low” one is analogous.
The resulting values of the two are stored in the list component
yband, and ybandindex contains the information to which
group ("low" or "high") the value belongs.
A list with components:
x |
vector of x values, sorted, within levels of |
y |
matrix with 1 or more columns of corresponding fitted values of the smoothing. |
group |
grouping factor, sorted, if actif. |
index |
vector of indices of the argument |
xorig |
original |
ysmorig |
corresponding fitted values |
residuals |
if required by the argument |
If band==TRUE,
yband |
vector of low and high smoothed values (for the first
column of |
ybandindex |
Indicator if |
This function is called by plyx and
plmatrix when smooth=T is set,
as well as by
plregr applied to model objects.
It is rarely needed to call it directly.
A band is generated only for the first columnn of y since the
others are supposed to be simulated versions of the first one
and do not need a band.
Werner A. Stahel, ETH Zurich
smoothRegr,
plsmooth, plsmoothline
data(d.blast) r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast, na.action=na.exclude) r.smooth <- gensmooth( fitted(r.blast), residuals(r.blast)) showd(r.smooth$y) plot(fitted(r.blast), resid(r.blast), main="Tukey-Anscombe Plot") abline(h=0) lines(r.smooth$x,r.smooth$y, col="red") ## grouped data t.plargs <- list(pdata=data.frame(".smooth.group."=d.blast$location)) r.smx <- gensmooth( d.blast$dist, residuals(r.blast), plargs=t.plargs) plot(d.blast$dist, residuals(r.blast), main="Residuals against Regressor") abline(h=0) plsmoothline(r.smx, d.blast$dist, resid(r.blast), plargs=t.plargs) ## or, without using plsmoothlines: ## for (lg in 1:length(levels(r.smx$group))) { ## li <- as.numeric(r.smx$group)==lg ## lines(r.smx$x[li],r.smx$y[li], col=lg+1, lwd=3) ## }data(d.blast) r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast, na.action=na.exclude) r.smooth <- gensmooth( fitted(r.blast), residuals(r.blast)) showd(r.smooth$y) plot(fitted(r.blast), resid(r.blast), main="Tukey-Anscombe Plot") abline(h=0) lines(r.smooth$x,r.smooth$y, col="red") ## grouped data t.plargs <- list(pdata=data.frame(".smooth.group."=d.blast$location)) r.smx <- gensmooth( d.blast$dist, residuals(r.blast), plargs=t.plargs) plot(d.blast$dist, residuals(r.blast), main="Residuals against Regressor") abline(h=0) plsmoothline(r.smx, d.blast$dist, resid(r.blast), plargs=t.plargs) ## or, without using plsmoothlines: ## for (lg in 1:length(levels(r.smx$group))) { ## li <- as.numeric(r.smx$group)==lg ## lines(r.smx$x[li],r.smx$y[li], col=lg+1, lwd=3) ## }
genvarattributes generates attributes of variables that are
useful for the plgraphics functions.
It is called by pl.control.setvarattributes modifies or sets such attributes.
genvarattributes(data, vnames = NULL, vcol = NULL, vlty = NULL, vpch = NULL, varlabel = NULL, innerrange = NULL, plscale = NULL, zeroline = NULL, replace=FALSE, ploptions = NULL, ...) setvarattributes(data, attributes = NULL, list = NULL, ...)genvarattributes(data, vnames = NULL, vcol = NULL, vlty = NULL, vpch = NULL, varlabel = NULL, innerrange = NULL, plscale = NULL, zeroline = NULL, replace=FALSE, ploptions = NULL, ...) setvarattributes(data, attributes = NULL, list = NULL, ...)
data |
data.frame consisting of the variables (columns) to be characterized by their attributes |
vnames |
names of variables to be treated as y variables |
vcol, vlty, vpch
|
color, line type and plotting character
to be used when multiple y-s are plotted (in the sense of
|
varlabel |
labels of the variables, in the case that the
|
innerrange |
logical indicating whether inner plotting ranges should be determined and/or used. May also be the limits of the inner plotting range, if predetermined, see Details |
plscale |
plot scale: name of the function to be used for
generating a plotting scale, like |
zeroline |
value(s) for which a horizontal or vertical line will be
drawn (in addition to the gridlines). The default is given by
|
ploptions |
list containing the plotting elements needed to set the attributes |
replace |
logical: should existing attributes be replaced? |
attributes |
(for |
list |
a list of attributes to be set.
Each component must have a name giving the name of the variable
attribute to be set, and be itself a list (or a vector).
This list must have names that identify the variables in
|
... |
further arguments, which will be collected and used as or
added to |
If the attribute innerrange is replaced, then plcoord is
also replaced.
innerrange may be a named list of ranges with names
corresponding to variables (not necessarily all of them),
or a scalar vector of length 2 to be used as range for all the
variables.
It can also be a logical vector superseding the argument
innerrange, either named (as just mentioned) or
unnamed, to be repeated the appropriate number of times.
Data.frame, returning the original values, but the variables are
supplemented by the following attributes, where available:
nvalues |
number of distinct values |
innerrange |
inner plotting range |
plcoord |
plotting coordinates |
ticksat |
tick marks for axis |
varlabel |
label to be used as axis label |
zeroline |
value(s) for which a horizontal or vertical line will be drawn (in addition to the gridlines) |
Werner A. Stahel
data(d.blast) dd <- genvarattributes(d.blast) str(attributes(dd$tremor)) ddd <- setvarattributes(dd, list( tremor=list(ticksat=seq(0,24,2), ticklabelsat = seq(0,24,10), ticklabels=c("low","medium","high")) ) ) str(attributes(ddd$tremor)) data(d.river) plyx(O2+H2CO3+T ~ date, data=d.river, subset=as.Date(date)<as.Date("2010-02-28")) dd <- setvarattributes(d.river, list=list(vcol=c(O2="blue", T="red")), vpch=c(O2=1, T="T", H2CO3=5) ) attributes(dd$O2) plyx(O2+H2CO3+T ~ date, data=d.river, subset=as.Date(date)<as.Date("2010-02-28"), plscale = c(O2="log", H2CO3="log") )data(d.blast) dd <- genvarattributes(d.blast) str(attributes(dd$tremor)) ddd <- setvarattributes(dd, list( tremor=list(ticksat=seq(0,24,2), ticklabelsat = seq(0,24,10), ticklabels=c("low","medium","high")) ) ) str(attributes(ddd$tremor)) data(d.river) plyx(O2+H2CO3+T ~ date, data=d.river, subset=as.Date(date)<as.Date("2010-02-28")) dd <- setvarattributes(d.river, list=list(vcol=c(O2="blue", T="red")), vpch=c(O2=1, T="T", H2CO3=5) ) attributes(dd$O2) plyx(O2+H2CO3+T ~ date, data=d.river, subset=as.Date(date)<as.Date("2010-02-28"), plscale = c(O2="log", H2CO3="log") )
identical to getS3method
getmeth(fn, mt)getmeth(fn, mt)
fn |
name of generic function, quoted or unquoted |
mt |
name of method, quoted or unquoted |
Source code of the method
Werner A. Stahel, ETH Zurich
getmeth(simresiduals, glm)getmeth(simresiduals, glm)
getvarnames extracts the variables' names
occurring in a formula, in raw form
(as get\_all\_vars) or in transformed form
(as model.frame does it).
getvariables collects variables from a data.frame
getvariables(formula, data = NULL, transformed = TRUE, envir = parent.frame(), ...) getvarnames(formula, data = NULL, transformed = FALSE)getvariables(formula, data = NULL, transformed = TRUE, envir = parent.frame(), ...) getvarnames(formula, data = NULL, transformed = FALSE)
formula |
a model 'formula' or 'terms' object or an R object, or a character vector of variable names |
data |
a data.frame, list or environment (or object coercible by 'as.data.frame' to a data.frame), containing the variables in 'formula'. Neither a matrix nor an array will be accepted. |
transformed |
logical. If |
envir |
environment in which the |
... |
further arguments such as
|
For getvarnames:
names of all variables (transformed=FALSE)
or simple terms (transformed=TRUE),
including the attributes
xvar |
those from the right hand side of the formula |
yvar |
left hand side, if present |
yvar |
conditioning part, denoted after a |
For getvariables:
data.frame containing the extracted variables or simple terms,
with the attributes of getvarnames
Werner A. Stahel
data(d.blast) getvarnames(log10(tremor)~log10(distance)*log10(charge), data=d.blast) dd <- getvariables(log10(tremor)~log10(distance)*log10(charge), data=d.blast, by=location) str(dd)data(d.blast) getvarnames(log10(tremor)~log10(distance)*log10(charge), data=d.blast) dd <- getvariables(log10(tremor)~log10(distance)*log10(charge), data=d.blast, by=location) str(dd)
Adds a legend to a plot as does legend. This function
just expresses the position relative to the range of the coordinates
legendr(x = 0.05, y = 0.95, legend, ...)legendr(x = 0.05, y = 0.95, legend, ...)
x |
position in horizontal direction, between 0 for left margin and 1 for right margin |
y |
position in vertical direction, between 0 for bottom margin and 1 for top margin |
legend |
text of the legend |
... |
arguments passed to |
See legend
Werner A. Stahel, ETH Zurich
ts.plot(ldeaths, mdeaths, fdeaths,xlab="year", ylab="deaths", lty=c(1:3)) legendr(0.7,0.95, c("total","female","male"), lty=1:3)ts.plot(ldeaths, mdeaths, fdeaths,xlab="year", ylab="deaths", lty=c(1:3)) legendr(0.7,0.95, c("total","female","male"), lty=1:3)
Extracts the leverage component of a fit object using the
na.action component if available
leverage(object)leverage(object)
object |
an object containing a component |
The difference to hatvalues is that leverage does not
call influence and therefore does not require residuals.
It is therefore simpler and more widely applicable.
The function uses the qr decomposition of object.
If necessary, it generate it.
The leverage is the squared Mahalanobis distance of the observation
from the center of the design X (model.matrix) with
"covariance" X^T X. If there are weights (object$weights),
the weighted center and "covariance" are used, and the distances are
multiplied by the weights.
To obtain the distances in the latter case, "de-weight" the leverages
by dividing them by the weights.
The vector fit$leverage, possibly expanded by missing values
if fit$na.action has class na.exclude
Werner A. Stahel, ETH Zurich
data(d.blast) r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) showd(leverage(r.blast))data(d.blast) r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) showd(leverage(r.blast))
extracts the linear.predictor component of a model object, taking 'na.resid' into account, in analogy to 'residuals' or 'fitted.values'
linear.predictors(object)linear.predictors(object)
object |
model fit |
vector (or, for models inheriting from 'multinom', matrix) of linear predictor values
Werner A. Stahel
## example from 'glm' clotting <- data.frame( u = c(5,10,15,20,30,40,60,80,100), lot1 = c(118,58,NA,35,27,25,21,19,18), ## NA inserted instead of 42 lot2 = c(69,35,26,21,18,16,13,12,12)) r.gam <- glm(lot1 ~ log(u), data = clotting, family = Gamma) linear.predictors(r.gam) ## 8 elements; 3rd missing. r.gex <- glm(lot1 ~ log(u), data = clotting, family = Gamma, na.action=na.exclude) linear.predictors(r.gex) ## 9 elements, third is NA## example from 'glm' clotting <- data.frame( u = c(5,10,15,20,30,40,60,80,100), lot1 = c(118,58,NA,35,27,25,21,19,18), ## NA inserted instead of 42 lot2 = c(69,35,26,21,18,16,13,12,12)) r.gam <- glm(lot1 ~ log(u), data = clotting, family = Gamma) linear.predictors(r.gam) ## 8 elements; 3rd missing. r.gex <- glm(lot1 ~ log(u), data = clotting, family = Gamma, na.action=na.exclude) linear.predictors(r.gex) ## 9 elements, third is NA
Transforms the data by a log10 transformation, modifying small and zero observations such that the transformation yields finite values.
logst(data, calib=data, threshold=NULL, mult = 1)logst(data, calib=data, threshold=NULL, mult = 1)
data |
a vector or matrix of data, which is to be transformed |
calib |
a vector or matrix of data used to calibrate the
transformation(s),
i.e., to determine the constant |
threshold |
constant c that determines the transformation, possibly a vector with a value for each variable. |
mult |
a tuning constant affecting the transformation of small values, see Details |
Small values are determined by the threshold c. If not given by the
argument threshold, then it is determined by the quartiles
and of the non-zero data as those
smaller than .
The rationale is that for lognormal data, this constant identifies
2 percent of the data as small.
Beyond this limit, the transformation continues linear with the
derivative of the log curve at this point. See code for the formula.
The function chooses log10 rather than natural logs because they can be backtransformed relatively easily in the mind.
the transformed data. The value c needed for the transformation is
returned as attr(.,"threshold").
The names of the function alludes to Tudey's idea of "started logs".
Werner A. Stahel, ETH Zurich
dd <- c(seq(0,1,0.1),5*10^rnorm(100,0,0.2)) dd <- sort(dd) r.dl <- logst(dd) plot(dd, r.dl, type="l") abline(v=attr(r.dl,"threshold"),lty=2)dd <- c(seq(0,1,0.1),5*10^rnorm(100,0,0.2)) dd <- sort(dd) r.dl <- logst(dd) plot(dd, r.dl, type="l") abline(v=attr(r.dl,"threshold"),lty=2)
Adjusts the proportion of extreme points to be labeled to the number of
observations. It is the default of the ploption markextremes.
markextremes(n)markextremes(n)
n |
number of observations |
The function simply applies ceiling(sqrt(n)/2)/n.
A scalar between 0 and 0.5
Werner A. Stahel
markextremes(20) for (n in c(10,20,50,100,1000)) print(c(n,markextremes(n)))markextremes(20) for (n in c(10,20,50,100,1000)) print(c(n,markextremes(n)))
Makes it easy to modify one or a few elements of a vector or list of default settings. This function is to be used within functions that contain vectors of control arguments such as colors for different elements of a plot
modarg(arg = NULL, default)modarg(arg = NULL, default)
arg |
named vector or list of the elements that should override the settings in 'default' |
default |
named vector or list of default settings |
Same as the argument 'default' with elements replaced according to
'arg'.
See the source code of plmboxes.default for a typical application.
Werner A. Stahel
modarg(c(b="B", c=0), list(a=4, b="bb", c=NA)) df <- ploptions("linewidth") cbind(df, modarg(c(dot=1.4, dashLongDot=1.3), df)) ## These statements lead to a warning: modarg(c(b=2, d=6), c(a="4", b="bb", c=NA)) modarg(1:6, c(a="4", b="bb", c=NA))modarg(c(b="B", c=0), list(a=4, b="bb", c=NA)) df <- ploptions("linewidth") cbind(df, modarg(c(dot=1.4, dashLongDot=1.3), df)) ## These statements lead to a warning: modarg(c(b=2, d=6), c(a="4", b="bb", c=NA)) modarg(1:6, c(a="4", b="bb", c=NA))
Vectors of month and weekday names
c.months c.mon c.weekdays c.wkdc.months c.mon c.weekdays c.wkd
none
character vector.c.months contains the 12 month names.c.mon same, abbreviated to 3 characters,c.weekdays names of the 7 weekdaysc.wkd same, abbreviated to 3 characters,
Werner A. Stahel, ETH Zurich
c.weekdays[1:5]c.weekdays[1:5]
Drops the rows of a data frame that contain an NA, an NaN, or an Inf value
nainf.exclude(object, ...)nainf.exclude(object, ...)
object |
an R object, typically a data frame |
... |
further arguments special methods could require. |
This is a simple modification of na.omit and
na.exclude
The value is of the same type as the argument object,
with possibly less elements.
Werner A. Stahel, ETH Zurich
t.d <- data.frame(V1=c(1,2,NA,4), V2=c(11,12,13,Inf)) nainf.exclude(t.d)t.d <- data.frame(V1=c(1,2,NA,4), V2=c(11,12,13,Inf)) nainf.exclude(t.d)
Generate a notice to be sent to output
notice(..., printnotices = NULL)notice(..., printnotices = NULL)
... |
contents of the notice, will be pasted together |
printnotices |
logical: Should the notice be printed? Default is the respective pl option. |
This function is very similar to 'message'
None.
Werner A. Stahel
ff <- function(x) { if (length(x)==0) { notice("ff: argument 'x' is NULL. I return 0") return(0) } 1/x } ff(3) ff(NULL) oo <- ploptions(printnotices=FALSE) ff(NULL)ff <- function(x) { if (length(x)==0) { notice("ff: argument 'x' is NULL. I return 0") return(0) } 1/x } ff(3) ff(NULL) oo <- ploptions(printnotices=FALSE) ff(NULL)
Arguments that can be specified calling plyx and
other 'pl' functions are checked and data is prepared for plotting.
pl.control(x=NULL, y=NULL, condvar = NULL, data = NULL, subset = NULL, transformed = TRUE, distinguishy = TRUE, gensequence = NULL, csize = NULL, csize.pch = NULL, psize = NULL, plab = FALSE, pch = NULL, pcol = NULL, smooth.weights = NULL, smooth.weight = NULL, markextremes = NULL, smooth = NULL, xlab = NULL, ylab = NULL, varlabel = NULL, vcol = NULL, vlty = NULL, vpch = NULL, plscale = NULL, log = NULL, main = NULL, sub = NULL, .subdefault = NULL, mar = NULL, gencoord = TRUE, plargs = pl.envir, ploptions = NULL, .environment. = parent.frame(), assign = TRUE, ... )pl.control(x=NULL, y=NULL, condvar = NULL, data = NULL, subset = NULL, transformed = TRUE, distinguishy = TRUE, gensequence = NULL, csize = NULL, csize.pch = NULL, psize = NULL, plab = FALSE, pch = NULL, pcol = NULL, smooth.weights = NULL, smooth.weight = NULL, markextremes = NULL, smooth = NULL, xlab = NULL, ylab = NULL, varlabel = NULL, vcol = NULL, vlty = NULL, vpch = NULL, plscale = NULL, log = NULL, main = NULL, sub = NULL, .subdefault = NULL, mar = NULL, gencoord = TRUE, plargs = pl.envir, ploptions = NULL, .environment. = parent.frame(), assign = TRUE, ... )
x, y, data
|
as in |
condvar |
conditioning variables for |
subset |
subset of data.frame 'data' to be used for plotting. See details. |
transformed |
logical: should transformed variables be used? |
distinguishy |
logical: should multiple y's be distinguished?
This is |
gensequence |
logical: if only |
csize |
character expansion, applied to both labels and plotting characters. |
csize.pch |
expansion of plotting symbol relative to
|
psize, plab, pch, pcol
|
Plotting characteristics of points,
specified as a (unquoted) variable name found in |
smooth.weights, smooth.weight
|
weights to be used in calculating smooth lines. Both are equivalent. |
markextremes |
scalar: proportion of extreme points to be labelled |
smooth |
logical: should a smooth line be added? |
xlab, ylab
|
axis labels |
varlabel |
labels for variables replacing their names in the |
vcol, vlty, vpch
|
color, line type and plotting character
to be used when multiple y-s are plotted (in the sense of
|
plscale |
plot scale: name of the function to be used for
generating a plotting scale, like |
log |
requires log scale as in R's basic plot function,
e.g., equals either |
main, sub
|
string. Main title of the plot(s).
If |
.subdefault |
for internal use: default of subtitle |
mar |
plot margins |
gencoord |
logical: should plotting coordinates be generated? This is avoided for low level pl graphics. |
plargs |
pl arguments, a list with components
|
ploptions |
Plotting attributes, e.g., plotting character,
line types, colors and the like, for different aspects of plots.
Result of |
.environment. |
used by the calling function to provide the
environment for evaluating |
assign |
logical: should the result of |
... |
further arguments. These may include:
|
The function selects the data according to the arguments
x, y, data and subset (the latter by calling
plsubset).
The argument subset should be used instead of
data[subset,] if the dataset data contains variable
attributes like varlabel, ticksat, ....
The argument is evaluated in the dataset defined by data,
i.e., variable names may be used to define the subset.
A list containing all the arguments, possibly in modified form.
Specifically, the evaluations of the variables contained in
x and y along with
psize, plab, pch, pcol, smoothGroup, smoothWeights
are collected in the component pldata.
The component, ploptions, collects the ploptions, and
plfeatures contains a list of additional features, both
to be used in the calling high level pl function
Werner A. Stahel
plyx(Sepal.Width~Sepal.Length, data=iris, axp=7, plab=TRUE, csize.plab=0.6) ## same as plargs <- pl.control(Sepal.Width~Sepal.Length, data=iris) plargs$pdata$plab <- row.names(iris) plargs$csize.lab <- 0.6 plargs$axp <- 7 plyx(Sepal.Width~Sepal.Length, plargs=plargs)plyx(Sepal.Width~Sepal.Length, data=iris, axp=7, plab=TRUE, csize.plab=0.6) ## same as plargs <- pl.control(Sepal.Width~Sepal.Length, data=iris) plargs$pdata$plab <- row.names(iris) plargs$csize.lab <- 0.6 plargs$axp <- 7 plyx(Sepal.Width~Sepal.Length, plargs=plargs)
Adds horizontal or vertical bars to a plot
plbars(x = NULL, y = NULL, midpointwidth = NULL, plargs = NULL, ploptions = NULL, marpar = NULL, ...)plbars(x = NULL, y = NULL, midpointwidth = NULL, plargs = NULL, ploptions = NULL, marpar = NULL, ...)
x, y
|
coordinates for the horizontal and veritical axis,
respectively. Either of them must have 3 columns.
If |
midpointwidth |
for |
plargs, ploptions
|
result of |
marpar |
margin parameters, if already available.
By default, they will be retieved from |
... |
absorbs extra arguments |
For plbars, the argument midpointwidth determines the
length of the segments that mark the midpoint relative to the default,
which is proportional to the range of the plotting area and inversely
proportional to the number of (finite) observations.
plargs and ploptions may be specified explicitly.
Otherwise, they are taken from pl.envir.
None.
Werner A. Stahel
data(d.river) dd <- plsubset(d.river, 1:2000) da <- aggregate(dd[,3:7], dd[,"date",drop=FALSE], mean, na.rm=TRUE) ds <- aggregate(dd[,3:7], dd[,"date",drop=FALSE], sd, na.rm=TRUE) plyx(O2~date, data=da, type="n") td <- da$O2 + outer(ds$O2, c(0,-1,1)) plbars(y = td, midpointwidth=0.1, bar.lwd=2)data(d.river) dd <- plsubset(d.river, 1:2000) da <- aggregate(dd[,3:7], dd[,"date",drop=FALSE], mean, na.rm=TRUE) ds <- aggregate(dd[,3:7], dd[,"date",drop=FALSE], sd, na.rm=TRUE) plyx(O2~date, data=da, type="n") td <- da$O2 + outer(ds$O2, c(0,-1,1)) plbars(y = td, midpointwidth=0.1, bar.lwd=2)
A scatterplot matrix is generated that shows, in each
panel, the relationship between two primary variables, with the
dataset restricted by appropriate subranges of two 'conditioning'
variables.
This corresponds to link{coplot}.
The points that are near to the the 'window' defining the panel's
restriction are also shown, in a distinct style.
plcond(x, y = NULL, condvar = NULL, data = NULL, panel = NULL, nrow = NULL, ncol = NULL, xaxmar = NULL, yaxmar = NULL, xlab = NULL, ylab = NULL, oma = NULL, plargs = NULL, ploptions = NULL, assign = TRUE, ...)plcond(x, y = NULL, condvar = NULL, data = NULL, panel = NULL, nrow = NULL, ncol = NULL, xaxmar = NULL, yaxmar = NULL, xlab = NULL, ylab = NULL, oma = NULL, plargs = NULL, ploptions = NULL, assign = TRUE, ...)
x, y
|
the two variables used to generate each panel.
They may be specified as vectors, as column names of |
condvar |
two (or one) variables that define the restrictions of the data for the different panels. A numerical variable is cut into intervals, see Details. A factor defines the 'ranges' as its levels. For each combination of intervals or levels of the two variables, a panel is generated. |
data |
data.frame in which the variables are found if needed |
panel |
function that generates each panel.
If set by the user, it must accept the arguments
|
nrow, ncol
|
number of maximum rows and columns on a page |
xaxmar, yaxmar
|
margin in which the axis (tick marks and
corresponding labels) should be shown: either 1 or 3 for
|
xlab, ylab
|
labels of the variables |
oma |
width of outer margins, see |
plargs |
result of calling |
ploptions |
list of pl options. |
assign |
logical: Should the plargs be stored in |
... |
further arguments passed to the |
A numerical conditioning variable (condvar) will be
split by default into classes by splitting its robust range
(robrange) into ploptions("plcond.nintervals")
equally long intervals. Alternatively, the variable may contain
an attribute cutpoints which then defines the intervals.
For numerical conditioning variables, each panel also shows
neighboring points with a different color and diminished size.
The size of the neighborhood is defined by the proportion of extension
ploptions("plcond.ext").
The point size of the respective 'exterior' points is given by
ploptions("plcond.cex") The color are given by
the 4 elements of ploptions("plcond.col"):
The first element is used to paint the neighboring points
to the left of the current range of the conditioning x variable,
the second element paints those to the right,
and the third and fourth are used in the same way for the
conditioning y variable. The neighboring points that are outside
both ranges get a color mixing the two applicable colors according to
this rule.
Finally, paling is applied to these colors with a degree that is
linear in the distance from the interval, determined by the range
given by ploptions("plcond.pale").
None.
Werner A. Stahel
plcond(Sepal.Width~Sepal.Length, data=iris, condvar=~Species+Petal.Length)plcond(Sepal.Width~Sepal.Length, data=iris, condvar=~Species+Petal.Length)
For plots with an "inner plot range" (see Details) this function converts the data values to the coordinates in the plot
plcoord(x, range = NULL, innerrange.factor = NULL, innerrange.ext = NULL, plext = NULL, ploptions = NULL)plcoord(x, range = NULL, innerrange.factor = NULL, innerrange.ext = NULL, plext = NULL, ploptions = NULL)
x |
data to be represented |
range |
vector of 2 elements giving the inner plot range. Data
beyond the given interval will be non-linearly transformed to fit
within the (outer) plot margins. Defaults to
|
innerrange.factor |
factor used to determine the default of
|
innerrange.ext |
factor for extending the |
plext |
vector of 1 or 2 elements setting the extension factor for the plotting range |
ploptions |
plotting options |
When plotting data that contain outliers, the non-outlying data is represented poorly. Rather than simply clipping outliers, one can split the plotting area into an inner region, where the (non-outlying) data is plotted as usual, and a plot area margin, in which outliers are represented on a highly non-linear scale that allows to display them all.
This function converts the data to the coordinates used in the graphical display, and also returns the inner and outer ranges for plotting.
vector of coordinates used for plotting, that is, unchanged x
values for those within the range and transformed values
for those outside.
Attributes:
attr(, "plrange")
|
the range to be used when plotting |
attr(, "range")
|
the "inner" plot range, either the argument
|
attr(, "nouter")
|
the number of modified observations |
Werner A. Stahel
set.seed(0) x <- c(rnorm(20),rnorm(3,5,10)) ( xmod <- plcoord(x) ) plot(x,xmod) ## This shows what high level pl functions do by default plot(xmod) abline(h=attr(xmod,"innerrange"),lty=3, lwd=2) ## plgraphics plyx(x)set.seed(0) x <- c(rnorm(20),rnorm(3,5,10)) ( xmod <- plcoord(x) ) plot(x,xmod) ## This shows what high level pl functions do by default plot(xmod) abline(h=attr(xmod,"innerrange"),lty=3, lwd=2) ## plgraphics plyx(x)
These functions set up the frame of a plot based on the 'pl' paradigm
plframe(x = NULL, y = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, mar = NULL, showlabels = TRUE, plext = NULL, axcol = rep(1, 4), plargs = NULL, ploptions = NULL, marpar = NULL, xy = NULL, ...) pltitle(main=NULL, sub=NULL, csize=NULL, csizemin=NULL, side=3, line=NULL, adj=NULL, outer.margin=NULL, col="black", doc=NULL, show=NA, plargs=NULL, ploptions = NULL, marpar = NULL, ...) plaxis(side, x=NULL, showlabels=TRUE, range=NULL, varlabel=NULL, col=1, tickintervals=NULL, plargs = NULL, ploptions = NULL, marpar = NULL, ...)plframe(x = NULL, y = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, mar = NULL, showlabels = TRUE, plext = NULL, axcol = rep(1, 4), plargs = NULL, ploptions = NULL, marpar = NULL, xy = NULL, ...) pltitle(main=NULL, sub=NULL, csize=NULL, csizemin=NULL, side=3, line=NULL, adj=NULL, outer.margin=NULL, col="black", doc=NULL, show=NA, plargs=NULL, ploptions = NULL, marpar = NULL, ...) plaxis(side, x=NULL, showlabels=TRUE, range=NULL, varlabel=NULL, col=1, tickintervals=NULL, plargs = NULL, ploptions = NULL, marpar = NULL, ...)
x |
coordinates for the horizontal axis |
y |
coordinates for the vertical axis |
xlab, ylab
|
axis labels |
xlim, ylim
|
plot ranges |
mar |
plot margins |
showlabels |
logical: should labels for tickmarks and the
variable label be displayed?
If |
plext |
extension of the plotting area beyond the range of the data. |
axcol |
colors for drawing axes scales |
main, sub
|
main title and subtitle |
varlabel |
variable name |
side |
For |
csize |
character size. May be vector of length 3, giving size for
main title, subtitle, and |
csizemin |
minimal character size, to be used to adjust the
character size to the length of the text (if |
line |
line in margin on which the main title is placed – or the
subtitle if |
adj |
text adjustment, scalar between 0 and 1 |
outer.margin |
logical: should title text be placed in outer margin? |
col |
color for the title text or axis line and tickmarks |
range |
range in which tickmarks are set |
doc |
logical: should the |
show |
logical: if |
tickintervals |
number of intervals used by
|
plargs, ploptions
|
result of |
marpar |
margin parameters, if already available.
By default, they will be retieved from |
xy |
logical: should the coordinates be obtained as in
high level graphics? This is set to |
... |
absorbs extra arguments |
If the arguments x and y are not given,
they are obtained from pl.envir$pldata.
plframe draws axes according to argument axes,
by calling plaxis.
It looks for attributes of x and y, such as
innerrange and ticksat.
Tick labels are shown at the values of the ticklabelsat
attribute if available, otherwise at the values of ticksat.
The labels can be given by the attribute ticklabels.
This facilitates setting more tick marks than labels, see the
example.
It also draws a grid.
The positions of gridlines at ticksat by default.
Finally, it draws "zero" lines as determined by the pl option
zeroline. The latter can be a numeric vector giving
the positions of such threshold lines, or a list of two such vectors,
the first for horizontal axis, the second for the vertical axis.
plaxis only shows the variable label, tick labels and tickmarks
if there is enough space or showlabels > 1.
If it is called when there are multiple panels, this is decided
according to the actual mar setting if it is an inner panel;
if it is a panel adjacent to an outer margin, then the oma
setting is also used.
plargs and ploptions may be specified explicitly,
but they are usually generated by calling pl.control.
plframe and plaxis invisibly return the former
par(c("cex", "mar", "mgp"))
if setpar is TRUE, otherwise NULL.
pltitle invisibly return a list consisting of the main
and sub title.
Werner A. Stahel
plyx(Sepal.Width ~ Sepal.Length, data=iris) ## again, each step separately t.dt <- pl.envir$pldata oldpar <- plframe() ## or plframe(t.dt$Sepal.Length, t.dt$Sepal.Width, plargs=pl.envir) plsmooth() ## or plsmooth(t.dt$Sepal.Length, t.dt$Sepal.Width, plargs=pl.envir) t.plab <- plmark(markextremes=0.03) ## or plmark(t.dt$Sepal.Length, t.dt$Sepal.Width, markextremes=0.03, plargs=pl.envir) plpoints(plab=t.plab) ## or plpoints(t.dt$Sepal.Length, t.dt$Sepal.Width, ## plargs=pl.envir, plab=t.plab) plaxis(4) par(oldpar) ## reset the changed graphical parametersplyx(Sepal.Width ~ Sepal.Length, data=iris) ## again, each step separately t.dt <- pl.envir$pldata oldpar <- plframe() ## or plframe(t.dt$Sepal.Length, t.dt$Sepal.Width, plargs=pl.envir) plsmooth() ## or plsmooth(t.dt$Sepal.Length, t.dt$Sepal.Width, plargs=pl.envir) t.plab <- plmark(markextremes=0.03) ## or plmark(t.dt$Sepal.Length, t.dt$Sepal.Width, markextremes=0.03, plargs=pl.envir) plpoints(plab=t.plab) ## or plpoints(t.dt$Sepal.Length, t.dt$Sepal.Width, ## plargs=pl.envir, plab=t.plab) plaxis(4) par(oldpar) ## reset the changed graphical parameters
Calculates inner limits for plotting, based on a robust estimate of the range.
plinnerrange(innerrange, data, factor = 4, FUNC = robrange)plinnerrange(innerrange, data, factor = 4, FUNC = robrange)
innerrange |
logical: Should range be calculated?
If |
data |
vector or data.frame for which the range(s) will be calculated |
factor |
expansion of the calculated robust range to yield the plotting range |
FUNC |
function used to calculate the robust range.
The |
Matrix of 2 rows giving the ranges to be used as
inner plotting ranges for the variables.
If innerrange is such a matrix or data.frame, it will be returned
as is.
Werner A. Stahel
data(d.blast) dd <- d.blast[,c("charge","distance","tremor")] ( t.ipl <- plinnerrange(TRUE, dd) ) plot(dd[,"tremor"], plcoord(dd[,"tremor"], t.ipl[,"tremor"])) abline(h=t.ipl[,"tremor"])data(d.blast) dd <- d.blast[,c("charge","distance","tremor")] ( t.ipl <- plinnerrange(TRUE, dd) ) plot(dd[,"tremor"], plcoord(dd[,"tremor"], t.ipl[,"tremor"])) abline(h=t.ipl[,"tremor"])
The inner plotting range is the range in which plotting functions of the regr0 package show unmodified coordinates. This function determines the range for one or more variables.
pllimits(pllim, data, limfac = NULL, FUNC=NULL)pllimits(pllim, data, limfac = NULL, FUNC=NULL)
pllim |
either a logical: shall an inner plotting range be
determined? – or a matrix with 2 rows and |
data |
vector or matrix or data.frame of data for which the inner plotting range is to be determined |
limfac |
scalar factor by which the range determined by
|
FUNC |
function that determines the range of the data |
A matrix with 2 rows containing the minimum and the maximum
of the inner plotting range. The columns correspond to those in
data.
Werner A. Stahel
set.seed(0) xx <- rt(50, df=3) ( pll <- pllimits(TRUE, xx) ) sum(xx<pll[1,] | xx>pll[2,]) ## 3set.seed(0) xx <- rt(50, df=3) ( pll <- pllimits(TRUE, xx) ) sum(xx<pll[1,] | xx>pll[2,]) ## 3
plmarginpar calls par to set the margin widths
mar and mgp equal to those used in the last call of a
high level pl function
plmarginpar(plargs = pl.envir, csize = NULL)plmarginpar(plargs = pl.envir, csize = NULL)
plargs |
list from which the margin parameters are obtained.
If |
csize |
size of plot symbols and text, changes |
The old settings of par(c("mar","mgp")) are returned
invisibly.
plmarginpar is used to complement a plot with
low level ordinary R functions like mtext or
segments, see Example.
The same effect can be achieved by setting the pl option
keeppar to TRUE, either by calling ploptions
or by setting keeppar=TRUE in the call to the high level
pl function.
Werner A. Stahel
par(mar=c(2,2,5,2)) plyx(Sepal.Width~Sepal.Length, data=iris) ## margins according to ploptions par("mar") ## paramteres have been recovered mtext("wrong place for text",3,1, col="red") ## margins not appropriate for active plot plmarginpar() par("mar") ## margins used inside the call to plyx . These are now active mtext("here is the right place",3,1, col="blue")par(mar=c(2,2,5,2)) plyx(Sepal.Width~Sepal.Length, data=iris) ## margins according to ploptions par("mar") ## paramteres have been recovered mtext("wrong place for text",3,1, col="red") ## margins not appropriate for active plot plmarginpar() par("mar") ## margins used inside the call to plyx . These are now active mtext("here is the right place",3,1, col="blue")
Determine extreme points and get labels for them.
plmark(x, y = NULL, markextremes = NULL, plabel = NULL, plargs = NULL, ploptions = NULL)plmark(x, y = NULL, markextremes = NULL, plabel = NULL, plargs = NULL, ploptions = NULL)
x, y
|
coordinates of points. If |
markextremes |
proportion of extreme points to be 'marked'.
This may be a list of proportions with names
indicating the variables for which the proportion is to be applied.
If a vector (of length 2), the elements define the proportions
for the lower and upper end, respectively.
In the default case ( |
plabel |
character vector of labels to be used for extreme
points. If |
plargs, ploptions
|
result of |
A character vector in which the 'marked' observations contain
the respective label and the others equal "".
Werner A. Stahel
plyx(Sepal.Width ~ Sepal.Length, data=iris) ( t.plab <- plmark(iris$Sepal.Length, iris$Sepal.Width, markextremes=0.03) )plyx(Sepal.Width ~ Sepal.Length, data=iris) ( t.plab <- plmark(iris$Sepal.Length, iris$Sepal.Width, markextremes=0.03) )
Plots a scatterplot matrix, for which the variables shown horizontally do not necessarily coincide with those shown vertically. If desired, the matrix is divided into several blocks such that it fills more than 1 plot page.
plmatrix(x, y = NULL, data = NULL, panel = NULL, nrow = NULL, ncol = nrow, reduce = TRUE, xaxmar=NULL, yaxmar=NULL, xlabmar=NULL, ylabmar=NULL, xlab=NULL, ylab=NULL, mar=NULL, oma=NULL, diaglabel.csize = NULL, plargs = NULL, ploptions = NULL, assign = TRUE, ...)plmatrix(x, y = NULL, data = NULL, panel = NULL, nrow = NULL, ncol = nrow, reduce = TRUE, xaxmar=NULL, yaxmar=NULL, xlabmar=NULL, ylabmar=NULL, xlab=NULL, ylab=NULL, mar=NULL, oma=NULL, diaglabel.csize = NULL, plargs = NULL, ploptions = NULL, assign = TRUE, ...)
x |
data for columns (x axis), or formula defining column variables. If it is a formula containing a left hand side, the left side variables will be used last. |
y |
data or formula for rows (y axis). Defaults to |
data |
data.frame containing the variables in case |
panel |
a function that generates the marks of the individual panels, see Details. |
nrow, ncol
|
maximum number of rows and columns of panels on a page |
reduce |
if y is not provided and |
xaxmar, yaxmar
|
margin in which the axis (tick marks and
corresponding labels) should be shown: either 1 or 3 for
|
xlabmar, ylabmar
|
in which margin should the x- [y-] axis be labelled? |
xlab, ylab
|
not used (introduced to avoid confusion with
|
mar, oma
|
width of margins, see |
diaglabel.csize |
Character expansion for labels appearing in the "diagonal" of the scatterplot matrix (if present) |
plargs |
result of calling |
ploptions |
list of pl options. |
assign |
logical: Should the plargs be stored in the |
... |
further arguments passed to the |
The panel function can be user written. It needs
arguments which must correspond to the arguments of
plpanel: x, y, indx, indy, plargs.
If some arguments are not used, just introduce them as arguments
to the function anyway in order to avoid (unnecessary) error messages
and stops.
Since large scatterplot matrices lead to tiny panels, plmatrix
splits the matrix into blocks of at most nrow rows and
ncol columns. If these numbers are missing, they default to
nrow=5 and ncol=6 for landscape pages, and to
nrow=8 and ncol=5 for portrait pages.
The panel argument defaults to plpanel, which results
essentially in points or text
depending on the argument pch, including a smooth line,
to plmboxes if 'x' is a factor and 'y' is not or
vice versa,
or to a modification of sunflowers if both are factors.
The function must have the arguments x and y
to take the coordinates of the points and may have the arguments
indx and indy to transfer the variables\' index.
If there is an argument plargs, the current value of
plargs will be passed on. It is a list and can be extended
to pass any additional items to the function.
none
There are many more arguments, obtained from pl.control,
see ?pl.control. These can be passed to plmatrix
by an argument plargs that is hidden in the ... argument list.
Werner A. Stahel, ETH Zurich
plmatrix(iris, pch=as.numeric(Species)) plmatrix(~Sepal.Length+Sepal.Width, ~Petal.Length+Petal.Width, data=iris, smooth=TRUE, plab=substr(Species,1,2))plmatrix(iris, pch=as.numeric(Species)) plmatrix(~Sepal.Length+Sepal.Width, ~Petal.Length+Petal.Width, data=iris, smooth=TRUE, plab=substr(Species,1,2))
Draw multibox plot(s) for given (grouped) values, possibly asymmetric. 'plbox' draws a single multibox plot (low level graphical function). 'plboxes' is a high level graphics function that draws multiboxes for grouped data. A secondary, binary grouping factor can be given to produce asymmetric multiboxes.
plmboxes(x, ...) ## S3 method for class 'formula' plmboxes(x, y=NULL, data, ...) ## Default S3 method: plmboxes(x=NULL, y=NULL, data=NULL, width=1, at=NULL, horizontal=FALSE, probs=NULL, outliers=TRUE, na=FALSE, backback=NULL, refline=NULL, add=FALSE, xlim=NULL, ylim=NULL, axes=TRUE, xlab=NULL, ylab=NULL, labelsperp=FALSE, xmar=NULL, mar=NULL, widthfac=NULL, minheight=NULL, colors=NULL, lwd=NULL, .subdefault=NULL, plargs = NULL, ploptions = NULL, marpar = NULL, ...) plmbox(x, at=0, probs=NULL, outliers=TRUE, na.pos=NULL, horizontal=FALSE, width=1, wfac=NULL, minheight=NULL, adj=0.5, extquant=FALSE, widthfac=c(max=2, med=1.3, medmin=0.3, outl=NA), colors=c(box="lightblue2",med="blue",na="gray90"), lwd=c(med=3, range=2), warn=options("warn"))plmboxes(x, ...) ## S3 method for class 'formula' plmboxes(x, y=NULL, data, ...) ## Default S3 method: plmboxes(x=NULL, y=NULL, data=NULL, width=1, at=NULL, horizontal=FALSE, probs=NULL, outliers=TRUE, na=FALSE, backback=NULL, refline=NULL, add=FALSE, xlim=NULL, ylim=NULL, axes=TRUE, xlab=NULL, ylab=NULL, labelsperp=FALSE, xmar=NULL, mar=NULL, widthfac=NULL, minheight=NULL, colors=NULL, lwd=NULL, .subdefault=NULL, plargs = NULL, ploptions = NULL, marpar = NULL, ...) plmbox(x, at=0, probs=NULL, outliers=TRUE, na.pos=NULL, horizontal=FALSE, width=1, wfac=NULL, minheight=NULL, adj=0.5, extquant=FALSE, widthfac=c(max=2, med=1.3, medmin=0.3, outl=NA), colors=c(box="lightblue2",med="blue",na="gray90"), lwd=c(med=3, range=2), warn=options("warn"))
x |
For 'plmboxes.formula': a formula, such as 'y ~ grp' or 'y~grp+grp2', where 'y' is a numeric vector of data values to be split into groups according to the grouping variable 'grp' (usually a factor) and, if given, according to the binary variable 'grp2'. 'y~1+grp2' produces a single asymmetric mbox. For 'plmboxes.default': factor to be used as the grouping variable or matrix or data.frame with 2 columns (for asymmetric mbox plot), where the second column is binary. |
y |
a numeric vector of data values |
data |
a data.frame from which the variables in 'formula' should be taken. |
width |
a vector giving the widths of the multibox plot for each group 'grp1'. |
at |
horizontal position of the multiboxes. Must have length equal to the number of (present) levels of the factor 'grp'. if an element of 'at' is 'NA', the group will be skipped. Defaults to 1, 2, ... |
horizontal |
logical. If TRUE, boxes will be drawn horizontally. Note that 'y' is then the horizontal coordinate, i.e., still the quantitative variable defining the boxes, and 'x' is still the grouping. |
probs |
probability values for selecting the quantiles. If all 'probs' are <=0.5, they will be mirrored at 0.5 for 'plmboxes'. The default is c(0.05,0.25,0.5) if the average number of data per group (for 'plmboxes', or the number of data, for 'plmbox') is less than 20, c(0.025,0.05,0.125,0.25,0.375,0.5), otherwise. |
outliers |
logical: should outliers be marked? |
na, na.pos
|
if 'na' is not NULL, NA values will be represented by a box. If 'na' is TRUE, the position of the box will be generated to be below the minimum of the data. If 'na' (for 'plmboxes') or 'na.pos' (for 'plmbox') is a scalar or a vector of length 2, the position of the box is at that value (with a generated width) or between the 2 values, respectively. |
backback |
logical: Should two back-to-back multiboxes be displayed if the (single) x factor is binary? |
refline |
vertical positions of any horizontal reference lines |
add |
logical. If TRUE, the mboxes will be added to an existing plot without calling 'plot'. |
xlim |
plotting limits for the horizontal axis. |
ylim |
plotting limits for the vertical axis. |
axes |
logical. If FALSE, no axes are drawn. |
xlab |
label for the x axis. Defaults to the "x factor" – the first name on the right hand side of 'formula' |
ylab |
label for the x axis. Defaults to the left hand side of 'formula'. |
labelsperp |
logical: Should the labels for the levels of the "x factor" be shown in perpendicular to the axis? If it is numeric, it determines the maximum label length, with a maximum of 20. |
xmar |
plot margin for the "x factor" axis.
Default tries to be suitable, i.e. expand the margin if
|
mar |
margin widths |
widthfac |
named vector used to modify the following settings:
|
colors |
named vector or list selecting the colors to be used, with named elements: box="lightblue2": color with which the central box(es) (those corresponding to probabilieties between 0.25 and 0.75) will be filled. med="blue": color of the mark for the median. na="grey90": color with which the box for NA values will be filled. For 'plmboxes', the argument needs to contain only the elements that should be different from the default values. |
lwd |
named vector or list selecting the line width to be used, with named elements: med=3: line width for the mark showing the median range=2: line width for the line along the range of the data For 'plmboxes', the argument needs to contain only the elements that should be different from the default values. |
plargs |
result of calling |
ploptions |
list of pl options. |
marpar |
margin parameters, if already available.
By default, they will be retieved from |
... |
additional arguments passed to 'plot' |
.subdefault |
text for the subtitle in case that it is not specified |
Specific arguments for 'plmbox':
wfac |
factor by which the widths of the boxes must be multiplied. If given, it overrides 'width' |
minheight |
minimal class width ("height") for the boxes (in case two quantiled are [almost] identical). The default is 0.02 times the (median of the within group) IQR. |
adj |
adjustment of the boxes. 'adj=0' leads to boxes aligned on the left, 'adj=1', on the right, 'adj=0.5', centered. Other values of 'adj' make little sense. |
extquant |
logical, passed to |
warn |
level of warning for the case when there is no non-missing data |
A multibox plot is a generalization (and modification) of the ordinary box plot that draws more details of the distribution in the form of a histogram with variable class widths. The classes are selected such that preselected quantiles form the class breaks. By default, these quantiled include the median and the quartiles, thereby recovering the box of the traditional box plot.
plmboxes invisibly returns the 'at' values that are finally
used.\
plmbox returns a scalar by which the width of the boxes are
multiplied for plotting,
and, as attributes, the quantiles and widths used to draw the boxes
Werner A. Stahel
plmboxes(Sepal.Length~Species, data=iris) plmboxes(Sepal.Length~Species, data=iris, widthfac=c(med=2), colors=c(med="red"), horizontal=TRUE) plmboxes(Sepal.Length~factor(Species)+I(Sepal.Width<=3), data=iris[1:100,], labelsperp=TRUE, horizontal=TRUE)plmboxes(Sepal.Length~Species, data=iris) plmboxes(Sepal.Length~Species, data=iris, widthfac=c(med=2), colors=c(med="red"), horizontal=TRUE) plmboxes(Sepal.Length~factor(Species)+I(Sepal.Width<=3), data=iris[1:100,], labelsperp=TRUE, horizontal=TRUE)
This is a short-cut to set some graphical parameters
plmframes(mfrow = NULL, mfcol = NULL, mft = NULL, byrow = TRUE, reduce = FALSE, oma = NULL, mar = NULL, mgp = NULL, plargs = NULL, ploptions = NULL, ...)plmframes(mfrow = NULL, mfcol = NULL, mft = NULL, byrow = TRUE, reduce = FALSE, oma = NULL, mar = NULL, mgp = NULL, plargs = NULL, ploptions = NULL, ...)
mfrow, mfcol
|
number of rows and columns of panels. The default is 1 for both, which will reset the subdivision of the plotting page. |
mft |
total number of panels, to be split into |
byrow |
if TRUE, the panels will be generated by rows, otherwise, by columns |
reduce |
logical: If the number of rows or columns asked for by
|
mar |
plot margins.
Any |
oma |
outer plot margins.
Any |
mgp |
margin-pars passed to |
plargs, ploptions
|
result of calling |
... |
further graphical parameters passed to
|
The function calls par. Its purpose is to simplify a call like
par(mfrow=c(3,4)) to plmframes(3,4) and to set some
defaults differently from par.
A named list containing the old values of the parameters,
as for par.
Werner A. Stahel, ETH Zurich
plmframes(2,3) plmframes(mft=15) ## will split the plotting area into >= 15 panels, plmframes() ## reset to 1 panel t.plo <- ploptions(mframesmax=9, assign=FALSE) t.mf <- plmframes(4,4, reduce=TRUE, ploptions=t.plo) par("mfg") t.mf[c("mfigsug","npages")] ## $mfigsug ## [1] 2 4 ## $npages ## [1] 2 1 ## if the device area was higher than wide, ## the result is the other way 'round t.mft <- plmframes(mft=12, reduce=TRUE, ploptions=t.plo)plmframes(2,3) plmframes(mft=15) ## will split the plotting area into >= 15 panels, plmframes() ## reset to 1 panel t.plo <- ploptions(mframesmax=9, assign=FALSE) t.mf <- plmframes(4,4, reduce=TRUE, ploptions=t.plo) par("mfg") t.mf[c("mfigsug","npages")] ## $mfigsug ## [1] 2 4 ## $npages ## [1] 2 1 ## if the device area was higher than wide, ## the result is the other way 'round t.mft <- plmframes(mft=12, reduce=TRUE, ploptions=t.plo)
The user can set (and get) 'pl' options – mostly graphical "parameters" – which influence the behavior plgraphics functions.
ploptions(x = NULL, ploptions = NULL, list = NULL, default = NULL, assign = TRUE, ...) default.ploptionsploptions(x = NULL, ploptions = NULL, list = NULL, default = NULL, assign = TRUE, ...) default.ploptions
x |
character (vector) of name(s) of ploptions to query.
If |
ploptions |
the list of options that should be inspected or
modified. Defaults to |
list |
a named list of options to be set, see Details |
default |
character vector of option names.
These ploptions will be set according to |
assign |
logical: should the list be assigned to
|
... |
any ploptions can be defined or modified,
using |
If the argument list is set, it must be a named list,
and each component, with name name and value value
is used as described for arguments in ..., see above
(in addition to such arguments).
There is an object ploptions in the pl.envir
environment, which contains the ploptions that have been used
(usually after modification) by the high level pl function
last called. This list is used by subsequent calls of lower level pl
functions. Advanced uses may want to modify this list by assigning to
pl.envir$ploptions$pch, for example.
Here is an incomplete list of the components of default.ploptions,
describing the suitable alternative values to be set by calling
ploptions. For the full set, see
?ploptions.list.
logical. If TRUE, the graphical parameter settings "mar", "oma", "cex", "mgp", and "mfg" will be maintained when leaving high level pl functions, otherwise, the old values will be restored (default).
The palette to be used by pl functions
General character size, relative to par("cex")
default argument for colorpale
vector of length 2. The first element is the
desired number of tick intervals for axes, to be used as argument
n in pretty.
The second determines how many tick labels are shown in the same
way, and should therefore be smaller than (or equal to) the first.
plotting symbols or characters
size of plotting symbols, relative to default. This may be a function with an argument that will be the number of observations at the time it is used.
size of point labels, relative to csize.pch
maximum value of size of plotting symbols
line type(s) and width(s)
colors to be used generally and
specifically for points (symbols or text) and lines, respectively,
given as index of ploptions("colors").
This are often (and by default) vectors to be used for showing
groups. The first element is usually black.
the palette to be used
...
can be
– a logical indicating if gridlines should be drawn. If
TRUE, gridlines will be drawn at the values given in
attr(.,"ticksat");
– a vector of values at which the gridlines should appear;
– a list of length 2 of such values;
– a named list. If a name equals the attribute varname
of either the x or y variable, the respective component will be
used.
smooth.lty, smooth.col: line type and color.
Note that if there is a smooth.group factor,
group.lty and group.col are used.
smooth.lwd: line width. If of length 2 (or more),
the second element is the factor by which the line width is
reduced for simulated smooths (that is, for the second to the last
column of smoothline$y). It defaults to 0.7.
proportion of fitted values to be trimmed off on
both sides when drawing a smooth line, either a number or a function
that takes the number of points as its argument.
The default is the simple function 2^log10(n)/n.
The smoothing function may produce an attribute xtrim
that is used as an additional factor to smooth.xtrim.
This is applied, e.g., to suppress trimming if a straight line is
fitted instead of a smooth by requiring smoothLm as the
smoothing function.
minimal number of observations needed for calculating a smooth.
Indicator (logical) determining whether "low" and "high" smooth lines should be drawn. See above for their definition.
Conditional quantiles for censored residuals.
logical: should bars be drawn for censored residuals?
If FALSE, censored observations will be set to the median of
the conditional distribution and shown by a different plotting
character, see argument censored of
ploptions.
If NULL, the standard plotting character will be used.
range for probabilities. If the probability corresponding to the censored part of the distribution is outside the range, bars will not be drawn.
factor by which the pcol color
will be paled to show the points (condquant.pale[1])
and the bars (...[2]).
features of plcond.
panel function to be used
number of intervals into which numerical variables will be cut
proportion of neighboring intervals for which points are shown. 0 means no overlap.
4 colors to be used to mark the points of the neighboing intervals: The first and second ones color the points lower or higher than the interval of the horizontal conditioning variable, and the other two regulating the same features for the vertical variable. The points which are outside the intervals of both conditioning variables will get a mixed color.
minimum and maximum paling, to be applied for distance 0 and maximal distance from the interval.
symbol size, relative to cex, used to
show the points outside the interval
For ploptions(x), where x is the name of a pl option,
the current value of the option,
or NULL if it is not such a name.
If x contains several (valid) names, the respective list.
For ploptions(), the list of all plptions sorted by name.
For uses setting one or more options, the important effect is a changed
list usr.ploptions in the pl.envir environment
that is used by the package's functions
(if assign is TRUE).
The (invisibly) returned value is the same list, complemented by an
attribute "old" containing the previous values of those options
that have been changed.
This list is useful for undoing the changes to restore
the previous status.
Werner A. Stahel
stamp; ploptions.list;
pl.envir;
R's own predefined options().
## get options ploptions(c("jitter.factor", "gridlines")) ploptions("stamp") ## see example(stamp) ploptions() ## all pl options, see '?ploptions.list' ## set options ploptions(stamp=FALSE, pch=0, col=c.colors[-1], anything="do what you want") ploptions(c("stamp", "anything")) ploptions(default=TRUE) ## reset all pl options, see '?ploptions.list' ## assign to transient options t.plopt <- ploptions(smooth.col="purple", assign=2) t.plopt$smooth.col attr(t.plopt, "old") ploptions("smooth.col") ## unchanged ploptions("smooth.col", ploptions=2) ## transient options pl.envir$ploptions["smooth.col"] ## the same ## switching 'margin parameters' between those used ## outside and inside high level pl functions par(mar=c(2,2,5,2)) plyx(Sepal.Width~Sepal.Length, data=iris, title="The famous iris data set") par("mar") mtext("wrong place for text",3,1, col="red") t.plo <- plmarginpar() par("mar") mtext("here is the right place",3,1) par(attr(t.plo, "oldpar")) ## resets the 'margin parameters' par("mar") plyx(Sepal.Width~Sepal.Length, data=iris, keeppar=TRUE) par("mar") ## manipulating 'pl.envir$ploptions' plyx(Sepal.Width~Sepal.Length, data=iris) pl.envir$ploptions$pch plpoints(7,4, csize=4) pl.envir$ploptions$pch <- 4 plpoints(7.5,4, csize=4)## get options ploptions(c("jitter.factor", "gridlines")) ploptions("stamp") ## see example(stamp) ploptions() ## all pl options, see '?ploptions.list' ## set options ploptions(stamp=FALSE, pch=0, col=c.colors[-1], anything="do what you want") ploptions(c("stamp", "anything")) ploptions(default=TRUE) ## reset all pl options, see '?ploptions.list' ## assign to transient options t.plopt <- ploptions(smooth.col="purple", assign=2) t.plopt$smooth.col attr(t.plopt, "old") ploptions("smooth.col") ## unchanged ploptions("smooth.col", ploptions=2) ## transient options pl.envir$ploptions["smooth.col"] ## the same ## switching 'margin parameters' between those used ## outside and inside high level pl functions par(mar=c(2,2,5,2)) plyx(Sepal.Width~Sepal.Length, data=iris, title="The famous iris data set") par("mar") mtext("wrong place for text",3,1, col="red") t.plo <- plmarginpar() par("mar") mtext("here is the right place",3,1) par(attr(t.plo, "oldpar")) ## resets the 'margin parameters' par("mar") plyx(Sepal.Width~Sepal.Length, data=iris, keeppar=TRUE) par("mar") ## manipulating 'pl.envir$ploptions' plyx(Sepal.Width~Sepal.Length, data=iris) pl.envir$ploptions$pch plpoints(7,4, csize=4) pl.envir$ploptions$pch <- 4 plpoints(7.5,4, csize=4)
The user can set (and get) 'pl' options – mostly graphical "parameters" – which influence the behavior of plgraphics functions.
## not used, this gives the complete list of 'pl' options## not used, this gives the complete list of 'pl' options
logical. If TRUE, the graphical parameter settings "mar", "oma", "cex", "mgp", and "mfg" will be maintained when leaving high level pl functions, otherwise, the old values will be restored (default).
The palette to be used by pl functions
default argument for colorpale
vector of lwd values to be used for
the different line types (lty). The package
sets lwd to a value
ploptions("linewidth")[lty]*lwd intending to balance
the visual impact of the different line types, e.g.,
to allow a dotted line to make a similar impression as a solid
line.
General character size, relative to par("cex")
vector of 4 scalars: tickmark length,
corresponding to par("tcl"). The first 2 elements
define the length of the regular tickmarks, the other two,
of the “small” tichmarks given by
attr(ticksat, "small") (ticksat is a possible
attribute of each variable).
There are two elements each in order to define tickmarks that
cross the axis.
vector of length 2. The first element is the
desired number of tick intervals for axes, to be used as argument
n in pretty.
The second determines how many tick labels are shown in the same
way, and should therefore be smaller than (or equal to) the first.
plotting symbols or characters
size of plotting symbols, relative to default. This may be a function with an argument that will be the number of observations at the time it is used.
size of point labels, relative to csize.pch
maximum value of size of plotting symbols
line type, line width, color to be used
color to be used for plotting symbols and labels, respectively
innerrange
logical: should an innerrange be used in plots if needed?
factor needed to determined the inner range
extension of the inner range
function used to calculate the inner range
extension of the data range to the plotting range
proportion of observations to be marked by their labels on the lower and upper extremes
vectors of symbols, color, line type, line color to be used for showing different y variables
plotting symbol and size, and pale value to be applied to censored observations. Different symbols are used for distinguishing right and left censoring in vertical and horizontal direction and there combination.
vector of symbols and colors used for observations and types and colors used for lines in the different groups
title parameters.
line in margin[3] on which the title appears
adjustment of the title
character size of the title, relative to
ploptions("csize")*ploptions("margin.csize")[1]
minimum csize
maximum number of characters in title
logical: should subtitle be shown?
labels of x and y axes
maximum number of panels to be shown on one page
panel function to be used in high level pl functions
axes to be shown
margin parameters.
...
their default values
character size for variable labels and tick labels
lines in margin where variable labels and tick labels are shown
expansion of margins beyond needed lines, for inner and outer margins
space between panels
date parameters.
The year which serves as origin of the internal (julian) date scale
format for showing dates
data.frame ruling how many small and large ticks and tick labels will be shown. The first column determines the row that will be used
can be
– a logical indicating if gridlines should be drawn. If
TRUE, gridlines will be drawn at the values given in
attr(.,"ticksat");
– a vector of values at which the gridlines should appear;
– a list of length 2 of such values;
– a named list. If a name equals the attribute varname
of either the x or y variable, the respective component will be
used.
logical: should zero (0) be shown be a special grid line? Can be numerical, then gives coordinates of such lines, generalizing the zero line.
line type, width and color of the zero line
reference line, any line to be added to the current
plot using the following properties. See plrefline
for possible types of values
line type, width and color of the ref line
smooth.
logical: should a smoothing line be shown?
function for calculating the smoother
parameters for the function
minimal number of observations needed for calculating a smooth.
Indicator (logical) determining whether "low" and "high" smooth lines should be drawn. See above for their definition.
smooth.lty, smooth.col: line type and color.
Note that if there is a smooth.group factor,
group.lty and group.col are used.
smooth.lwd: line width. If of length 2 (or more),
the second element is the factor by which the line width is
reduced for simulated smooths (that is, for the second to the last
column of smoothline$y). It defaults to 0.7.
paling factor to be applied for secondary smooth lines
proportion of fitted values
to be trimmed off on both sides when drawing a smooth line,
either a number or a function
that takes the number of points as its argument.
The default is the simple function 2^log10(n)/n.
The smoothing function may produce an attribute xtrim
that is used as an additional factor to smooth.xtrim.
This is applied, e.g., to suppress trimming if a straight line is
fitted instead of a smooth by requiring smoothLm as the
smoothing function.
width of the line shown at the central point of a bar
line type, width (for bar and midpoint line), color of bars
factors, multibox plots:
how should factors be plotted.
Options are "mbox", "jitter" or "asis"
minimal number of observations shown as a multibox plot
see ?plmboxes
colors to be used for multibox plots
amount of jitter, or logical: should jittering be applied?
minimal number of observations to which jittering should be applied
what proportion of the gap between different values will be filled by the jittering?
condquant: Conditional quantiles for censored residuals.
logical: should bars be drawn
for censored residuals?
If FALSE, censored observations will be set to the median of
the conditional distribution and shown by a different plotting
character, see argument censored of
ploptions.
If NULL, the standard plotting character will be used.
range for probabilities. If the probability corresponding to the censored part of the distribution is outside the range, bars will not be drawn.
factor by which the pcol color
will be paled to show the points (condquant.pale[1])
and the bars (...[2]).
plcond: features of plcond.
panel function to be used
number of intervals into which numerical variables will be cut
proportion of neighboring intervals for which points are shown. 0 means no overlap.
4 colors to be used to mark the points of the neighboing intervals: The first and second ones color the points lower or higher than the interval of the horizontal conditioning variable, and the other two regulating the same features for the vertical variable. The points which are outside the intervals of both conditioning variables will get a mixed color.
minimum and maximum paling, to be applied for distance 0 and maximal distance from the interval.
symbol size, relative to cex, used to
show the points outside the interval
adjust plot range for a subset if the range is
smaller than subset.rgratio times the plot range for the full
data set
if a function is to be shown, the number of argument values for which the function is evaluated
options for the function plregr
selection of diagnostic plots that are produced, see ...
should residuals be shown as they are or component effects added to them?
...
values of Cook's distance for which contours will be shown on the leverage plot
logical: should stamps be shown in the bottom right concern documenting the date and any project and step titles?
logical: should any documentations of the data set be shown as subtitles, i.e., at in the top margin of the plot?
logical: should notices produced by the functions be shown?
Some functions that produce nice-to-have features
are prevented from aborting the process if they fail
(by using the try function) and produce a warning instead
– unless debug is TRUE
Werner A. Stahel
names(default.ploptions)names(default.ploptions)
Draw a scatterplot or multibox plot, usuallly after pl.control
and plframe have been called.
May also be used to augment an existing plot.
plpanel(x = NULL, y = NULL, indx = NULL, indy = NULL, type = "p", frame = FALSE, title = FALSE, plargs = NULL, ploptions = NULL, marpar = NULL, ...) panelSmooth(x, y, indx, indy, plargs = NULL, ...) plpanelCond(x, y, ckeyx, ckeyy, pch = 1, pcol = 1, psize = 1, pale = c(0.2, 0.6), csize = 0.8, smooth = NULL, smooth.minobs = NULL, plargs = NULL, ploptions = NULL, ...)plpanel(x = NULL, y = NULL, indx = NULL, indy = NULL, type = "p", frame = FALSE, title = FALSE, plargs = NULL, ploptions = NULL, marpar = NULL, ...) panelSmooth(x, y, indx, indy, plargs = NULL, ...) plpanelCond(x, y, ckeyx, ckeyy, pch = 1, pcol = 1, psize = 1, pale = c(0.2, 0.6), csize = 0.8, smooth = NULL, smooth.minobs = NULL, plargs = NULL, ploptions = NULL, ...)
x |
values of the horizontal variable |
y |
values of the vertical variable |
indx |
index of the variable shown horizontally, among the
|
indy |
index of the variable shown horizontally, among the
|
type |
type of plot as usual in R: "p" for points, ... |
frame |
logical: should |
title |
logical: should |
ckeyx, ckeyy
|
vectors of 'keys' to calculate paling values and
weights for smoothing. NA means that points should not be shown
in this panel. 0 means no paling and weight 1.
Other values are between -1 and 1,
|
pch, pcol, psize
|
vector of plotting symbols, colors and sizes for plotting points |
pale |
vector of length 2 indicating the range of paling values
obtained from |
csize |
factor applied to the character expansion of the points
with |
smooth |
should a smooth line be drawn? |
smooth.minobs |
minimum number of points required for calculating and showing a smooth line |
plargs, ploptions
|
result of calling |
marpar |
margin parameters, if already available.
By default, they will be retieved from |
... |
further arguments passed to
|
The panel function plpanel draws a scatterplot if both
x and y are
numerical, and a multibox plot if one of them is a factor and
ploptions$factor.show == "mbox".
Grouping, reference and smooth lines and properties of the points
are determined by the component of plargs in plpanel.
This function is usually called by the high level pl functions
plyx and plmatrix.
A different suitable function can be used by setting their
argument panel.
The first arguments, x and y,
can be formulas, and an argument data can be given.
These arguments then have the same meaning as in plyx,
with the restriction that only one variable should result for
the x and y coordinates in the plot.
When frame is true, plpanel can be used instead of
plyx for generating a single plot.
Note that plpanel does not modify pl.envir,
in contrast to plyx.
plpanelCond shows selected points only and may show
some of them with reduced size and paled color.
It is appropriate for the high level function plcond.
none
These functions are rarely called by the user.
The intention is to modify ond of them and then call the modified
version when using plyx, plmatrix or
plcond by setting panel=mypanel.
Werner A. Stahel, ETH Zurich
plyx is essentially a wrapper function of
plpanel which calls pl.control and provides additional
features.
plmatrix also uses plpanel, whereas
plcond uses plpanelCond.
t.plargs <- pl.control(~Species+Petal.Length, ~Sepal.Width+Sepal.Length, data=iris, smooth.group=Species, pcol=Species) t.plargs$ploptions$group.col <- c("magenta","orange","cyan") plpanel(iris$Petal.Length, iris$Petal.Width, plargs=t.plargs, frame=TRUE)t.plargs <- pl.control(~Species+Petal.Length, ~Sepal.Width+Sepal.Length, data=iris, smooth.group=Species, pcol=Species) t.plargs$ploptions$group.col <- c("magenta","orange","cyan") plpanel(iris$Petal.Length, iris$Petal.Width, plargs=t.plargs, frame=TRUE)
Low level functions for plotting point and lines based on the 'pl' paradigm.
plpoints(x=NULL, y=NULL, type="p", plab=NULL, pch=NULL, pcol=NULL, col=NULL, lcol=NULL, lty=NULL, lwd=NULL, psize=NULL, csize = NULL, group = NULL, plargs = NULL, ploptions = NULL, marpar = NULL, xy = TRUE, ...) pllines(x, y, type="l", ...)plpoints(x=NULL, y=NULL, type="p", plab=NULL, pch=NULL, pcol=NULL, col=NULL, lcol=NULL, lty=NULL, lwd=NULL, psize=NULL, csize = NULL, group = NULL, plargs = NULL, ploptions = NULL, marpar = NULL, xy = TRUE, ...) pllines(x, y, type="l", ...)
x, y
|
coordinates for the horizontal and veritical axis,
respectively. If |
type |
type of displaying points. See |
plab |
labels for displaying points. Overrides labels provided by
|
pcol, col
|
color for points. |
lcol |
color for lines |
pch, psize, csize, lty, lwd
|
... and |
group |
grouping of observations, used to determine |
plargs, ploptions
|
result of |
marpar |
margin parameters, if already available.
By default, they will be retieved from |
xy |
logical: should the coordinates be obtained as in
high level graphics? This is set to |
... |
absorbs extra arguments |
For plpoints, the first arguments, x and y
can be formulas, and an argument data can be given.
These arguments then have the same meaning as in plyx.
plargs and ploptions may be specified explicitly,
but they are usually generated by calling pl.control.
plsmooth invisibly returns the data.frame needed for
drawing the smooth line. The other functions return NULL
Werner A. Stahel
plyx(Sepal.Width ~ Sepal.Length, data=iris, pcol=Species) da <- aggregate(iris[,1:4], list(Species=iris$Species), mean) plpoints(Sepal.Width ~ Sepal.Length, plargs=list(pldata=da), plab=da$Species, csize.pch=1, pcol=as.numeric(da$Species))plyx(Sepal.Width ~ Sepal.Length, data=iris, pcol=Species) da <- aggregate(iris[,1:4], list(Species=iris$Species), mean) plpoints(Sepal.Width ~ Sepal.Length, plargs=list(pldata=da), plab=da$Species, csize.pch=1, pcol=as.numeric(da$Species))
Diagnostic plots for fitted regression models: Residuals versus fit (Tukey-Anscombe plot) and/or target variable versus fit; Absolute residuals versus fit to assess equality of error variances; Normal Q-Q plot (for ordinary regression models); Residuals versus leverages to identify influential observations; Residuals versus sequence (if requested); and residuals versus explanatory variables. These plots are adjusted to the type of regression model.
plregr(x, data = NULL, plotselect = NULL, xvar = TRUE, transformed = NULL, sequence = FALSE, weights = NULL, addcomp = NULL, smooth = 2, smooth.legend = FALSE, markextremes = NA, plargs = NULL, ploptions = NULL, assign = TRUE, ...) plresx(x, data = NULL, xvar = TRUE, transformed = NULL, sequence = FALSE, weights = NULL, addcomp = NULL, smooth = 2, smooth.legend = FALSE, markextremes = NA, plargs = NULL, ploptions = NULL, assign = TRUE, ...)plregr(x, data = NULL, plotselect = NULL, xvar = TRUE, transformed = NULL, sequence = FALSE, weights = NULL, addcomp = NULL, smooth = 2, smooth.legend = FALSE, markextremes = NA, plargs = NULL, ploptions = NULL, assign = TRUE, ...) plresx(x, data = NULL, xvar = TRUE, transformed = NULL, sequence = FALSE, weights = NULL, addcomp = NULL, smooth = 2, smooth.legend = FALSE, markextremes = NA, plargs = NULL, ploptions = NULL, assign = TRUE, ...)
x |
|
data |
data set where explanatory variables and the following
possible arguments are found: |
plotselect |
which plots should be shown? See Details |
xvar |
if TRUE, residuals will be plotted versus all
explanatory variables (or terms, according to argument 'transformed')
in the model ( |
transformed |
logical: should residuals be shown against
transformed explanatory variables? If |
sequence |
if TRUE, residuals will be plotted versus the sequence as they appear in the data. If another explanatory variable is monotone increasing or decreasing, the plot is not shown, but a warning is given. |
weights |
if TRUE, residuals will be plotted versus
|
addcomp |
logical: should component effects be added to residuals for residuals versus input variables plots? |
smooth |
logical: should a smooth line be added? |
smooth.legend |
When a grouping factor is used
(argument |
markextremes |
proportion of extreme residuals to be labeled.
If all points should be labeled, let |
plargs |
result of calling |
ploptions |
list of pl options. |
assign |
logical: Should the plargs be stored
in the |
... |
Many further arguments are available to customize the plots,
see below for some of the most useful ones, and
|
Argument plotselect is used to determine which plots will be
shown. It should be a named vector of numbers indicating
do not show
show without smooth
show with smooth (not for qq nor leverage)
show with smooth and smooth band (only for resfit
in plregr and in plresx)
The default is
c( yfit=0, resfit=smdef, absresfit = NA, absresweights = NA, qq = NA,
leverage = 2, resmatrix = 1, qqmult = 3), where
smdef is 3 (actually argument smooth of
plregr.control plus 1) for normal random deviations and
one less (no band) for others.
Modify this vector to change the selection and the sequence in
which the plots appear.
Alternatively, provide a named vector defining all plots that should
be shown on a different level than the default indicates,
like plotselect = c(resfit = 2, leverage = 1).
Adding an element default = 0 suppresses all plots not
mentioned. This is useful to select single plots, like
plotselect = c(resfit = 3, default = 0)
The names of plotselect refer to:
response versus fitted values
residuals versus fitted values (Tukey-Anscombe plot)
residuals versus fitted values, defaults to TRUE for ordinary regression, FALSE for glm and others
residuals versus weights
normal Q-Q plot, defaults to TRUE for ordinary regression, FALSE for glm and others
residuals versus leverage (hat diabgonal)
scatterplot matrix of residuals for multivariate regression
qq plot for Mahlanobis lengths versus sqrt of chisquare quantiles.
In the 'resfit' (Tukey-Anscombe) plot, the reference line indicates
a "contour" line with constant values of the response variable,
constant. It has slope -1.
It is useful to judge whether any curvature shown by the smooth
might disappear after a nonlinear, monotone transformation of the
response.
If smresid is true, the 'absresfit' plot uses modified
residuals: differences between the ordinary residuals and the smooth
appearing in the 'resfit' plot.
Analogously, the 'qq' plot is then based on yet another modification
of these modified residuals: they are scaled by the smoothed scale
shown in the 'absresfit' plot, after these scales have been
standardized to have a median of 0.674 (=qnorm(0.75)).
The smoothing function used by default is smoothRegr,
which calls loess. This can be changed by setting
ploptions(smooth.function=<func>), which must have the same
arguments as smoothRegr.
The arguments lty, lwd, colors characterize how the graphical
elements in the plot are shown.
They should be three vectors of length 9 each, defining the
line types, line widths, and colors to be used for ...
observations;
reference lines;
smooth;
simulated smooths;
component effects in plresx;
confidence bands of component effects.
In the case of glm.restype="cond.quant"
(random) observations;
conditional medians;
bars showing conditional quantiles.
If smooths are shown according to groups (given in
smooth.group), then a legend can be required and positioned
in the respecive plots by using the argument smooth.legend.
If it is TRUE, then the legend will be placed in the
"bottomright" corner.
Alternatively, the corner can be specified as
"bottomright", "bottomleft", "topleft", or "topright".
A coordinate pair may also be given.
These possibilities can be used individually for each plot by
giving a named vector or a named list, where the names are
one of "yfit", "resfit", "absresfit", "absresweight", ".xvar." or
names of x variables provided by the xvar argument.
A component ".xvar." selects the first x variable.
There is an hidden argument innerrange.fit that allows
for fixing an inner range for plotting the fitted values.
The list of the evaluations of all arguments and some more useful items is returned invisibly.
This is a function under development. Future versions may behave differently and may not be compatible with this version.
Werner A. Stahel, ETH Zurich
data(LifeCycleSavings, package="datasets") r.savings <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings) plregr(r.savings) ## --- *transformed* linear model data(d.blast) r.blast <- lm(log10(tremor) ~ location+log10(distance)+log10(charge), data=d.blast) plregr(r.blast, sequence=TRUE, transformed=TRUE) plregr(r.blast, xvar=FALSE, innerrange.fit=c(0.3,1.2)) ## --- multivariate regression data(d.fossileSamples) r.foss <- lm(cbind(sAngle,lLength,rWidth) ~ SST+Salinity+lChlorophyll+Region+N, data=d.fossileSamples) plregr(r.foss, plotselect=c(resfit=3, resmatrix=1, qqmult=1)) ## --- logistic regression data(d.babysurvival) rr <- glm(Survival ~ Weight+Age+Apgar1, data=d.babysurvival, family=binomial) plregr(rr, xvar= ~Weight, cex.plab=0.7, ylim=c(-5,5)) plregr(rr, condquant=FALSE) ## --- ordinal regression if(requireNamespace("MASS")) { data(housing, package="MASS") rr <- MASS::polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) plregr(rr, factor.show="jitter") }data(LifeCycleSavings, package="datasets") r.savings <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings) plregr(r.savings) ## --- *transformed* linear model data(d.blast) r.blast <- lm(log10(tremor) ~ location+log10(distance)+log10(charge), data=d.blast) plregr(r.blast, sequence=TRUE, transformed=TRUE) plregr(r.blast, xvar=FALSE, innerrange.fit=c(0.3,1.2)) ## --- multivariate regression data(d.fossileSamples) r.foss <- lm(cbind(sAngle,lLength,rWidth) ~ SST+Salinity+lChlorophyll+Region+N, data=d.fossileSamples) plregr(r.foss, plotselect=c(resfit=3, resmatrix=1, qqmult=1)) ## --- logistic regression data(d.babysurvival) rr <- glm(Survival ~ Weight+Age+Apgar1, data=d.babysurvival, family=binomial) plregr(rr, xvar= ~Weight, cex.plab=0.7, ylim=c(-5,5)) plregr(rr, condquant=FALSE) ## --- ordinal regression if(requireNamespace("MASS")) { data(housing, package="MASS") rr <- MASS::polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) plregr(rr, factor.show="jitter") }
plregr
Specify some arguments of minor importance for the function
plregr
plregr.control(x, data = NULL, xvar = TRUE, transformed = FALSE, weights = NULL, stdresid = TRUE, mar = NULL, glm.restype = "working", condquant = TRUE, smresid = TRUE, partial.resid = NULL, addcomp = NULL, cookdistlines = NULL, leveragelimit = NULL, condprob.range = NULL, testlevel = 0.05, refline = TRUE, smooth = 2, smooth.sim = NULL, xlabs = NULL, reslabs = NULL, markextremes = NULL, mf = TRUE, mfcol = FALSE, multnrow = 0, multncol = 0, marmult = NULL, oma = NULL, assign = TRUE, ...)plregr.control(x, data = NULL, xvar = TRUE, transformed = FALSE, weights = NULL, stdresid = TRUE, mar = NULL, glm.restype = "working", condquant = TRUE, smresid = TRUE, partial.resid = NULL, addcomp = NULL, cookdistlines = NULL, leveragelimit = NULL, condprob.range = NULL, testlevel = 0.05, refline = TRUE, smooth = 2, smooth.sim = NULL, xlabs = NULL, reslabs = NULL, markextremes = NULL, mf = TRUE, mfcol = FALSE, multnrow = 0, multncol = 0, marmult = NULL, oma = NULL, assign = TRUE, ...)
x |
an object (result of a call to a model fitting function
such as |
data |
see |
xvar |
variables for which residuals shall be plotted.
Either a formula like |
transformed |
see |
weights |
logical: should residuals be plotted against weights?
Used in |
stdresid |
logical: should leverages and standardized residuals
be calculated? This is avoided for |
mar |
plot margins |
glm.restype |
type of residuals to be used for glm models.
In addition to those allowed in |
condquant |
logical: should conditional quantiles be shown for censored observations, binary and ordered responses? |
smresid |
logical: Should residuals from smooth be used for 'tascale' and 'qq' plots? |
partial.resid, addcomp
|
logical, synonyms: Should component effects be added to the residuals? This leads to what some authors call "partial residual plot". |
cookdistlines |
levels of Cook distance for which contours are plotted in the leverage plot |
leveragelimit |
bound for leverages to be used in standardizing
residuals and in calculation of standardized residuals from smooth
(if |
condprob.range |
numeric vector of length 2.
In the case of residuals of class |
testlevel |
level for statistical tests |
refline |
logical: should reference line be shown?
If |
smooth |
if TRUE (or 1), smooths are added to the plots where
appropriate. If |
smooth.sim |
number of simulated smooths added to each plot.
If NULL (the default) 19 simulated smooths will be generated if
possible and sensible (i.e., none if |
xlabs |
labels for x variables. Defaults to |
reslabs |
labels for vertical axes |
markextremes |
proportion of extreme residuals to be labeled.
If all points should be labeled, let |
mf |
vector of 2 elements, indicating the number of rows and
columns of panels on each plot page.
Defaults to |
mfcol |
if TRUE, the panel will be filled columnwise |
multnrow, multncol
|
number of rows and columns of panels on one page, for residuals of multivariate regression only |
marmult |
plot margins for scatterplot matrices in the case of multivariate regression |
oma |
vector of length 4 giving the number of lines in the outer margin. If it is of length 2, they refer to top an right margins. |
assign |
logical: should the result of |
... |
further arguments in the call, to be ignored by 'plotregr.control' |
A list containing all the items needed to specify plotting
in plregr and plresx
This function is not explicitly called by the user, but by
plregr and plresx.
All the arguments specified here can and should be given as
arguments to these functions.
Werner A. Stahel, Seminar for Statistics, ETH Zurich
data(d.blast) ( r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) ) plargs <- plregr.control(r.blast, formula = ~.+distance, transformed=TRUE, smooth.group = location ) showd(plargs$pdata) names(plargs)data(d.blast) ( r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) ) plargs <- plregr.control(r.blast, formula = ~.+distance, transformed=TRUE, smooth.group = location ) showd(plargs$pdata) names(plargs)
Plot 2 variables, showing a third one with line symbols. Most suitable for showing residuals of a model as this third variable.
plres2x(formula = NULL, reg = NULL, data = NULL, restrict = NULL, size = 1, xlab = NULL, ylab = NULL, pale = 0.2, plargs = NULL, ploptions = NULL, assign = TRUE, ...)plres2x(formula = NULL, reg = NULL, data = NULL, restrict = NULL, size = 1, xlab = NULL, ylab = NULL, pale = 0.2, plargs = NULL, ploptions = NULL, assign = TRUE, ...)
formula |
a formula of the form |
reg |
the result of the model fit, from which the residuals are extracted |
data |
the data.frame where the variables are found. Only needed if the variable 'x' or 'y' is not available from the fitting results. |
restrict |
absolute value which truncates the size.
if |
size |
the symbols are scaled so that |
xlab, ylab
|
labels for horizontal and vertical axes. Default to the variable names (or labels) |
.
pale |
scalar between 0 and 1: The points are shown in a more
pale color than the segments as determined by
|
plargs |
result of calling |
ploptions |
list of pl options. |
assign |
logical: Should the plargs be stored
in the |
... |
further arguments, passed to |
none.
Werner A. Stahel and Andreas Ruckstuhl
data(d.blast) t.r <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) plres2x(~distance+charge, t.r)data(d.blast) t.r <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) plres2x(~distance+charge, t.r)
Generates plscaled values and appropriate tick mark positions and labels for expressing a variable on a plscaled scale, e.g., on log scale
plscale(x, plscale = "log10", ticksat = NULL, logscale = NULL, valuesonly = FALSE, ploptions = NULL)plscale(x, plscale = "log10", ticksat = NULL, logscale = NULL, valuesonly = FALSE, ploptions = NULL)
x |
data to be used in plotting |
plscale |
name of the function defining the plscaled scale |
ticksat |
tick locations, If |
logscale |
if |
valuesonly |
logical: should only the transformed values be returned? Otherwise, axis ranges and tick information is also calculated. |
ploptions |
See |
The x data is returned, augmented by the following attributes:
the plscaled values to be used for plotting
the location of tick marks (plscaled values)
the labels for the tick marks showing the original scale
the name of the function used for the plscaleation
Besides the logarithmic plscale that is supported by core R graphics, any other plscaleation may be used, notably the so-called "first aid plscaleations".
Werner A. Stahel
x <- 10^seq(-1,3,0.5) plscale(x) xx <- plscale(x, plscale="sqrt") plyx(xx) x <- seq(0,100,2) plyx(plscale(x, plscale="asinp"), type="l")x <- 10^seq(-1,3,0.5) plscale(x) xx <- plscale(x, plscale="sqrt") plyx(xx) x <- seq(0,100,2) plyx(plscale(x, plscale="asinp"), type="l")
These functions add smooths or reference lines to an existing pl plot.
plsmooth(x = NULL, y = NULL, ysec = NULL, band=NULL, power = NULL, group = NULL, weight = NULL, smooth = TRUE, plargs = NULL, ploptions = NULL, xy = TRUE, ...) plsmoothline(smoothline = NULL, x = NULL, y = NULL, ysec = NULL, smooth.col = NULL, smooth.lty = NULL, smooth.lwd = NULL, plargs = NULL, ploptions = NULL, marpar = NULL, ...) plrefline(refline, x=NULL, innerrange=NULL, y=NULL, cutrange = c(x = TRUE, y = FALSE), plargs=NULL, ploptions=NULL, ...)plsmooth(x = NULL, y = NULL, ysec = NULL, band=NULL, power = NULL, group = NULL, weight = NULL, smooth = TRUE, plargs = NULL, ploptions = NULL, xy = TRUE, ...) plsmoothline(smoothline = NULL, x = NULL, y = NULL, ysec = NULL, smooth.col = NULL, smooth.lty = NULL, smooth.lwd = NULL, plargs = NULL, ploptions = NULL, marpar = NULL, ...) plrefline(refline, x=NULL, innerrange=NULL, y=NULL, cutrange = c(x = TRUE, y = FALSE), plargs=NULL, ploptions=NULL, ...)
x, y
|
coordinates for the horizontal and veritical axis,
respectively. If |
ysec |
for |
band |
logical: should a band (e.g., a confidence band) be drawn together with the smooth? |
power |
for |
group |
for |
weight |
weights of observations used for generating the smooth |
smooth |
logical: should smooth be done? Will almost always be
|
smoothline |
for |
smooth.col, smooth.lty, smooth.lwd
|
for |
refline |
for |
innerrange |
for |
cutrange |
for |
plargs, ploptions
|
result of |
marpar |
margin parameters, if already available.
By default, they will be retieved from |
xy |
logical: should the coordinates be obtained as in
high level graphics? This is set to |
... |
absorbs extra arguments |
The argument refline accepts different types of values.
If it is a function, it must either accept a formula
(which will be y~x) as its first argument or
x and y as the first two arguments.
Alternatively, refline can be
a list with components x and y
and possibly a component band that contains the coordinates
of the line (or lines, if y is a matrix) and the width of
a band around it (that is, additional lines, to be drawn with
ploptions("refline.col")[2]).
In order to obtain more than one reference line, a list of such
items may be given. It should not have compontents named
coef, coefficients, x or y, since it would otherwise
be mistaken for an argument of the types just described.
The components may carry attributes lty, lwd and lcol
to specify the properties of the lines individually. See Examples.
plsmooth and plrefline are very similar.
They are both called by high level pl functions.
plsmooth gets its smoothing function from
ploptions("smooth.function").
Their properties (line type, width, color) come from different
sets of pl options. plsmooth can also respect a group
structure in the data.
If x or y has an attribute "numvalues",
these are used as the values to calculate the smooth or the
refline.
plargs and ploptions may be specified explicitly,
but they are usually generated by calling pl.control.
The argument getpar is used for setting the graphical
parameters mar, mgp according to ploptions?
This is needed if the high level pl function has changed mar,
since this change has been reversed when the function was left.
By default, these graphical parameters will be retieved from
pl.envir$ploptions.
plsmooth invisibly returns the data.frame needed for
drawing the smooth line. The other functions return NULL
Werner A. Stahel
plyx(Sepal.Width ~ Sepal.Length, data=iris, smooth=TRUE, smooth.group=Species, pch=Species) plsmooth(smooth.group=FALSE) ## plrefline called from plyx plyx(Sepal.Width ~ Sepal.Length, data=iris, smooth=TRUE, pch=Species, smooth.group=iris$Species, refline=lm) ## more reference lines plrefline(list(c(-2,1), structure(c(-2.3,1), lcol="purple", lty=1)))plyx(Sepal.Width ~ Sepal.Length, data=iris, smooth=TRUE, smooth.group=Species, pch=Species) plsmooth(smooth.group=FALSE) ## plrefline called from plyx plyx(Sepal.Width ~ Sepal.Length, data=iris, smooth=TRUE, pch=Species, smooth.group=iris$Species, refline=lm) ## more reference lines plrefline(list(c(-2,1), structure(c(-2.3,1), lcol="purple", lty=1)))
Select rows of data.frames keeping the variable attributes that drive pl graphics
plsubset(x, subset = NULL, omit = NULL, select = NULL, drop = FALSE, keeprange = FALSE)plsubset(x, subset = NULL, omit = NULL, select = NULL, drop = FALSE, keeprange = FALSE)
x |
data.frame from which the subset is to be generated |
subset, omit
|
logical vector or vector of indices of rows or or
rownames of |
select |
vector of indices or names of variables to be selected |
drop |
logical: if only one variable remains, should the data.frame be converted into a vector? |
keeprange |
logical: should ranges
( |
plsubset maintains the 'pl' attributes of the variables
of the data.frame (if there are), such as 'col', 'lty', ..., and
subsets the two attributes 'numvalues' and 'plcoord'.
This is useful if the way of displaying the axis is to be kept when a
new plot is drawn.
Data.frame with the selected rows (or without the omitted
rows, respectively) and all attributes as described above.
Werner A. Stahel
Argument subset of the high level 'pl' functions
plyx, plmatrix
data(d.river) dd <- d.river[seq(1,1000,4),] dd$date <- gendateaxis("date",hour="hour", data=dd) attr(dd$date, "ticksat") dsubs <- plsubset(dd, subset=1:50) attr(dsubs$date, "ticksat") plyx(O2~date, data=dsubs) ## same as ## plyx(O2~date, data=dd, subset=1:50)data(d.river) dd <- d.river[seq(1,1000,4),] dd$date <- gendateaxis("date",hour="hour", data=dd) attr(dd$date, "ticksat") dsubs <- plsubset(dd, subset=1:50) attr(dsubs$date, "ticksat") plyx(O2~date, data=dsubs) ## same as ## plyx(O2~date, data=dd, subset=1:50)
Find ticks locations and labels
plticks(range, plscale = NULL, transformed = FALSE, nouter = 0, tickintervals = NULL, ploptions = NULL)plticks(range, plscale = NULL, transformed = FALSE, nouter = 0, tickintervals = NULL, ploptions = NULL)
range |
range of values that the ticks should cover |
plscale |
function defining the scale of the axis. Either the name of the function or a function, see Details. |
transformed |
logical: Is |
nouter |
number of outer .. |
tickintervals |
approximate number of tick intervals desired.
Default is taken from |
ploptions |
pl options |
plticks calls pretty for getting
tick locations if plscale is not specified and
prettyscale if it is.
It generates another set for locations of tick labels if
tickintervals has 2 elements, such that not all ticks
are labelled.
The scaling function plscale can be given by its name
if that name is one of
log, log10, logst, sqrt, asinp, logit, qnorm.
Otherwise, it must be a function with an attribute
inverse that defines the inverse function.
It should also have an attribute range and an
attribute range.transformed if the possible
range for its argument or its values are restricted,
like asinp that is defined for values between 0 and 100
and has values in the interval from 0 to 1.
A list with components
ticksat |
locations of ticks |
ticklabelsat |
locations of tick labels |
ticklabels |
tick labels, if |
Werner A. Stahel
plticks(c(23,87)) plticks(c(23,91), plscale="asinp", transformed=FALSE, tickintervals=c(10,2)) asinp ## shows the attributes 'inverse', 'range' and 'range.transformed'plticks(c(23,87)) plticks(c(23,91), plscale="asinp", transformed=FALSE, tickintervals=c(10,2)) asinp ## shows the attributes 'inverse', 'range' and 'range.transformed'
A scatterplot or a bunch of them is produced according to the concept of the plplot package
plyx(x = NULL, y = NULL, by=NULL, group = NULL, data = NULL, type = "p", panel = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, markextremes = 0, rescale = TRUE, mar = NULL, mf = FALSE, plargs = NULL, ploptions = NULL, assign = TRUE, ...)plyx(x = NULL, y = NULL, by=NULL, group = NULL, data = NULL, type = "p", panel = NULL, xlab = NULL, ylab = NULL, xlim = NULL, ylim = NULL, markextremes = 0, rescale = TRUE, mar = NULL, mf = FALSE, plargs = NULL, ploptions = NULL, assign = TRUE, ...)
x |
either a formula or the data to be used for the horizontal axis. If a formula of the type 'y~x', the variable 'y' in 'data' will be plotted against the variable(s) 'x'. If a data.frame with more than one column is given, each column will be used in turn to produce a plot. |
y |
data to be used as the y axis. |
by |
grouping factor: for each |
group |
grouping that determines plotting symbols, colors, and line types |
data |
data.frame containing the variables if 'x' is a formula |
xlab, ylab
|
axis labels |
xlim, ylim
|
plot ranges |
type |
type of plot, see |
panel |
panel function to do the actual drawing. See Details. |
markextremes |
proportion of extreme residuals to be labeled.
If all points should be labeled, let |
rescale |
logical. Only applies if there are multiple y
variables. If |
mar |
plot margins, see |
mf |
number of multiple frames. If more than one plot will be
generated because of a grouping or multiple x variables,
multiple frames will be produced by calling |
plargs |
result of calling |
ploptions |
list of pl options. |
assign |
logical: Should the plargs be stored
in the |
... |
more arguments, to be passed to |
panel defaults to plpanel, which results essentially in
points or text
depending on the argument pch including a smooth line,
to plmboxes if 'x' is a factor and 'y' is not or
vice versa,
or to a modification of sunflowers if both are factors.
The function must have the arguments x and y
to take the coordinates of the points and may have the arguments
indx and indy to transfer the two variables' indexes and
panelargs for any additional objects to be passed on.
None.
There are many more arguments, obtained from pl.control,
see ?pl.control. These can be passed to plmatrix
by an argument plargs that is hidden in the ... argument list.
Werner A. Stahel, ETH Zurich
plmatrix, plcond;
pl.control, ploptions
plyx(Petal.Width ~ Sepal.Length, data=iris) plyx(Petal.Width ~ Sepal.Length+Sepal.Width, data=iris, smooth=TRUE, group=Species) plyx(Petal.Length + Petal.Width ~ Sepal.Length+Sepal.Width, by = Species, data=iris, smooth=TRUE)plyx(Petal.Width ~ Sepal.Length, data=iris) plyx(Petal.Width ~ Sepal.Length+Sepal.Width, data=iris, smooth=TRUE, group=Species) plyx(Petal.Length + Petal.Width ~ Sepal.Length+Sepal.Width, by = Species, data=iris, smooth=TRUE)
Methods of predict and fitted
## S3 method for class 'regrpolr' predict(object, newdata = NULL, type = c("class", "probs", "link"), ...) ## S3 method for class 'regrpolr' fitted(object, type = c("class", "probs", "link"), ...)## S3 method for class 'regrpolr' predict(object, newdata = NULL, type = c("class", "probs", "link"), ...) ## S3 method for class 'regrpolr' fitted(object, type = c("class", "probs", "link"), ...)
object |
result of |
newdata |
data frame in which to look for variables with
which to predict. If |
type |
type of prediction: |
... |
arguments passed to standard methods of |
Vector of predicted or linear predictor values
Werner A. Stahel, ETH Zurich
predict, fitted, residuals.regrpolr
if(requireNamespace("MASS")) { data(housing, package="MASS") rr <- MASS::polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) aa <- fitted(rr) bb <- predict(rr) cc <- predict.regrpolr(rr) }if(requireNamespace("MASS")) { data(housing, package="MASS") rr <- MASS::polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) aa <- fitted(rr) bb <- predict(rr) cc <- predict.regrpolr(rr) }
Compute about n 'round' values that are about equally spaced
in a transformed (plotting) scale and cover the range of the values
in x.
prettyscale(x, transformed = FALSE, plscale = "log10", inverse = NULL, range = NULL, range.transformed = NULL, n = NULL, logscale = NULL)prettyscale(x, transformed = FALSE, plscale = "log10", inverse = NULL, range = NULL, range.transformed = NULL, n = NULL, logscale = NULL)
x |
numeric vector of data (original scale) |
transformed |
logical: Is |
plscale |
name of the transformation defining the plotting scale |
inverse |
back (or inverse) back transformation |
range, range.transformed
|
admissible range of original and transformed values, respectively. Usually not needed, cf. Details |
n |
approximate number of tickmark locations.
If of length |
logscale |
if |
prettyscale generates n+2 "anchor" values in the
transformed scale which cover the range of the transformed x
values and are equidistant within the range.
It then back-transforms these anchor values. For each one of them,
say c,
it seeks a pretty value near to it by the following construction:
it calls the R function pretty on the range given by the
back-transformed neighboring anchor values, asking for n[2]
pretty values. From these, it chooses the one for which the
transformed value is closest to the transformed c.
Therefore, if n[2] is large, the pretty values may be less
pretty, whereas small n[2] may lead to equal pretty values
for neighboring anchors and thus to too few resulting pretty values.
The default value for n[2] is 3.
The ranges are needed to get the limits as pretty values when
appropriate (and to avoid warning messages).
They are generated in the function for the commonly used plscales
and may be given as attributes of the plscale function,
see Examples.
Numeric vector of tick mark locations in transformed scale,
with an attribute ticklabels containing the appropriate
tick marks and labels (in original scale)
The function does not always lead to consistent results.
Increasing n sometimes leads to fewer resulting values.
W. A. Stahel
prettyscale(10^rnorm(10)) prettyscale(c(0.5, 2, 10, 90), plscale="sqrt") prettyscale(c(50,90,95,99), plscale="asinp", n=10) ## asinp has the useful attributes: asinpprettyscale(10^rnorm(10)) prettyscale(c(0.5, 2, 10, 90), plscale="sqrt") prettyscale(c(50,90,95,99), plscale="asinp", n=10) ## asinp has the useful attributes: asinp
Density, distribution function, quantile function and random
generation for the “Reverse” Gumbel distribution with
parameters location and scale.
drevgumbel (x, location = 0, scale = 1) prevgumbel (q, location = 0, scale = 1) qrevgumbel (p, location = 0, scale = 1) rrevgumbel (n, location = 0, scale = 1)drevgumbel (x, location = 0, scale = 1) prevgumbel (q, location = 0, scale = 1) qrevgumbel (p, location = 0, scale = 1) rrevgumbel (n, location = 0, scale = 1)
x, q
|
numeric vector of abscissa (or quantile) values at which to evaluate the density or distribution function. |
p |
numeric vector of probabilities at which to evaluate the quantile function. |
location |
location of the distribution |
scale |
scale ( |
n |
number of random variates, i.e., |
a numeric vector, of the same length as x, q, or
p for the first three functions, and of length n for
rrevgumbel().
Werner A. Stahel; partly inspired by package VGAM. Martin Maechler for numeric cosmetic.
the Weibull distribution functions in R's stats package.
curve(prevgumbel(x, scale= 1/2), -3,2, n=1001, col=1, lwd=2, main = "revgumbel(x, scale = 1/2)") abline(h=0:1, v = 0, lty=3, col = "gray30") curve(drevgumbel(x, scale= 1/2), n=1001, add=TRUE, col = (col.d <- adjustcolor(2, 0.5)), lwd=3) legend("left", c("cdf","pdf"), col=c("black", col.d), lwd=2:3, bty="n") med <- qrevgumbel(0.5, scale=1/2) cat("The median is:", format(med),"\n")curve(prevgumbel(x, scale= 1/2), -3,2, n=1001, col=1, lwd=2, main = "revgumbel(x, scale = 1/2)") abline(h=0:1, v = 0, lty=3, col = "gray30") curve(drevgumbel(x, scale= 1/2), n=1001, add=TRUE, col = (col.d <- adjustcolor(2, 0.5)), lwd=3) legend("left", c("cdf","pdf"), col=c("black", col.d), lwd=2:3, bty="n") med <- qrevgumbel(0.5, scale=1/2) cat("The median is:", format(med),"\n")
Quantiles for weighted observations
quantilew(x, probs = c(0.25, 0.5, 0.75), weights = 1, na.rm=FALSE)quantilew(x, probs = c(0.25, 0.5, 0.75), weights = 1, na.rm=FALSE)
x |
numeric vector whose sample quantiles are wanted 'NA' and 'NaN' values are not allowed unless 'na.rm' is 'TRUE'. |
probs |
numeric vector of probabilities with values in [0,1]. |
weights |
numeric vector of weights. They will be standardized to sum to 1. |
na.rm |
remove NAs from 'x'? If FALSE and 'x' contains NAs, the value will be NA. |
Empirical quantiles corresponding to the given probabilities and weights. If a quantile is not unique since the cumulated weights hit the probability value exactly (the case of the median of a sample of even size), the mean of the corresponding values is returned.
Werner A. Stahel
x <- c(1,3,4,8,12,13,18,20) quantile(x, c(0.25, 0.5)) quantilew(x, c(0.25, 0.5), weights=1:8) ## 8 13 ## relative weights (1+2+3)/36 sum to <0.25 , with the forth, they ## are over 0.25, therefore, the quantile is the 4th valuex <- c(1,3,4,8,12,13,18,20) quantile(x, c(0.25, 0.5)) quantilew(x, c(0.25, 0.5), weights=1:8) ## 8 13 ## relative weights (1+2+3)/36 sum to <0.25 , with the forth, they ## are over 0.25, therefore, the quantile is the 4th value
This function implements a version of empirical quantiles based on interpolation
quinterpol(x, probs = c(0.25, 0.5, 0.75), extend = FALSE)quinterpol(x, probs = c(0.25, 0.5, 0.75), extend = FALSE)
x |
vector of data determining the quantiles |
probs |
vector of probabilities defining which quantiles should be produced |
extend |
logical: Should quantiled be calculated outside the range of the data by linear extrapolation? This may make sense if the sample is small or the data is rounded or grouped or a score. |
The empirical quantile function jumps at the data values according to the usual definition. The version of quantiles calculated by 'quinterpol' avoids jumps. It is based on linear interpolation of the step version of the empirical cumulative distribution function, using as the given points the midpoints of both vertical and horizontal pieces of the latter. See 'examples' for a visualization.
vector of quantiles
Werner A. Stahel
quantile
## This example illustrates the definition of the "interpolated quantiles" set.seed(2) t.x <- sort(round(2*rchisq(20,2))) table(t.x) t.p <- ppoints(100) plot(quinterpol(t.x,t.p),t.p, type="l")## This example illustrates the definition of the "interpolated quantiles" set.seed(2) t.x <- sort(round(2*rchisq(20,2))) table(t.x) t.p <- ppoints(100) plot(quinterpol(t.x,t.p),t.p, type="l")
Methods of residuals for classes
polr, survreg and coxph,
calculating quartiles and random numbers according to the
conditional distribution of residuals for the latent variable of a
binary or ordinal regression or a regression with censored response,
given the observed response value.
See Details for an explanation.
## S3 method for class 'polr' residuals(object, type="condquant", ...) ## S3 method for class 'regrpolr' residuals(object, type="condquant", ...) ## S3 method for class 'regrsurvreg' residuals(object, type="condquant", ...) ## S3 method for class 'regrcoxph' residuals(object, type="CoxSnellMod", ...)## S3 method for class 'polr' residuals(object, type="condquant", ...) ## S3 method for class 'regrpolr' residuals(object, type="condquant", ...) ## S3 method for class 'regrsurvreg' residuals(object, type="condquant", ...) ## S3 method for class 'regrcoxph' residuals(object, type="CoxSnellMod", ...)
object |
the result of |
type |
type of residuals:
|
... |
arguments passed to standard methods of |
For binary and ordinal regression, the regression models can be described by introducing a latent response variable Z of which the observed response Y is a classified version, and for which a linear regression applies. The errors of this "latent regression" have a logistic distribution. Given the linearly predicted value eta[i], which is the fitted value for the latent variable, the residual for Z[i] can therefore be assumed to have a logistic distribution.
This function calculates quantiles and random numbers according to the conditional distribution of residuals for Z[i], given the observed y[i].
Modified Cox-Snell residuals: Cox-Snell residuals are defined in a way that they always follow an exponential distribution. Since this is an unususal law for residuals, it is convenient to transform them such that they then obey a standard normal distribution. See the vignette for more detail.
Vector of residual values. If conditional quantiles are requested,
the residuals for censored observations are replaced by conditional
medians, and an attribute "condquant" is attached, which is
a data.frame with the variables
median |
median of the conditional distributions |
lowq |
lower quartile |
uppq |
upper quartile |
random |
random number, drawn according to the conditional distribution |
prob |
probability of the condition being true |
limlow, limup
|
lower and upper limits of the intervals |
index |
index of the observation in the sequence of the result (residuals) |
fit |
linear predictor value |
y |
observed response value |
residuals.polr and residuals.regrpolr are identical
for the time being. Only type="condquant" is available now.
Werner A. Stahel, ETH Zurich
See http://stat.ethz.ch/~stahel/regression
require(MASS) data(housing, package="MASS") rr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) t.res <- residuals.regrpolr(rr) head (t.res) summary(t.res)require(MASS) data(housing, package="MASS") rr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing) t.res <- residuals.regrpolr(rr) head (t.res) summary(t.res)
Determines a robust range of the data on the basis of the trimmed mean and mean absolute deviation
robrange(data, trim = 0.2, fac = 5.0, na.rm=TRUE)robrange(data, trim = 0.2, fac = 5.0, na.rm=TRUE)
data |
a vector of data. Missing values are dropped |
trim |
trimming proportion |
fac |
factor used for expanding the range, see Details |
na.rm |
logical: should NAs be removed? If FALSE, result will be NA if there are NAs in 'data'. |
The function determines the trimmed mean m and then the "upper
trimmed mean" s of absolute deviations from m, multiplied by
fac. The robust minimum is then defined as m-fac*s or
min(data), whichever is larger, and similarly for the maximum.
The robust range.
Werner A. Stahel
x <- c(rnorm(20),rnorm(3,5,20)) robrange(x)x <- c(rnorm(20),rnorm(3,5,20)) robrange(x)
Strings are shortened if they are longer than
n
shortenstring(x, n = 50, endstring = "..", endchars = NULL)shortenstring(x, n = 50, endstring = "..", endchars = NULL)
x |
a string or a vector of strings |
n |
maximal character length |
endstring |
string(s) to be appended to the shortened strings |
endchars |
number of last characters to be shown at the end of
the abbreviated string. By default, it adjusts to |
Abbreviated string(s)
Werner A. Stahel
shortenstring("abcdefghiklmnop", 8) shortenstring(c("aaaaaaaaaaaaaaaaaaaaaa","bbbbc", "This text is certainly too long, don't you think?"),c(8,3,20))shortenstring("abcdefghiklmnop", 8) shortenstring(c("aaaaaaaaaaaaaaaaaaaaaa","bbbbc", "This text is certainly too long, don't you think?"),c(8,3,20))
Shows a part of the data.frame which allows for grasping the nature of the data. The function is typically used to make sure that the data is what was desired and to grasp the nature of the variables in the phase of getting acquainted with the data.
showd(data, first = 3, nrow. = 4, ncol. = NULL, digits=getOption("digits"))showd(data, first = 3, nrow. = 4, ncol. = NULL, digits=getOption("digits"))
data |
a data.frame, a matrix, or a vector |
first |
the first |
nrow. |
a selection of |
ncol. |
number of columns (variables) to be shown. The first and
last columns will also be included. If |
digits |
number of significant digits used in formatting numbers |
The tit attribute of data will be printed if available and
getUserOption("doc") > 0, and any doc attribute,
if getUserOption("doc") >= 2 (see tit).
returns invisibly the character vector containing the formatted data
Werner A. Stahel, ETH Zurich
showd(iris) data(d.birthrates) names(d.birthrates) ## only show 7 columns, including the first and last showd(d.birthrates, ncol=7) showd(cbind(1:100))showd(iris) data(d.birthrates) names(d.birthrates) ## only show 7 columns, including the first and last showd(d.birthrates, ncol=7) showd(cbind(1:100))
Simulates residuals for a given regression model
simresiduals(object, ...) ## Default S3 method: simresiduals(object, nrep=19, simfunction=NULL, stdresiduals = NULL, sigma = object$sigma, ...) ## S3 method for class 'glm' simresiduals(object, nrep=19, simfunction=NULL, glm.restype="working", ...)simresiduals(object, ...) ## Default S3 method: simresiduals(object, nrep=19, simfunction=NULL, stdresiduals = NULL, sigma = object$sigma, ...) ## S3 method for class 'glm' simresiduals(object, nrep=19, simfunction=NULL, glm.restype="working", ...)
object |
result of fitting a regression |
nrep |
number of replicates |
simfunction |
if a function, it is used to generate random values for
the target variable, with three arguments, which will be fed by
the number of observations, the fitted values, and
|
stdresiduals |
logical: should standardized residuals be produced? |
sigma |
scale parameter to be used, defaults to
|
glm.restype |
type of residuals to be generated (for glm) Warning: type "deviance" may produce NAs. |
... |
further arguments passed to forthcoming methods. |
The simulated residuals are obtained for the default method
by replacing the response variable by permuted standardized residuals
of the fitted regression, multiplied by the scale
object\$sigma, then fitting the model to these residuals and
getting the reseiduals from this new fit.
This is repeated nrep times.
If standarized residuals are not available, ordinary residuals are
used.
For the glm method, the values of the response variable are
obtained from simulating according to the model (binomial or Poisson),
and the model is re-fitted to these generated values.
A matrix of which each column contains an set of simulated residuals.
If standardized residuals are available,
attribute "stdresisduals" is the matrix containing corresponding
standardized residuals.
Werner A. Stahel, ETH Zurich
data(d.blast) r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) r.simblast <- simresiduals(r.blast, nrep=5) showd(r.simblast) ## -------------------------- data(d.babysurvival) r.babysurv <- lm( Survival~Weight+Age+Apgar1, data=d.babysurvival) r.simbs <- simresiduals(r.babysurv, nrep=5) showd(r.simbs)data(d.blast) r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) r.simblast <- simresiduals(r.blast, nrep=5) showd(r.simblast) ## -------------------------- data(d.babysurvival) r.babysurv <- lm( Survival~Weight+Age+Apgar1, data=d.babysurvival) r.simbs <- simresiduals(r.babysurv, nrep=5) showd(r.simbs)
Adjust the smoothing parameter to number of observations
smoothpar(n)smoothpar(n)
n |
number of observations |
smoothing parameter
Werner A. Stahel
smoothpar(50) t.n <- c(5,10,20,100,1000) smoothpar(t.n)smoothpar(50) t.n <- c(5,10,20,100,1000) smoothpar(t.n)
These functions wrap the loess smoothing function or
the lm.fit function in order to meet the argument conventions
used in the plgraphics package.
smoothRegr(x, y, weights = NULL, par = NULL, iterations = 50, minobs=NULL, ...) smoothLm(x, y, weights = NULL, ...)smoothRegr(x, y, weights = NULL, par = NULL, iterations = 50, minobs=NULL, ...) smoothLm(x, y, weights = NULL, ...)
x |
vector of x values |
y |
vector of y values to be smoothed |
weights |
vector of weigths used for fitting the smooth |
par |
value for the |
iterations |
number of iterations for the |
minobs |
minimal number of observations. If less valid observations
are provided, the result is |
... |
Further arguments, passed to |
vector of smoothed values, with an attribute xtrim,
which is 1 for smoothRegr and 0 for smoothLm.
If loess fails, NAs will be returned without issuing a
warning.
Werner A. Stahel, ETH Zurich
t.x <- (1:50)^1.5 t.y <- log10(t.x) + rnorm(length(t.x),0,0.3) t.y[40] <- 5 r.sm <- smoothRegr(t.x, t.y, par=0.5) r.sm1 <- smoothRegr(t.x, t.y, iterations=1, par=0.5) plot(t.x,t.y) lines(t.x,r.sm, col=2) lines(t.x,r.sm1, col=3)t.x <- (1:50)^1.5 t.y <- log10(t.x) + rnorm(length(t.x),0,0.3) t.y[40] <- 5 r.sm <- smoothRegr(t.x, t.y, par=0.5) r.sm1 <- smoothRegr(t.x, t.y, iterations=1, par=0.5) plot(t.x,t.y) lines(t.x,r.sm, col=2) lines(t.x,r.sm1, col=3)
The range in which smooth lines are drawn should be restricted in order to avoid the ill determined parts at both ends. The proportion of suppressed values is determined as a function of the number of observations.
smoothxtrim(n, c=2)smoothxtrim(n, c=2)
n |
number of observations |
c |
tuning parameter: how rapidly should the result decrease
with |
proportion of x values for which the smoothline will not be shown on both ends. Equals \ 1.6^(log10(n)*c) / n
W. Stahel
smoothxtrim(50) t.n <- c(5,10,20,100,1000) t.n * smoothxtrim(t.n)smoothxtrim(50) t.n <- c(5,10,20,100,1000) t.n * smoothxtrim(t.n)
A line is added to the current plot in the lower right corner that contains project information and date.
stamp(sure = TRUE, outer.margin = NULL, project = getOption("project"), step = getOption("step"), stamp = NULL, line = NULL, ploptions = NULL, ...)stamp(sure = TRUE, outer.margin = NULL, project = getOption("project"), step = getOption("step"), stamp = NULL, line = NULL, ploptions = NULL, ...)
sure |
if FALSE, the stamp will only be added if
|
outer.margin |
if TRUE, the stamp is put to the outer margin of the plot. This is the default if the plot is currently split into panels. |
project, step
|
character string describing the project and the step of analysis. |
stamp |
controls default action, see details |
line |
line in the (outer) margin on which the stamp should be shown. |
ploptions |
pl options |
... |
arguments passed to |
The function is used to document plots produced during a data
analysis. It is called by all plotting functions of this package.
For getting final presentation versions of the plots, the stamp can be
suppressed by changing the default by calling options(stamp=0).
In more detail: If stamp==0
(or options("stamp")==0)
the function will only do its thing if sure==TRUE.
If stamp==2, it will certainly do it.
If stamp==1 and sure==FALSE, the stamp is added when a
plot page is complete.
invisibly returns the string that is added to the plot – consisting of project title, step title and current date (including time).
Werner A. Stahel, ETH Zurich
options(project="Example A", step="regression analysis") plot(1:10) stamp() ##-> "stamp" at bottom of right borderoptions(project="Example A", step="regression analysis") plot(1:10) stamp() ##-> "stamp" at bottom of right border
Calculates standardized residuals and leverage values.
stdresiduals(x, residuals=NULL, sigma=x$sigma, weights=NULL, leveragelimit = NULL)stdresiduals(x, residuals=NULL, sigma=x$sigma, weights=NULL, leveragelimit = NULL)
x |
a fitted model object |
residuals |
unstandardized residuals. If missing, they are
obtained from |
sigma |
error standard deviation or other scale |
weights |
weights |
leveragelimit |
scalar a little smaller than 1: limit on leverage values to avoid unduely large or infinite standardized residuals |
The difference to stdres() from package MASS
is that stdresiduals also applies to multivariate regression
and can be used with regression model fits not inheriting from lm.
The function uses the qr decomposition of object.
If necessary, it generates it.
vector or matrix of standardized residuals,
with attributesattr(.,"stdresratio"):
ratio of standardized / unstandardized residuals,attr(.,"leverage"): leverage (hat) values,attr(.,"weighted"): weights used in the standardization,attr(.,"stddev"): error standard deviation or scale parameter.
Werner A. Stahel, ETH Zurich
stdres; hat; hatvalues; influence
data(d.blast) r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) t.stdr <- stdresiduals(r.blast) showd(t.stdr) showd(attr(t.stdr, "leverage"))data(d.blast) r.blast <- lm(log10(tremor)~location+log10(distance)+log10(charge), data=d.blast) t.stdr <- stdresiduals(r.blast) showd(t.stdr) showd(attr(t.stdr, "leverage"))
Count the missing or non-finite values for each column of a matrix or data.frame
sumNA(object, inf = TRUE)sumNA(object, inf = TRUE)
object |
a vector, matrix, or data.frame |
inf |
if TRUE, Inf and NaN values are counted along with NAs |
numerical vector containing the missing value counts for each column
This is a simple shortcut for apply(is.na(object),2,sum)
or apply(!is.finite(object),2,sum)
Werner A. Stahel, ETH Zurich
t.d <- data.frame(V1=c(1,2,NA,4), V2=c(11,12,13,Inf), V3=c(21,NA,23,Inf)) sumNA(t.d)t.d <- data.frame(V1=c(1,2,NA,4), V2=c(11,12,13,Inf), V3=c(21,NA,23,Inf)) sumNA(t.d)
Returns a Surv object that allows for setting up a Tobit
regression model by calling survreg
Tobit(data, limit = 0, limhigh = NULL, transform = NULL, log = FALSE, ...)Tobit(data, limit = 0, limhigh = NULL, transform = NULL, log = FALSE, ...)
data |
the variable to be used as the response in the Tobit regression |
limit |
Lower limit which censors the observations.
If |
limhigh |
Upper limit which censors the observations (for untransformed data). |
transform |
if data should be transformed, specify the function to be used. |
log |
logical. If |
... |
any additional arguments to the |
Tobit regression is a special case of regression with left censored
response data. The function survreg is suitable for fitting.
In regr, this is done automatically.
A Surv object.
Werner A. Stahel
if(requireNamespace("survival")) { data("tobin", package="survival") Tobit(tobin$durable) (t.r <- survival::survreg(Tobit(durable) ~ age + quant, data = tobin, dist="gaussian")) if(interactive()) plregr(t.r) }if(requireNamespace("survival")) { data("tobin", package="survival") Tobit(tobin$durable) (t.r <- survival::survreg(Tobit(durable) ~ age + quant, data = tobin, dist="gaussian")) if(interactive()) plregr(t.r) }
Attach the attributes of an object to another object
transferAttributes(x, xbefore, except = NULL)transferAttributes(x, xbefore, except = NULL)
x |
the object to which the attributes should be transferred |
xbefore |
the object which delvers the attributes |
except |
names of attributes that will not be transferred |
Object x with attributes from xbefore (and possibly
some that it already had)
This function would not be needed if structure
allowed for a list of attributes.
W. A. Stahel
a <- structure(1:10, title="sequence") transferAttributes(31:40, a)a <- structure(1:10, title="sequence") transferAttributes(31:40, a)
Gives a List of Warnings
warn()warn()
This function simplyfies the output of warnings if there
are several identical warnings, by counting their occurence
the table of warnings
Werner A. Stahel, ETH Zurich
for (i in 3:6) m <- matrix(1:7, 3, i) suppressWarnings( ## or set options(warn=-1) for (i in 3:6) m <- matrix(1:7, 3, i)) warn()for (i in 3:6) m <- matrix(1:7, 3, i) suppressWarnings( ## or set options(warn=-1) for (i in 3:6) m <- matrix(1:7, 3, i)) warn()
From Dates, obtain the day of the week or the year, month and day
weekday(date, month = NULL, day = NULL, out = NULL, factor = FALSE) ymd(date)weekday(date, month = NULL, day = NULL, out = NULL, factor = FALSE) ymd(date)
date |
date(s), given as a |
month, day
|
If the first argument is the year, these arguments must also be given. |
factor |
logical: Should the result be a (ordered) factor? |
out |
selection of output: either
|
For weekdays,
the output is as described above, depending on
factor and out.
The functions call functions from the chron
package
Werner A. Stahel
weekday(c("2020-05-01", "2020-05-02"), factor=TRUE) ## [1] Thursday Sunday ## Levels: Sunday < Monday < Tuesday < Wednesday < Thursday < Friday < Saturday dt <- ymd(18100+1:5) weekday(dt) ## [1] 3 4 5 6 0weekday(c("2020-05-01", "2020-05-02"), factor=TRUE) ## [1] Thursday Sunday ## Levels: Sunday < Monday < Tuesday < Wednesday < Thursday < Friday < Saturday dt <- ymd(18100+1:5) weekday(dt) ## [1] 3 4 5 6 0
A test for the completeness of a linear regression model can be performed based on comparing the differences of residuals for pairs of observations that are close to each other to the estimated standard deviation of the model.
xdistResdiff(object, perc = c(3, 10, 80), trim = 0.1, nmax = 100, out = "aggregate") xdistResscale(x, perc = c(3, 10, 90), trim = 1/6)xdistResdiff(object, perc = c(3, 10, 80), trim = 0.1, nmax = 100, out = "aggregate") xdistResscale(x, perc = c(3, 10, 90), trim = 1/6)
object |
an object containing the result of fitting a linear
model by |
x |
an object produced by |
perc |
Percentage points to define distance classes |
trim |
Trimming proportion for calculating means of absolute residual differences |
nmax |
maximal number of observations to form pairs |
out |
determines the value of |
See package vignette.
For xdistResdiff with out="aggregate" and
xdistResscale, a matrix is returned with a row for
each class of x distances and the columns
xdist |
mean x distance |
rdiff.mean |
absolute differences of residuals for pairs of observations in the distance class, averaged over the class |
rdiff.simmean |
mean of (trimmed) means for simulated data |
rdiff.se |
standard error of (trimmed) means as obtained from simulation |
The matrix carries along the following attributes:
perc |
given argument |
xd.classlim |
the actual class limits corresponding to
|
trim |
given argument |
rdiff.grandmean |
overall mean of absolute residual differences |
p-values |
p values for the classes as obtained from simulation, and p-value for the sum of squares statistic |
class |
The value has S3 class |
.
If xdistResdiff with out different from
"aggregate", then a data.frame is returned containing a row for
each pair of observations and the columns
id1, id2
|
the labels of the two observations |
xdist |
the x distance between the two observations |
resdiff |
the difference of residuals for the two observations |
The value has S3 class xdistResdiff and data.frame.
Werner A. Stahel, ETH Zurich
See package vignette.
data(d.blast) rr <- lm(tremor~distance+charge, data=d.blast) ## an inadequate model! xdrs <- xdistResdiff(rr) xdrd <- xdistResdiff(rr, out="all") showd(xdrd) xdrs <- xdistResscale(xdrd) ## same as first call of xdiffResdiff plot(xdrs)data(d.blast) rr <- lm(tremor~distance+charge, data=d.blast) ## an inadequate model! xdrs <- xdistResdiff(rr) xdrd <- xdistResdiff(rr, out="all") showd(xdrd) xdrs <- xdistResscale(xdrd) ## same as first call of xdiffResdiff plot(xdrs)