plregr.control: Further Arguments to `plregr`

Description

Specify some arguments of minor importance for the function plregr

Usage

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, ...)

Value

A list containing all the items needed to specify plotting in plregr and plresx

Arguments

x: an object (result of a call to a model fitting function such as lm, glm, .... This is the only argument that is needed. All others have useful defaults.
data: see ?plregr
xvar: variables for which residuals shall be plotted. Either a formula like ~ x1 + x2 or a character vector of names. Defaults to all variables (or terms, see transformed) in the model.
transformed: see ?plregr
weights: logical: should residuals be plotted against weights? Used in plresx.
stdresid: logical: should leverages and standardized residuals be calculated? This is avoided for plresx
mar: plot margins
glm.restype: type of residuals to be used for glm models. In addition to those allowed in residuals() for glm objects, type condquant is possible for (ungrouped) binary regression. See ?residuals.regrpolr for an explanation. Warning: type "deviance" will not work with simulated smooths since NAs will emerge.
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 smresid is TRUE).
condprob.range: numeric vector of length 2. In the case of residuals of class condquant, quartile bars are only drawn for residuals with probability between condprob.range[1] and condprob.range[1]. Default is c(0.05,0.8) for less than 50 observations, and c(0,0), suppressing the bars, otherwise.
testlevel: level for statistical tests
refline: logical: should reference line be shown? If refline==2, a confidence band be drawn for the component effects
smooth: if TRUE (or 1), smooths are added to the plots where appropriate. If ==2, smmooths to positive and negative residuals-from-smooth are also shown.
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 smooth.group is set).
xlabs: labels for x variables. Defaults to vars
reslabs: labels for vertical axes
markextremes: proportion of extreme residuals to be labeled. If all points should be labeled, let markextremes=1.
mf: vector of 2 elements, indicating the number of rows and columns of panels on each plot page. Defaults to c(2,2), except for multivariate models, where it adjusts to the number of target variables. mf=c(1,1) or mf=1 asks for a single frame per page. mf=NA or mf=0 leaves the framing (and oma) unchanged.
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 pl.control be assigned to the pl.envir environment? This will be done for high level pl functions, but avoided for low level ones. It allows for reusing the settings and helps debug unexpected behavior.
...: further arguments in the call, to be ignored by 'plotregr.control'

Author

Werner A. Stahel, Seminar for Statistics, ETH Zurich

Examples

Run this code

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)