residual.plots {HH} | R Documentation |
Residual plots for a linear model. Four sets of plots are produced: (1) response against each of the predictor variables, (2) residuals against each of the predictor variables, (3) partial residuals for each predictor against that predictor ("partial residuals plots", and (4) partial residuals against the residuals of each predictor regressed on the other predictors ("added variable plots").
residual.plots(lm.object, X=dft$x, layout=c(dim(X)[2],1), par.strip.text=list(cex=.8), scales.cex=.6, na.action=na.pass, y.relation="free", ...)
lm.object |
An object inheriting from "lm" .
It may be necessary for the lm.object to be constructed with
arguments x=TRUE, y=TRUE . |
X |
The x matrix of predictor variables used in the linear model
lm.object . |
layout, par.strip.text |
trellis or lattice arguments. |
scales.cex |
cex argument forwarded to the scales argument
of xyplot . |
na.action |
A function to filter missing data. See lm . |
y.relation |
See relation in the discussion of the
scales argument in trellis.args in S-Plus and
in xyplot in R. |
... |
Other arguments for xysplom or xyplot . |
A list of four trellis objects, one for each of the four sets of
plots. The objects are named "y.X"
, "res.X"
"pres.X"
, "pres.Xj"
. The default "printing" of the
result will produce four pages of plots, one set per page. They are
often easier to read when all four sets appear as separate rows on one
page (this usually requires an oversize device), or two rows are
printed on each of two pages.
Richard M. Heiberger <rmh@temple.edu>
Heiberger, Richard M. and Holland, Burt (2004b). Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer. ISBN 0-387-40270-5.
if.R(s={ longley <- data.frame(longley.x, Employed = longley.y) },r={ data(longley) }) longley.lm <- lm( Employed ~ . , data=longley, x=TRUE, y=TRUE) ## 'x=TRUE, y=TRUE' are needed to pass the S-Plus CMD check. ## They may be needed if residual.plots() is inside a nested set of ## function calls. tmp <- residual.plots(longley.lm) ## print two rows per page print(tmp[[1]], position=c(0, 0.5, 1, 1.0), more=TRUE) print(tmp[[2]], position=c(0, 0.0, 1, 0.5), more=FALSE) print(tmp[[3]], position=c(0, 0.5, 1, 1.0), more=TRUE) print(tmp[[4]], position=c(0, 0.0, 1, 0.5), more=FALSE)