termplot {stats} | R Documentation |
Plots regression terms against their predictors, optionally with standard errors and partial residuals added.
termplot(model, data = NULL, envir = environment(formula(model)),
partial.resid = FALSE, rug = FALSE,
terms = NULL, se = FALSE,
xlabs = NULL, ylabs = NULL, main = NULL,
col.term = 2, lwd.term = 1.5,
col.se = "orange", lty.se = 2, lwd.se = 1,
col.res = "gray", cex = 1, pch = par("pch"),
col.smth = "darkred", lty.smth = 2, span.smth = 2/3,
ask = dev.interactive() && nb.fig < n.tms,
use.factor.levels = TRUE, smooth = NULL, ylim = "common",
...)
model |
fitted model object |
data |
data frame in which variables in |
envir |
environment in which variables in |
partial.resid |
logical; should partial residuals be plotted? |
rug |
add rugplots (jittered 1-d histograms) to the axes? |
terms |
which terms to plot (default |
se |
plot pointwise standard errors? |
xlabs |
vector of labels for the x axes |
ylabs |
vector of labels for the y axes |
main |
logical, or vector of main titles; if |
col.term , lwd.term |
color and line width for the ‘term curve’,
see |
col.se , lty.se , lwd.se |
color, line type and line width for the
‘twice-standard-error curve’ when |
col.res , cex , pch |
color, plotting character expansion and type
for partial residuals, when |
ask |
logical; if |
use.factor.levels |
Should x-axis ticks use factor levels or numbers for factor terms? |
smooth |
|
lty.smth , col.smth , span.smth |
Passed to |
ylim |
an optional range for the y axis, or |
... |
other graphical parameters. |
The model object must have a predict
method that accepts
type=terms
, eg glm
in the base package,
coxph
and survreg
in
the survival package.
For the partial.resid=TRUE
option it must have a
residuals
method that accepts type="partial"
,
which lm
and glm
do.
The data
argument should rarely be needed, but in some cases
termplot
may be unable to reconstruct the original data
frame. Using na.action=na.exclude
makes these problems less likely.
Nothing sensible happens for interaction terms.
For (generalized) linear models, plot.lm
and
predict.glm
.
require(graphics)
had.splines <- "package:splines" %in% search()
if(!had.splines) rs <- require(splines)
x <- 1:100
z <- factor(rep(LETTERS[1:4],25))
y <- rnorm(100, sin(x/10)+as.numeric(z))
model <- glm(y ~ ns(x,6) + z)
par(mfrow=c(2,2)) ## 2 x 2 plots for same model :
termplot(model, main = paste("termplot( ", deparse(model$call)," ...)"))
termplot(model, rug=TRUE)
termplot(model, partial.resid=TRUE, se = TRUE, main = TRUE)
termplot(model, partial.resid=TRUE, smooth=panel.smooth, span.smth=1/4)
if(!had.splines && rs) detach("package:splines")