termplot {stats}R Documentation

Plot regression terms


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 = interactive() && nb.fig < n.tms &&
               .Device != "postscript",
         use.factor.levels = TRUE, smooth = NULL, ...)


model fitted model object
data data frame in which variables in model can be found
envir environment in which variables in model can be found
partial.resid logical; should partial residuals be plotted?
rug add rugplots (jittered 1-d histograms) to the axes?
terms which terms to plot (default NULL means all terms)
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 TRUE, the model's call is taken as main title, NULL or FALSE mean no titles.
col.term, lwd.term color and line width for the “term curve”, see lines.
col.se, lty.se, lwd.se color, line type and line width for the “twice-standard-error curve” when se = TRUE.
col.res, cex, pch color, plotting character expansion and type for partial residuals, when partial.resid = TRUE, see points.
ask logical; if TRUE, the user is asked before each plot, see par(ask=.).
use.factor.levels Should x-axis ticks use factor levels or numbers for factor terms?
smooth NULL or a function with the same arguments as panel.smooth to draw a smooth through the partial residuals for non-factor terms
lty.smth,col.smth, span.smth Passed to smooth
... 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.

See Also

For (generalized) linear models, plot.lm and predict.glm.


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=TRUE, se = TRUE, main = TRUE)
termplot(model, partial=TRUE, smooth=panel.smooth,span.smth=1/4)
if(!had.splines && rs) detach("package:splines")

[Package stats version 2.5.0 Index]