| 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, e.g., glm in the stats package,
coxph and survreg in
the survival package.
For the partial.resid = TRUE option model 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, and they may cause errors.
The number of terms, invisibly.
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")