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termplot: Plot Regression Terms

termplot

R Documentation

Plot Regression Terms

Description

Plots regression terms against their predictors, optionally with standard errors and partial residuals added.

Usage

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",
         plot = TRUE, transform.x = FALSE, ...)

Arguments

`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); a vector passed to `predict(.., type = "terms", 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`
`ylim`	an optional range for the y axis, or `"common"` when a range sufficient for all the plot will be computed, or `"free"` when limits are computed for each plot.
`plot`	if set to `FALSE` plots are not produced: instead a list is returned containing the data that would have been plotted.
`transform.x`	logical vector; if an element (recycled as necessary) is `TRUE`, partial residuals for the corresponding term are plotted against transformed values. The model response is then a straight line, allowing a ready comparison against the data or against the curve obtained from `smooth-panel.smooth`.
`...`	other graphical parameters.

Details

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 plot = FALSE option is useful when some special action is needed, e.g. to overlay the results of two different models or to plot confidence bands.

Value

For plot = FALSE, a list with one element for each plot which would have been produced. Each element of the list is a data frame with variables x, y, and optionally the pointwise standard errors se. For continuous predictors x will contain the ordered unique values and for a factor it will be a factor containing one instance of each level. The list has attribute "constant" copied from the predicted terms object.

Otherwise, the number of terms, invisibly.

Examples

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")

if(requireNamespace("MASS", quietly = TRUE)) {
hills.lm <- lm(log(time) ~ log(climb)+log(dist), data = MASS::hills)
termplot(hills.lm, partial.resid = TRUE, smooth = panel.smooth,
        terms = "log(dist)", main = "Original")
termplot(hills.lm, transform.x = TRUE,
         partial.resid = TRUE, smooth = panel.smooth,
	 terms = "log(dist)", main = "Transformed")

}