stats (version 3.3.1)

termplot: 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.

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.

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 \href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}survivalsurvival 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.\ifelse{latex}{\out{~}}{ } to overlay the results of two different models or to plot confidence bands.

See Also

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

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")

## requires recommended package MASS
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")