termplot

From stats v3.2.5 by R-core R-core@R-project.org

Plot Regression Terms

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

Keywords: hplot, regression

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 \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.

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

library(stats)

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")

Documentation reproduced from package stats, version 3.2.5, License: Part of R 3.2.5

Community examples

Looks like there are no examples yet.