Summarizing, Printing, and Plotting Effects
plot methods for
## S3 method for class 'eff': print(x, type=c("response", "link"), ...) ## S3 method for class 'eff.list': print(x, ...) ## S3 method for class 'summary.eff': print(x, ...) ## S3 method for class 'eff': summary(object, type=c("response", "link"), ...) ## S3 method for class 'eff.list': summary(object, ...) ## S3 method for class 'eff': plot(x, x.var=which.max(levels), z.var=which.min(levels), multiline=is.null(x$se), rug=TRUE, xlab, ylab=x$response, main=paste(effect, "effect plot"), colors=palette(), symbols=1:10, lines=1:10, cex=1.5, ylim, factor.names=TRUE, type=c("response", "link"), ticks=list(at=NULL, n=5), alternating=TRUE, rescale.axis=TRUE, row=1, col=1, nrow=1, ncol=1, more=FALSE, ...) ## S3 method for class 'eff.list': plot(x, selection, ask=TRUE, ...)
- an object of type
"effect.list", as appropriate.
- an object of type
"effect.list", as appropriate.
"response"(the default), effects are printed or the vertical axis is labelled on the scale of the response variable; if
"link", effects are printed or the vertical axis labelled on the scale of the linear pre
- the index (number) or quoted name of the covariate or factor to place on the horizontal axis of each panel of the effect plot. The default is the predictor with the largest number of levels or values.
- the index (number) or quoted name of the covariate or factor for which individual lines are to be drawn in each panel of the effect plot. The default is the predictor with the smallest number of levels or values. This argument is only used
TRUE, each panel of the display represents combinations of values of two predictors, with one predictor (corresponding to
x.var) on the horzontal axis, and the other (corresponding to
z.var) used to defi
TRUE, the default, a rug plot is shown giving the marginal distribution of the predictor on the horizontal axis, if this predictor is a covariate.
- the label for the horizontal axis of the effect plot; if missing, the function will use the name of the predictor on the horizontal axis.
- the label for the vertical axis of the effect plot; the default is the response variable for the model from which the effect was computed.
- the title for the plot, printed at the top; the default title is constructed from the name of the effect.
colorsis used to plot effects,
colorsto plot confidence bands. In a mulitline plot, the successive
colorscorrespond to the levels of the
z.varcovariate or factor.
- symbols, lines
- corresponding to the levels of the
z.varcovariate or factor on a multiline plot. These arguments are used only if
multiline = TRUE; in this case a legend is drawn at the top of the display.
- character expansion for plotted symbols; default is
- 2-element vector containing the lower and upper limits of the vertical axes;
NULL, the default, then the vertical axes are scaled from the data.
- a logical value, default
TRUE, that controls the inclusion of factor names in conditioning-variable labels.
- a two-item list controlling the placement of tick marks on the vertical axis,
at=NULL(the default), the program attempts to find `nice' locations for the ticks, and the value of
TRUE(the default), the tick labels alternate by panels in multi-panel displays from left to right and top to bottom; if
FALSE, tick labels appear at the bottom and on the left.
TRUE(the default), the tick marks on the vertical axis are labelled on the response scale (e.g., the probability scale for effects computed on the logit scale for a binomial GLM).
- row, col, nrow, ncol, more
- These arguments are used to graph an effect as part of an
array of plots;
ncolare used to compose the
- the optional index (number) or quoted name of the effect in an effect list to be plotted; if not supplied, a menu of high-order terms is presented or all effects are plotted.
selectionis not supplied and
TRUE(the default), a menu of high-order terms is presented; if
FALSE, effects for all high-order terms are plotted in an array.
- arguments to be passed down.
In a generalized linear model, by default, the
summary methods for
effect objects print the computed effects on the scale of the
response variable using the inverse of the
link function. In a logit model, for example, this means that the effects are expressed on the probability
By default, effects in a GLM are plotted on the scale of the linear predictor, but the vertical
axis is labelled on the response scale. This preserves the linear structure of the model while permitting
interpretation on what is usually a more familiar scale.
This approach may also be used with linear models, for example to display effects on the scale of the
response even if the data are analyzed on a transformed scale, such as log or square-root.
"eff"objects returns a
"summary.eff"object with the following components (those pertaining to confidence limits need not be present):
header a character string to label the effect. effect an array containing the estimated effect. lower.header a character string to label the lower confidence limits. lower an array containing the lower confidence limits. upper.header a character string to label the upper confidence limits. upper an array containing the upper confidence limits.
mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial) eff.cowles <- all.effects(mod.cowles, xlevels=list(neuroticism=0:24, extraversion=seq(0, 24, 6))) eff.cowles model: volunteer ~ sex + neuroticism * extraversion sex effect sex female male 0.4409441 0.3811941 neuroticism*extraversion effect extraversion neuroticism 0 6 12 18 24 0 0.07801066 0.1871263 0.3851143 0.6301824 0.8225756 1 0.08636001 0.1963396 0.3870453 0.6200668 0.8083638 2 0.09551039 0.2058918 0.3889798 0.6098458 0.7932997 3 0.10551835 0.2157839 0.3909179 0.5995275 0.7773775 . . . 23 0.51953129 0.4747277 0.4303273 0.3870199 0.3454282 24 0.54709527 0.4895731 0.4323256 0.3768303 0.3243880 plot(eff.cowles, 'sex', ylab="Prob(Volunteer)") Loading required package: lattice plot(eff.cowles, 'neuroticism:extraversion', ylab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9))) plot(eff.cowles, 'neuroticism:extraversion', multiline=TRUE, ylab="Prob(Volunteer)") plot(effect('sex:neuroticism:extraversion', mod.cowles, xlevels=list(neuroticism=0:24, extraversion=seq(0, 24, 6))), multiline=TRUE) Warning message: sex:neuroticism:extraversion does not appear in the model in: effect("sex:neuroticism:extraversion", mod.cowles, xlevels = list(neuroticism = 0:24, mod.pres <- lm(prestige ~ log(income, 10) + poly(education, 3) + poly(women, 2), data=Prestige) eff.pres <- all.effects(mod.pres, default.levels=50) plot(eff.pres, ask=FALSE)