summary.eff {effects} | R Documentation |
summary
, print
, plot
, and [
methods for eff
, effpoly
,
and efflist
objects.
## S3 method for class 'eff' print(x, type=c("response", "link"), ...) ## S3 method for class 'effpoly' print(x, type=c("probability", "logits"), ...) ## S3 method for class 'efflatent' print(x, ...) ## S3 method for class 'efflist' 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 'effpoly' summary(object, type=c("probability", "logits"), ...) ## S3 method for class 'efflatent' summary(object, ...) ## S3 method for class 'efflist' summary(object, ...) ## S3 method for class 'eff' plot(x, x.var = which.max(levels), z.var = which.min(levels), multiline = is.null(x$se), rug = TRUE, xlab, ylab, main = paste(effect, "effect plot"), colors = palette(), symbols = 1:10, lines = 1:10, cex = 1.5, ylim, factor.names = TRUE, type = c("response", "link"), ticks = list(at = NULL, n = 5), alternating = TRUE, rotx = 0, roty = 0, grid = FALSE, layout, rescale.axis = TRUE, key.args = NULL, row = 1, col = 1, nrow = 1, ncol = 1, more = FALSE, ...) ## S3 method for class 'effpoly' plot(x, type = c("probability", "logit"), x.var = which.max(levels), rug = TRUE, xlab, ylab = paste(x$response, " (", type, ")", sep = ""), main = paste(effect, "effect plot"), colors, symbols = 1:10, lines = 1:10, cex = 1.5, factor.names = TRUE, style = c("lines", "stacked"), confint = (style == "lines" && !is.null(x$confidence.level)), ylim, rotx = 0, alternating = TRUE, roty = 0, grid = FALSE, layout, key.args = NULL, row = 1, col = 1, nrow = 1, ncol = 1, more = FALSE, ...) ## S3 method for class 'efflist' plot(x, selection, rows, cols, ask=TRUE, graphics=TRUE, ...) ## S3 method for class 'efflist' x[...]
x |
an object of class |
object |
an object of class |
type |
for linear and generalized linear models,
if |
x.var |
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. |
z.var |
for linear, generalized linear or mixed models,
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 if |
multiline |
for linear, generalized linear or mixed models,
if |
confint |
plot point-wise confidence bands around fitted effects (for
multinomial and proportional-odds logit models); defaults to |
rug |
if |
xlab |
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. |
ylab |
the label for the vertical axis of the effect plot; the default is constructed from the name of the response variable for the model from which the effect was computed. |
main |
the title for the plot, printed at the top; the default title is constructed from the name of the effect. |
colors |
|
symbols, lines |
corresponding to the levels of the |
cex |
character expansion for plotted symbols; default is |
ylim |
2-element vector containing the lower and upper limits of the vertical axes;
if |
factor.names |
a logical value, default |
style |
(for multinomial or proportional-odds logit models) |
ticks |
a two-item list controlling the placement of tick marks on the vertical axis,
with elements |
alternating |
if |
rotx, roty |
rotation angles for the horizontal and vertical tick marks, respectively. Default is 0. |
grid |
if |
layout |
the |
rescale.axis |
if |
key.args |
additional arguments to be passed to the |
row, col, nrow, ncol, more |
These arguments are used to graph an effect as part of an
array of plots; |
selection |
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. |
rows, cols |
Number of rows and columns in the “meta-array” of plots produced for an |
ask |
if |
graphics |
if |
... |
arguments to be passed down. |
In a generalized linear model, by default, the print
and summary
methods for
eff
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
scale.
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.
In a polytomous (multinomial or proportional-odds) logit model, by default effects are plotted on the probability scale; they may be alternatively plotted on the scale of the individual-level logits.
The summary
method for "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. |
The [
method for "efflist"
objects is used to subset an "efflist"
object and returns an object of the same class.
John Fox jfox@mcmaster.ca and Jangman Hong.
effect
, allEffects
, xyplot
,
densityplot
, print.trellis
rainbow_hcl
, sequential_hcl
mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial) eff.cowles <- allEffects(mod.cowles, xlevels=list(neuroticism=0:24, extraversion=seq(0, 24, 6))) eff.cowles plot(eff.cowles, 'sex', ylab="Prob(Volunteer)", grid=TRUE, rotx=90) 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)", key.args = list(x = 0.75, y = 0.75, corner = c(0, 0))) plot(effect('sex:neuroticism:extraversion', mod.cowles, xlevels=list(neuroticism=0:24, extraversion=seq(0, 24, 6))), multiline=TRUE) mod.beps <- multinom(vote ~ age + gender + economic.cond.national + economic.cond.household + Blair + Hague + Kennedy + Europe*political.knowledge, data=BEPS) plot(effect("Europe*political.knowledge", mod.beps, xlevels=list(Europe=1:11, political.knowledge=0:3))) plot(effect("Europe*political.knowledge", mod.beps, xlevels=list(Europe=1:11, political.knowledge=0:3), given.values=c(gendermale=0.5)), style="stacked", colors=c("blue", "red", "orange"), rug=FALSE) mod.wvs <- polr(poverty ~ gender + religion + degree + country*poly(age,3), data=WVS) plot(effect("country*poly(age, 3)", mod.wvs)) plot(effect("country*poly(age, 3)", mod.wvs), style="stacked", colors=c("gray75", "gray50", "gray25")) plot(effect("country*poly(age, 3)", latent=TRUE, mod.wvs)) mod.pres <- lm(prestige ~ log(income, 10) + poly(education, 3) + poly(women, 2), data=Prestige) eff.pres <- allEffects(mod.pres, default.levels=50) plot(eff.pres, ask=FALSE) plot(eff.pres[1:2], ask=FALSE)