plot.effects  R Documentation 
plot
methods for predictoreff
, predictorefflist
, eff
, efflist
and effpoly
objects created by calls other methods in the effects
package. The plot arguments were substantially changed in mid2017. For more details and many examples, see the Predictor Effects Graphics Gallery vignette.
## S3 method for class 'eff' plot(x, x.var, main=paste(effect, "effect plot"), symbols=TRUE, lines=TRUE, axes, confint, partial.residuals, id, lattice, ..., # legacy arguments: multiline, z.var, rug, xlab, ylab, colors, cex, lty, lwd, ylim, xlim, factor.names, ci.style, band.transparency, band.colors, type, ticks, alternating, rotx, roty, grid, layout, rescale.axis, transform.x, ticks.x, show.strip.values, key.args, use.splines, residuals.color, residuals.pch, residuals.cex, smooth.residuals, residuals.smooth.color, show.fitted, span) ## S3 method for class 'efflist' plot(x, selection, rows, cols, ask=FALSE, graphics=TRUE, lattice, ...) ## S3 method for class 'predictoreff' plot(x, x.var, main = paste(names(x$variables)[1], "predictor effect plot"), ...) ## S3 method for class 'predictorefflist' plot(x, selection, rows, cols, ask = FALSE, graphics = TRUE, lattice, ...) ## S3 method for class 'effpoly' plot(x, x.var=which.max(levels), main=paste(effect, "effect plot"), symbols=TRUE, lines=TRUE, axes, confint, lattice, ..., # legacy arguments: type, multiline, rug, xlab, ylab, colors, cex, lty, lwd, factor.names, show.strip.values, ci.style, band.colors, band.transparency, style, transform.x, ticks.x, xlim, ticks, ylim, rotx, roty, alternating, grid, layout, key.args, use.splines) ## S3 method for class 'mlm.efflist' plot(x, ...)
x 
an object of class 
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. This argument is ignored with 
main 
the title for the plot, printed at the top; the default title is constructed from the name of the effect. 
symbols 

lines 

axes 
an optional list of specifications for the x and y axes; if not given, axis properties take generally reasonable default values. See Details for more information. 
confint 
an optional list of specifications for plotting confidence regions and intervals; if not given, generally reasonable default values are used. See Detailed Argument Descriptions under Details for more information. 
partial.residuals 
an optional list of specifications for plotting partial residuals for linear and generalized linear models; if not given, generally reasonable default values are used. See Detailed Argument Descriptions under Details for more information, along with the Effect Displays with Partial Residuals vignette. 
id 
an optional list of specifications for identifying points when partial residuals are plotted; if not specified, no points are labelled. See Detailed Argument Descriptions under Details for more information. 
lattice 
an optional list of specifications for various lattice properties, such as legend placement; if not given, generally reasonable default values are used. See Detailed Argument Descriptions under Details for more information. 
selection 
the optional index (number) or quoted name of the effect in an efflist object to be plotted; if not supplied, a menu of highorder terms is presented or all effects are plotted. 
rows, cols 
Number of rows and columns in the “metaarray” of plots produced for an 
ask 
if 
graphics 
if 
... 
arguments to be passed down. For 
multiline, z.var, rug, xlab, ylab, colors, cex, lty, lwd,
ylim, xlim, factor.names, ci.style,
band.transparency, band.colors, ticks,
alternating, rotx, roty, grid, layout,
rescale.axis, transform.x, ticks.x, show.strip.values,
key.args, use.splines, type,
residuals.color, residuals.pch, residuals.cex, smooth.residuals,
residuals.smooth.color, show.fitted, span, style 
legacy arguments retained for backwards compatibility; if specified, these will take precedence over the newer liststyle arguments described above. See 
Effects plots and predictor effects plots are produced by these methods. The plots are highly customizable using the optional arguments described here. For example, 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 squareroot. See the axes
argument details below to change the scale to response scale, or to linear predictor scale with tick marks labeled in response scale.
When a factor is on the xaxis, the plot
method for eff
objects connects the points representing the effect by line segments, creating a response “profile.” If you wish to suppress these lines, add lty=0
to the lines
argument to the call to plot
(see below and the examples).
In a polytomous multinomial or proportionalodds logit model, by default effects are plotted on the probability scale; they may alternatively be plotted on the scale of the individuallevel logits.
All of the arguments to plot objects created by Effect
or allEffects
can also be used with objects created by predictorEffect
or predictorEffects
.
Detailed Argument Descriptions
For more information about these arguments and many examples, see the Predictor Effects Graphics Gallery vignette.
Maximizing the flexibility of these plot commands requires inclusion of a myriad of options. In an attempt to simplify the use of these options, they have been organized into just a few arguments that each accept a list of specifications as an argument. In a few cases the named entries in the list are themselves lists.
Each of the following arguments takes an optional list of specifications; any specification absent from the list
assumes its default value. Some of the list elements are themselves lists, so in complex cases, the argument can take
the form of nested lists. All of these arguments can also be used on objects created with predictorEffects
.
symbols
TRUE
, FALSE
, or a list of options that controls the plotting symbols and their sizes for use with factors;
if FALSE
symbols are suppressed; if TRUE
default values are used:
pch
ploting symbols, a vector of plotting characters, with the default taken from trellis.par.get("superpose.symbol")$pch
, typically a vector of 1s (circles).
cex
plotting character sizes, a vector of values, with the default taken from trellis.par.get("superpose.symbol")$cex
, typically a vector of 0.8s.
lines
TRUE
, FALSE
, or a list that controls the characteristics of lines drawn on a plot, and also whether or not multiple lines should be drawn in the same panel in the plot; if FALSE
lines are suppressed; if TRUE
default values are used:
multiline
display a multiline plot in each panel; the default is TRUE
if there are no standard errors
in the "eff"
object, FALSE
otherwise. For an "effpoly"
object multline=TRUE
causes all of the response
levels to be shown in the same panel rather than in separate panels.
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 for multipline plots.
lty
vector of line types, with the default taken from trellis.par.get("superpose.line")$lty
, typically a vector of 1s (solid lines).
lwd
vector of line widths, with the default taken from trellis.par.get("superpose.line")$lwd
, typically a vector with 2 in the first position followed by 1s.
col
a vector of line colors, with the default taken from from trellis.par.get("superpose.line")$col
, used both for lines and
for areas in stacked area plots for "effpoly"
objects; in the latter case, the default colors for an ordered response are instead generated by
sequential_hcl
in the colorspace package.
splines
use splines to smooth plotted effect lines; the default is TRUE
.
axes
a list with elements x
, y
, alternating
, and grid
that control axis limits, ticks, and labels.
The x
and y
elements may themselves be lists.
The x
entry is a list with elements named for predictors, with each predictor element itself a list with the following elements:
lab
axis label, defaults to the name of the predictor; may either
be a text string or a list with the text label (optionally named label
)
as its first element and the named element cex
as its second element.
lim
a twoelement vector giving the axis limits, with the default determined from the data.
ticks
a list with either element at
, a vector specifying locations for the ticks marks, or n
, the number
of tick marks.
transform
transformations to be applied to the horizontal axis of a numeric predictor,
in the form of a list of two functions, with
element names trans
and inverse
. The
trans
function is applied to the values of the predictor, and inverse
is used for computing
proper axis tick labels. The default is not to transform the predictor axis.
Two additional elements may appear in the x
list, and apply to all predictors:
rotate
angle in degrees to rotate tick labels; the default is 0.
rug
display a rug plot showing the marginal distribution of a numeric predictor; the default is TRUE
.
The y
list contains lab
, lim
, ticks
, and rotate
elements
(similar to those specified for individual predictors in the x
list), along with the additional type
, transform
, and style
elements:
type
for plotting linear or generalized linear models, "rescale"
(the default) plots the vertical
axis on the link scale (e.g., the logit scale for a logit model) but labels the axis on the response
scale (e.g., the probability scale for a logit model);
"response"
plots and labels the vertical axis on the scale of the response (e.g., the probability scale for a logit model); and
"link"
plots and labels the vertical axis on the scale of the link (e.g., the logit scale for a logit model).
For polytomous logit models, this element is either "probability"
or "logit"
, with the former as the default.
transform
primarily for linear or linear mixed models, this argument is used to apply an arbitrary transformation to the vertical axis. For example, if fitting a linear model with response log(y)
, then setting transform=exp
would plot exp(log(y)) = y
on the vertical axis. If the response were 1/y
, then use transform=function(yt) 1/yt
, since the reciprocal is its own inverse. The transform
argument can also be a list of two functions. For example with a response log(y)
, the specification transform=list(trans=log, inverse=log), type="rescale"
will plot in logscale, but will label tick marks in arithmetic scale; see the example below. The specification transform=list(trans=log, inverse=exp), type="response"
is equivalent to transform=exp
. When type="response"
the lab
argument will geneally be used to get a label for the axis that matches the untransformed response. If this argument is used with a generalized linear model or another model with a nonidentity link function, the function is applied to the linear predictor, and will probably not be of interest.
style
for polytomous logit models, this element can take on the value "lines"
(the default) or "stacked"
for line plots or stackedarea plots, respectively.
Other elements:
alternating
if TRUE
(the default), the tick labels alternate by panels in
multipanel displays from left to right and top to bottom; if FALSE
, tick labels
appear at the bottom and on the left.
grid
if TRUE
(the default is FALSE
), add grid lines to the plot.
confint
specifications to add/remove confidence intervals or regions from a plot, and to set the nominal confidence level.
style
one of "auto"
, "bars"
, "lines"
, "bands"
, and "none"
; the default
is "bars"
for factors, "bands"
for numeric predictors, and "none"
for multiline plots; "auto"
also produces "bars"
for factors
and "bands"
for numeric predictors, even in multiline plots.
alpha
transparency of confidence bands; the default is 0.15.
col
colors; the default is taken from the line colors.
partial.residuals
specifications concerning the addition of partial residuals to the plot.
plot
display the partial residuals;
the default is TRUE
if residuals are present in the "eff"
object, FALSE
otherwise.
fitted
show fitted values as well as residuals; the default is FALSE
.
col
color for partial residuals; the default is the second line color.
pch
plotting symbols for partial residuals; the default is 1, a circle.
cex
size of symbols for partial residuals; the default is 1.
smooth
draw a loess smooth of the partial residuals; the default is TRUE
.
span
span for the loess smooth; the default is 2/3.
smooth.col
color for the loess smooth; the default is the second line color.
lty
line type for the loess smooth; the default is the first line type, normally 1 (a solid line).
lwd
line width for the loess smooth; the default is the first line width, normally 2.
id
specifications for optional point identification when partial residuals are plotted.
n
number of points to identify; default is 2
if id=TRUE
and 0
if id=FALSE
. Points are selected based on the Mahalanobis
distances of the pairs of xvalues and partial residuals from their centroid.
col
color for the point labels; default is the same as the color of the partial residuals.
cex
relative size of text for point labels; default is 0.75
.
labels
vector of point labels; the default is the names of the residual vector, which is typically the row names of the data frame to which the model is fit.
lattice
the plots are drawn with the lattice package, generally by the xyplot
function. These specifications are passed as arguments to the functions that actually draw the plots.
layout
the layout
argument to the lattice function xyplot
(or, in some cases densityplot
), which
is used to draw the effect display; if not specified, the plot will be formatted so that
it appears on a single page.
key.args
a key, or legend, is added to the plot if multiline=TRUE
. This argument is a list with components that determine the the placement and other characteristics of the key. The default if not set by the user is key.args = list(space="top", columns=2, border=FALSE, fontfamily="serif", cex.title=.80, cex=0.75)
. If there are more than 6 groups in the plot, columns
is set to 3. For stackedarea plots, the default is a onecolumn key. In addition to the arguments shown explicitly below, any of the arguments listed in the xyplot
documentation in the key
section can be used.
space
determines the placement of the key outside the plotting area, with default space="above"
for above the plot and below its title. Setting space="right"
uses space to the right of the plot for the key.
x, y, corner
used to put the key on the graph itself. For example, x=.05, y=.95, corner=c(0,1)
will locate the upperleft corner of the key at (.05, .95), thinking of the graph as a unit square.
columns
number of columns in the key. If space="top"
, columns should be 2, 3 or 4; if space="right"
, set columns=1
.
border
if TRUE
draw a border around the key; omit the border if FALSE
.
fontfamily
the default is "sans"
for the sansserif font used in the rest of the plot; the alternative is "serif"
for a serif font.
cex, cex.title
the default relative size of the font for labels and the title, respectively. To save space set these to be smaller than 1.
strip
a list with three elements: factor.names
, which if TRUE
, the default, shows conditioning
variable names in the panel headers; values
, which if TRUE
, the default unless partial residuals are plotted,
displays conditioning variable values in the panel headers, and cex
, the relative size of the text displayed in the strip.
array
a list with elements row
, col
, nrow
, ncol
, and more
,
used to graph an effect as part of an array of plots; row
, col
, nrow
, and ncol
are used to compose
the split
argument and more
the more
argument to print.trellis
.
The array
argument is automatically set by plot.efflist
and will be ignored if used with that function.
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 plot
method for "eff"
objects returns a "plot.eff"
object (an enhanced "trellis"
object); the provided
print
method plots the object.
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.
LegacyArguments
, effect
, allEffects
, effectsTheme
,
xyplot
, densityplot
, print.trellis
, loess
,
sequential_hcl
,
and the Predictor Effects Graphics Gallery and Effect Displays with Partial Residuals vignettes.
# also see examples in ?effect # plot predictorEffects mod < lm(prestige ~ education + log(income)*type + women, Prestige) plot(predictorEffects(mod, ~ income), axes=list(grid=TRUE)) plot(predictorEffects(mod, ~ income), lines=list(multiline=TRUE), axes=list(grid=TRUE)) plot(predictorEffects(mod, ~ type), lines=list(multiline=TRUE), axes=list(grid=TRUE), confint=list(style="bars")) mod.cowles < glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial) eff.cowles < allEffects(mod.cowles, xlevels=list(extraversion=seq(0, 24, 6))) eff.cowles as.data.frame(eff.cowles[[2]]) # neuroticism*extraversion interaction plot(eff.cowles, 'sex', axes=list(grid=TRUE, y=list(lab="Prob(Volunteer)"), x=list(rotate=90)), lines=list(lty=0)) plot(eff.cowles, 'neuroticism:extraversion', axes=list(y=list(lab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9))))) plot(Effect(c("neuroticism", "extraversion"), mod.cowles, se=list(type="Scheffe"), xlevels=list(extraversion=seq(0, 24, 6))), axes=list(y=list(lab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9))))) # change color of the confidence bands to 'black' with .15 transparency plot(eff.cowles, 'neuroticism:extraversion', axes=list(y=list(lab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9)))), confint=list(col="red", alpha=.3)) plot(eff.cowles, 'neuroticism:extraversion', lines=list(multiline=TRUE), axes=list(y=list(lab="Prob(Volunteer)")), lattice=list(key.args = list(x = 0.65, y = 0.99, corner = c(0, 1)))) # use probability scale in place of logit scale, all lines are black. plot(eff.cowles, 'neuroticism:extraversion', lines=list(multiline=TRUE, lty=1:8, col="black"), axes=list(y=list(type="response", lab="Prob(Volunteer)")), lattice=list(key.args = list(x = 0.65, y = 0.99, corner = c(0, 1))), confint=list(style="bands")) plot(effect('sex:neuroticism:extraversion', mod.cowles, xlevels=list(extraversion=seq(0, 24, 6))), lines=list(multiline=TRUE)) plot(effect('sex:neuroticism:extraversion', mod.cowles, xlevels=list(extraversion=seq(0, 24, 6))), lines=list(multiline=TRUE), axes=list(y=list(type="response")), confint=list(style="bands"), lattice=list(key.args = list(x=0.75, y=0.75, corner=c(0, 0)))) if (require(nnet)){ 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(political.knowledge=0:3))) plot(effect("Europe*political.knowledge", mod.beps, xlevels=list(political.knowledge=0:3), fixed.predictors=list(given.values=c(gendermale=0.5))), axes=list(y=list(style="stacked"), x=list(rug=FALSE), grid=TRUE), lines=list(col=c("blue", "red", "orange"))) } if (require(MASS)){ 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), lines=list(multiline=TRUE)) plot(effect("country*poly(age, 3)", mod.wvs), axes=list(y=list(style="stacked")), lines=list(col=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) plot(eff.pres) plot(eff.pres[1:2]) plot(eff.pres[1], axes=list(x=list(income=list(transform=list( trans=log10, inverse=function(x) 10^x), ticks=list(at=c(1000, 2000, 5000, 10000, 20000)))))) mod < lm(log(prestige) ~ income:type + education, data=Prestige) p1 < predictorEffects(mod, ~ income) # logscale for response plot(p1, lines=list(multiline=TRUE)) # logscale, with arithmetic tick marks plot(p1, lines=list(multiline=TRUE), axes=list(y=list(transform=list(trans=log, inverse = exp), lab="prestige", type="rescale"))) # arithmetic scale and tick marks, with other arguments plot(p1, lines=list(multiline=TRUE), grid=TRUE, lattice=list(key.args=list(space="right", border=TRUE)), axes=list(y=list(transform=exp, lab="prestige")))
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