View source: R/predictorEffects.R
predictorEffects | R Documentation |
Alternatives to the Effect
and allEffects
functions that use a different paradigm for conditioning in an effect display. The user specifies one predictor, either numeric or a factor (where character and logical variables are treated as factors), for the horizontal axis of a plot, and the function determines the appropriate plot to display (which is drawn by plot
). See the vignette Predictor Effects Graphics Gallery for details and examples.
predictorEffect(predictor, mod, focal.levels=50, xlevels=5, ...) ## S3 method for class 'poLCA' predictorEffect(predictor, mod, focal.levels=50, xlevels=5, ...) ## S3 method for class 'svyglm' predictorEffect(predictor, mod, focal.levels=50, xlevels=5, ...) ## Default S3 method: predictorEffect(predictor, mod, focal.levels=50, xlevels=5, ..., sources) predictorEffects(mod, predictors, focal.levels=50, xlevels=5, ...) ## S3 method for class 'poLCA' predictorEffects(mod, predictors = ~ ., focal.levels=50, xlevels=5, ...) ## Default S3 method: predictorEffects(mod, predictors = ~ ., focal.levels=50, xlevels=5, ..., sources)
mod |
A model object. Supported models include all those described on the help page for |
predictor |
quoted name of the focal predictor. |
predictors |
If the default, |
focal.levels |
for For |
xlevels |
this argument is used to set the levels of conditioning predictors; it may either be a single number specifying the number of evenly-spaced values (the default is 5) to which each conditioning predictor is to be set, or it may be a list with elements named for the predictors giving the number of values or a vector of values to which each conditioning predictor is to be set, as explained in the help for If the focal predictor is included in the The default behavior of The |
... |
Additional arguments passed to |
sources |
Provides a mechanism for applying |
Effect plots view a fitted regression function E(Y|X) in (sequences of) two-dimensional plots using conditioning and slicing. The functions described here use a different method of determining the conditioning and slicing than allEffects
uses. The predictor effect of a focal predictor, say x1
, is the usual effect for the generalized interaction of x1
with all the other predictors in a model. When a predictor effect object is plotted, the focal predictor is by default plotted on the horizontal axis.
For example, in the model mod
with formula y ~ x1 + x2 + x3
, the predictor effect p1 <- predictorEffects(mod, ~ x1)
is essentially equilavent to p2 <- Effect("x1", mod)
. When plotted, these objects may produce different graphs because plot(p1)
will always put x1
on the horizontal axis, while plot(p2)
uses a rule to determine the horizontal axis based on the characteristics of all the predictors, e.g., preferring numeric predictors over factors.
If mod
has the formula y ~ x1 + x2 + x3 + x1:x2
, then p1 <- predictorEffects(mod, ~ x1)
is essentially equivalent to p2 <- Effect(c("x1", "x2"), mod)
. As in the last example, the plotted versions of these objects may differ because of different rules used to determine the predictor on the horizontal axis.
If mod
has the formula y ~ x1 + x2 + x3 + x1:x2 + x1:x3
, then p1 <- predictorEffects(mod, ~ x1)
is essentially equilavent to p2 <- Effect(c("x1", "x2", "x3"), mod)
. Again, the plotted versions of these objects may differ because of the rules used to determine the horizontal axis.
predictorEffect
returns an object of class c("predictoreff", "eff")
. The components of the object are described in the help for Effect
; predictorEffects
returns an object of class "predictorefflist"
, which is a list whose elements are of class c("predictoreff", "eff")
.
S. Weisberg sandy@umn.edu and J. Fox
See Effect
.
Effect
, plot.predictoreff
, the Predictor Effects Graphics Gallery vignette, and the Effect Displays with Partial Residuals vignette.
mod <- lm(prestige ~ type*(education + income) + women, Prestige) plot(predictorEffect("income", mod)) plot(predictorEffects(mod, ~ education + income + women)) mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial) plot(predictorEffects(mod.cowles, xlevels=4)) plot(predictorEffect("neuroticism", mod.cowles, xlevels=list(extraversion=seq(5, 20, by=5))), axes=list(grid=TRUE, x=list(rug=FALSE), y=list(lab="Probability of Vounteering")), lines=list(multiline=TRUE), type="response") predictorEffects(mod.cowles, focal.levels=4, xlevels=4) # svyglm() example (adapting an example from the survey package) if (require(survey)){ data(api) dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) mod <- svyglm(sch.wide ~ ell + meals + mobility, design=dstrat, family=quasibinomial()) plot(predictorEffects(mod), axes=list(y=list(lim=log(c(0.4, 0.99)/c(0.6, 0.01)), ticks=list(at=c(0.4, 0.75, 0.9, 0.95, 0.99))))) }
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