R: Functions For Constructing Effect Displays for Many Modeling...

EffectMethods {effects}

R Documentation

Functions For Constructing Effect Displays for Many Modeling Paradigms

Description

The Effect, effect and predictorEffects methods are used to draw effects plots to visualize a fitted regression surface. These plots can be drawn at least in principle for any model that uses a linear predictor. Methods for modeling paradigms than the basic lm, glm, multinom and polr methods are documented here.

Usage


## Default S3 method:
Effect(focal.predictors, mod,  ...,
    sources=NULL)

## S3 method for class 'gls'
Effect(focal.predictors, mod, ...)

## S3 method for class 'clm2'
Effect(focal.predictors, mod, ...)

## S3 method for class 'clmm'
Effect(focal.predictors, mod, ...)

## S3 method for class 'clm'
Effect(focal.predictors, mod, ...)

## S3 method for class 'merMod'
Effect(focal.predictors, mod, ...,
    KR=FALSE)

## S3 method for class 'rlmerMod'
Effect(focal.predictors, mod, ...)

## S3 method for class 'lme'
Effect(focal.predictors, mod, ...)

## S3 method for class 'poLCA'
Effect(focal.predictors, mod, ...)

## S3 method for class 'mlm'
Effect(focal.predictors, mod, response, ...)

## S3 method for class 'betareg'
Effect(focal.predictors, mod,  ...)

Arguments

`focal.predictors`	a character vector of one or more predictors in the model in any order.
`mod`	a fitted model object of the appropriate class.
`...`	additional arguments passed to other `Effect`. See `Effect` for all the arguments included.
`response`	for an `"mlm"` object, a vector containing the name(s) or indices of one or more response variable(s). The default is to use all responses in the model.
`sources`	This argument appears only in the default method for `Effect`, and allows the user to draw effects plots for fitting methods for which there are not existing methods in the effects package. Seven arguments are provided: type the default is `"glm"`, which assumes the modeling method shares characteristics with a generalized linear model, including a univariate response, a linear predictor, and possibly a error family and link function. call For S3 objects, the default is `ojbect$call`, returning the call that created the object. This is used to harvest standard arguments like `data`, `subset`, `family` an d`weights`. formula the formula for the linear predictor, defaulting to `formula(object)`. If the formula may include terms for random effects that will be ignored. family if the model object includes an error family, but it is not returned by `family(object)`, specify the family with this argument; otherwise it can be ignored. link if the model object has a link function but not a family, specify the link with this argument; otherwise it can be ignored. coefficients The estimates of the coefficents in the linear predictor, with default `coef(object)`. vcov the estimated variance covariance materix to be used in computing errors in the effects plots; default is codevcov(object).
`KR`	if `TRUE` and the pbkrtest package is installed, use the Kenward-Roger coefficient covariance matrix to compute effect standard errors for linear mixed models fit with `lmer` in the lme4 package. The default is `FALSE` because the computation can be very slow.

Details

Most of these methods simply call the Effect.default method with the appropriate values in the arguement sources. See the vignettte Effect Methods in the vignettes for the effects package. All the iteresting work is done by the methods described in Effect.

Value

See Effect

Author(s)

John Fox jfox@mcmaster.ca, Sanford Weisberg sandy@umn.edu

References

Vignette for this package

Examples

## Not run: 
# lme
require(nlme)
fm1 <- lme(distance ~ age + Sex, data = Orthodont, random = ~ 1)
plot(predictorEffects(fm1))

# gls
library(nlme)
g <- gls(Employed ~ GNP + Population,
         correlation=corAR1(form= ~ Year), data=longley)
print(predictorEffects(g))

# lmer  uses method Effect.lmerMod
if("package:nlme" 
require(lme4)
data("Orthodont", package="nlme")
fm2 <- lmer(distance ~ age + Sex + (1 |Subject), data = Orthodont)
plot(allEffects(fm2))

# glmer  uses method Effect.lmerMod
require(lme4)
gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
              data = cbpp, family = binomial)
as.data.frame(predictorEffect("period", gm1))

# rlmer  uses method Effect.rlmerMod
require(robustlmm)
fm3 <- rlmer(distance ~ age + Sex + (1 |Subject), data = Orthodont)
plot(effect("age:Sex", fm3))
plot(predictorEffects(fm3, ~ age + Sex))

# clm in ordinal
require(ordinal)
require(MASS)
mod.wvs1 <- clm(poverty ~ gender + religion + degree + country*poly(age,3),data=WVS)
plot(Effect(c("country", "age"), mod.wvs1), lines=list(multiline=TRUE))

# clm2
require(ordinal)
require(MASS)
v2 <- clm2(poverty ~ gender + religion + degree + country*poly(age,3),data=WVS)
as.data.frame(emod2 <- Effect(c("country", "age"), v2))

# clmm
require(ordinal)
require(MASS)
mm1 <- clmm(SURENESS ~ PROD + (1|RESP) + (1|RESP:PROD), data = soup,
            link = "logit", threshold = "flexible")
as.data.frame(Effect("PROD", mm1))

# poLCA
library(poLCA)
data(election)
f2a <- cbind(MORALG,CARESG,KNOWG,LEADG,DISHONG,INTELG,
             MORALB,CARESB,KNOWB,LEADB,DISHONB,INTELB)~PARTY
nes2a <- poLCA(f2a,election,nclass=3,nrep=5)    # log-likelihood: -16222.32
allEffects(nes2a)

# betareg from the betareg package
library(betareg)
library(effects4)
data("GasolineYield", package = "betareg")
gy_logit <- betareg(yield ~ batch + temp, data = GasolineYield)
summary(gy_logit)
Effect("batch", gy_logit)
predictorEffects(gy_logit)

## End(Not run)

[Package effects version 4.0-1 Index]