| effect {effects} | R Documentation |
effect constructs an "eff" object for a term (usually a high-order term)
in a linear or generalized linear model, or an "effpoly" object for a term in a
multinomial or proportional-odds logit model,
absorbing the lower-order terms marginal
to the term in question, and averaging over other terms in the model.
allEffects identifies all of the high-order terms in a model and returns
a list of "eff" or "effpoly" objects (i.e., an object of type "efflist").
effect(term, mod, ...)
## S3 method for class 'lm':
effect(term, mod, xlevels=list(), default.levels=10, given.values,
se=TRUE, confidence.level=.95,
transformation=list(link=family(mod)$linkfun, inverse=family(mod)$linkinv),
typical=mean, ...)
## S3 method for class 'multinom':
effect(term, mod, confidence.level=.95, xlevels=list(), default.levels=10,
given.values, se=TRUE, typical=mean, ...)
## S3 method for class 'polr':
effect(term, mod, confidence.level=.95, xlevels=list(), default.levels=10,
given.values, se=TRUE, typical=mean, latent=FALSE, ...)
allEffects(mod, ...)
## S3 method for class 'eff':
as.data.frame(x, row.names=NULL, optional=TRUE, ...)
## S3 method for class 'effpoly':
as.data.frame(x, row.names=NULL, optional=TRUE, ...)
## S3 method for class 'efflatent':
as.data.frame(x, row.names=NULL, optional=TRUE, ...)
term |
the quoted name of a term, usually, but not necessarily, a high-order
term in the model. The term must be given exactly as it appears in the printed
model, although either colons (:) or asterisks (*) may be used
for interactions. |
mod |
an object of class "lm", "glm", "multinom", or "polr". |
xlevels |
an optional list of values at which to set covariates,
with components of the form covariate.name = vector.of.values. |
default.levels |
number of values for covariates that are not
specified explicitly via xlevels; covariate values set by
default are evenly spaced between the minimum and maximum values in
the data. |
given.values |
a numeric vector of named elements, setting particular
columns of the model matrix to specific values for terms not
appearing in an effect; if specified, takes precedence over the
application of the function given in the typical argument
(below). Care must be taken in specifying these values — e.g.,
for a factor, the values of all contrasts should be given and these
should be consistent with each other. |
se |
if TRUE, the default, calculate standard errors and
confidence limits for the effects. |
confidence.level |
level at which to compute confidence limits
based on the standard-normal distribution; the default is 0.95. |
transformation |
a two-element list with elements link and
inverse. For a generalized linear model, these are by default
the link function and inverse-link (mean) function. For a linear model,
these default to NULL. If NULL, the identify function,
I, is used; this effect can also be achieved by setting the
argument to NULL. The inverse-link may be used to transform effects
when they are printed or plotted; the link may be used in positioning
axis labels (see below). If the link is not given, an attempt will be
made to approximate it from the inverse-link. |
typical |
a function to be applied to the columns of the model matrix
over which the effect is "averaged"; the default is mean. |
latent |
if TRUE, effects in a proportional-odds logit model
are computed on the scale of the latent response; if FALSE
(the default) effects are computed as individual-level probabilities
and logits. |
x |
an object of class "eff" or "effpoly". |
row.names, optional |
not used. |
... |
arguments to be passed down. |
Normally, the functions to be used directly are allEffects, to return
a list of high-order effects, and the generic plot function to plot the effects.
(see plot.efflist, plot.eff, and plot.effpoly).
Plots are drawn using the xyplot (or in some cases,
the densityplot) function in the
lattice package. Effects may also be printed (implicitly or explicitly via
print) or summarized (using summary)
(see print.efflist, summary.efflist,
print.eff, summary.eff, print.effpoly, and summary.effpoly).
If asked, the effect function will compute effects for terms that have
higher-order relatives in the model, averaging over those terms (which rarely makes sense), or for terms that
do not appear in the model but are higher-order relatives of terms that do.
For example, for the model Y ~ A*B + A*C + B*C, one could
compute the effect corresponding to the absent term A:B:C, which absorbs the constant, the
A, B, and C main effects, and the three two-way interactions. In either of these
cases, a warning is printed.
In calculating effects, the strategy for `safe' prediction described in Hastie (1992: Sec. 7.3.3) is employed.
For lm and glm, effect returns an "eff" object, and for multinom
and polr, an "effpoly" object, with the following components:
term |
the term to which the effect pertains. |
formula |
the complete model formula. |
response |
a character string giving the name of the response variable. |
y.levels |
(for "effpoly" objects) levels of the polytomous response variable. |
variables |
a list with information about each predictor, including its name, whether it is a factor, and its levels or values. |
fit |
(for "eff" objects) a one-column matrix of fitted values, representing the effect
on the scale of the linear predictor; this is a ravelled table, representing all
combinations of predictor values. |
prob |
(for "effpoly" objects) a matrix giving fitted probabilities for the effect
for the various levels of the the response (columns) and combinations of the focal predictors (rows). |
logit |
(for "effpoly" objects) a matrix giving fitted logits for the effect
for the various levels of the the response (columns) and combinations of the focal predictors (rows). |
x |
a data frame, the columns of which are the predictors in the effect, and the rows of which give all combinations of values of these predictors. |
model.matrix |
the model matrix from which the effect was calculated. |
data |
a data frame with the data on which the fitted model was based. |
discrepancy |
the percentage discrepancy for the `safe' predictions of the original fit; should be very close to 0. |
model |
(for "effpoly" objects) "multinom" or "polor", as appropriate. |
se |
(for "eff" objects) a vector of standard errors for the effect, on the scale of the linear predictor. |
se.prob, se.logit |
(for "effpoly" objects) matrices of standard errors for the effect, on the probability and logit scales. |
lower, upper |
(for "eff" objects) one-column matrices of confidence limits, on the
scale of the linear predictor. |
lower.prob, upper.prob, lower.logit, upper.logit |
(for "effpoly" objects) matrices of confidence limits
for the fitted logits and probabilities; the latter are computed by transforming
the former. |
confidence.level |
for the confidence limits. |
transformation |
(for "eff" objects) a two-element list, with element link giving the
link function, and element inverse giving the inverse-link (mean) function. |
effectList returns a list of "eff" or "effpoly" objects
corresponding to the high-order terms of the model.
John Fox jfox@mcmaster.ca and Jangman Hong.
Fox, J. (1987) Effect displays for generalized linear models. Sociological Methodology 17, 347–361.
Fox, J. (2003) Effect displays in R for generalised linear models. Journal of Statistical Software 8:15, 1–27, <http://www.jstatsoft.org/counter.php?id=75&url=v08/i15/effect-displays-revised.pdf&ct=1>.
Fox, J. and R. Andersen (2006) Effect displays for multinomial and proportional-odds logit models. Sociological Methodology 36, 225–255.
Hastie, T. J. (1992) Generalized additive models. In Chambers, J. M., and Hastie, T. J. (eds.) Statistical Models in S, Wadsworth.
print.eff, summary.eff, plot.eff,
print.summary.eff,
print.effpoly, summary.effpoly, plot.effpoly,
print.efflist, summary.efflist,
plot.efflist, xyplot,
densityplot
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)), given.values=c(sexmale=0.5))
eff.cowles
plot(eff.cowles, 'sex', ylab="Prob(Volunteer)")
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)
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")
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)