summary.eff {effects} | R Documentation |
Summarizing and Printing Effects
Description
summary
, print
, and as.data.frame
methods for objects created using the effects package.
Usage
## 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 'mlm.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 'mlm.efflist'
summary(object, ...)
## S3 method for class 'eff'
as.data.frame(x, row.names=NULL, optional=TRUE,
type=c("response", "link"), ...)
## S3 method for class 'efflist'
as.data.frame(x, row.names=NULL, optional=TRUE, type, ...)
## 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, ...)
## S3 method for class 'eff'
vcov(object, ...)
Arguments
x , object |
an object consisting of fitted values and other information needed to draw effects plots that is produced by functions in the |
type |
fitted values are by default printed by these functions in the |
row.names , optional |
arguments to |
... |
other arguments passed on |
Value
The print
methods return the fitted values in tables. The summary
methods return the fitted values and 95 percent condifence intervals, also in tables. The as.data.frame
method returns fitted values, standard errors, and 95 percent confidence intervals as a data frame, or as a list of data frames for the efflist
method. The vcov
method returns the covariance matrix of the fitted values.
Author(s)
John Fox jfox@mcmaster.ca and Jangman Hong.
Examples
mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion,
data=Cowles, family=binomial)
eff.cowles <- predictorEffects(mod.cowles)
print(eff.cowles)
print(eff.cowles[["neuroticism"]], type="link")
summary(eff.cowles[["neuroticism"]], type="link")
as.data.frame(eff.cowles)
# covariance matrix of fitted values in linear predictor scale
vcov(eff.cowles[[1]])