car: compareCoefs – R documentation

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compareCoefs

Print estimated coefficients and their standard errors in a table for several regression models.

Description

This function extracts estimates of regression parameters and their standard errors from one or more models and prints them in a table.

Usage

compareCoefs(..., se = TRUE, zvals = FALSE, pvals = FALSE, vcov.,
    print = TRUE, digits = 3)

Arguments

`...`	One or more regression-model objects. These may be of class `lm`, `glm`, `nlm`, or any other regression method for which the functions `coef` and `vcov` return appropriate values, or if the object inherits from the `mer` class created by the `lme4` package or `lme` in the `nlme` package.
`se`	If `TRUE`, the default, show standard errors as well as estimates.
`zvals`	If `TRUE` (the default is `FALSE`), print Wald statistics, the ratio of each coefficient to its standard error.
`pvals`	If codeTRUE (the default is `FALSE`), print two-sided p-values from the standard normal distribution corresponding to the Wald statistics.
`vcov.`	an optional argument, specifying a function to be applied to all of the models, returning a coefficient covariance matrix for each, or a list with one element for each model, with each element either containing a function to be applied to the corresponding model or a coefficient covariance matrix for that model. If omitted, `vcov` is applied to each model.
`print`	If `TRUE`, the defualt, the results are printed in a nice format using `printCoefmat`. If `FALSE`, the results are returned as a matrix
`digits`	Passed to the `printCoefmat` function for printing the result.

Value

This function is mainly used for its side-effect of printing the result. It also invisibly returns a matrix of estimates, standard errors, Wald statistics, and p-values.

Author(s)

John Fox jfox@mcmaster.ca

References

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

Examples

mod1 <- lm(prestige ~ income + education, data=Duncan)
mod2 <- update(mod1, subset=-c(6,16))
mod3 <- update(mod1, . ~ . + type)
mod4 <- update(mod1, . ~ . + I(income + education)) # aliased coef.
compareCoefs(mod1)
compareCoefs(mod1, mod2, mod4)
compareCoefs(mod1, mod2, mod3, zvals=TRUE, pvals=TRUE)
compareCoefs(mod1, mod2, se=FALSE)
compareCoefs(mod1, mod1, vcov.=list(vcov, hccm))

car

Companion to Applied Regression

v3.0-10

GPL (>= 2)

Authors

John Fox [aut, cre], Sanford Weisberg [aut], Brad Price [aut], Daniel Adler [ctb], Douglas Bates [ctb], Gabriel Baud-Bovy [ctb], Ben Bolker [ctb], Steve Ellison [ctb], David Firth [ctb], Michael Friendly [ctb], Gregor Gorjanc [ctb], Spencer Graves [ctb], Richard Heiberger [ctb], Pavel Krivitsky [ctb], Rafael Laboissiere [ctb], Martin Maechler [ctb], Georges Monette [ctb], Duncan Murdoch [ctb], Henric Nilsson [ctb], Derek Ogle [ctb], Brian Ripley [ctb], William Venables [ctb], Steve Walker [ctb], David Winsemius [ctb], Achim Zeileis [ctb], R-Core [ctb]

Initial release

2020-09-23