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

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

…

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.

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.

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

# NOT RUN { 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)) # }