# compareCoefs

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

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

- Keywords

##### 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.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.

##### References

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

##### Examples

```
# 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))
# }
```

*Documentation reproduced from package car, version 3.0-0, License: GPL (>= 2)*