S {car} | R Documentation

## Modified Functions for Summarizing Linear, Generalized Linear, and Some Other Models

### Description

car package replacements for the `summary` (`S`) and `confint` (`Confint`) functions for `lm`, `glm`, `multinom`, and `polr` objects, with additional arguments but the same defaults as the original functions. The `Confint` method for `"polr"` objects profiles the likelihood to get confidence intervals for the regression parameters but uses Wald intervals for the thresholds. Default methods that call the standard R `summary` and `confint` functions are provided for the `S` and `Confint` generics, so the car functions should be safe to use in general. The default method for `Confint` also assumes that there is an appropriate `coef` method. For briefer model summaries, see `brief`.

### Usage

    S(object, brief, ...)

    ## S3 method for class 'lm'
    S(object, brief=FALSE,
        correlation = FALSE, symbolic.cor = FALSE,
        vcov. = vcov(object, complete=FALSE), header = TRUE,
        resid.summary = FALSE, adj.r2 = FALSE,
        ...)

    ## S3 method for class 'glm'
    S(object, brief=FALSE,
        exponentiate, dispersion, correlation = FALSE, symbolic.cor = FALSE,
        vcov. = vcov(object, complete=FALSE), header = TRUE,
        resid.summary = FALSE, ...)

    ## S3 method for class 'multinom'
    S(object, brief=FALSE, exponentiate=FALSE, ...)

    ## S3 method for class 'polr'
    S(object, brief=FALSE, exponentiate=FALSE, ...)

    ## S3 method for class 'lme'
    S(object, brief=FALSE, correlation=FALSE, ...)

    ## S3 method for class 'lmerMod'
    S(object, brief=FALSE, KR=FALSE, correlation=FALSE, ...)

    ## S3 method for class 'glmerMod'
    S(object, brief=FALSE, correlation=FALSE, exponentiate, ...)

    ## Default S3 method:
    S(object, brief, ...)

    ## S3 method for class 'S.lm'
    print(x, digits = max(3, getOption("digits") - 3),
        symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...)

    ## S3 method for class 'S.glm'
    print(x, digits = max(3L, getOption("digits") - 3L),
        symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...)

    ## S3 method for class 'S.multinom'
    print(x, digits = max(3, getOption("digits") - 3),
        signif.stars = getOption("show.signif.stars"), ...)

    ## S3 method for class 'S.polr'
    print(x, digits = max(3, getOption("digits") - 3),
        signif.stars = getOption("show.signif.stars"), ...)

    ## S3 method for class 'S.lme'
    print(x, digits=max(3, getOption("digits") - 3),
        signif.stars = getOption("show.signif.stars"), ...)

    ## S3 method for class 'S.lmerMod'
    print(x, digits=max(3, getOption("digits") - 3),
        signif.stars = getOption("show.signif.stars"), ...)

    ## S3 method for class 'S.glmerMod'
    print(x, digits=max(3, getOption("digits") - 3),
        signif.stars = getOption("show.signif.stars"), ...)

    Confint(object, ...)

    ## S3 method for class 'lm'
    Confint(object, estimate=TRUE,
        parm, level=0.95, vcov.=vcov(object, complete=FALSE), ...)

    ## S3 method for class 'glm'
    Confint(object, estimate=TRUE, exponentiate=FALSE,
        vcov., dispersion, ...)

    ## S3 method for class 'polr'
    Confint(object, estimate=TRUE, exponentiate=FALSE,
        thresholds=!exponentiate, ...)

    ## S3 method for class 'multinom'
    Confint(object, estimate=TRUE, exponentiate=FALSE, ...)

    ## S3 method for class 'lme'
    Confint(object, estimate=TRUE, level=0.95, ...)

    ## S3 method for class 'lmerMod'
    Confint(object, estimate=TRUE, level=0.95, ...)

    ## S3 method for class 'glmerMod'
    Confint(object, estimate=TRUE, level=0.95,
        exponentiate=FALSE, ...)

    ## Default S3 method:
    Confint(object, estimate=TRUE, level=0.95, vcov., ...)

### Arguments

`object` | a model object, e.g., of class `"lm"` as produced by a call to `lm`.
`exponentiate` | for a `"glm"` or `"glmerMod"` model using the `log` or `logit` link, or a `"polr"` or `"multinom"` model, show exponentiated coefficient estimates and confidence bounds.
`correlation, symbolic.cor` | see `summary.lm`
`x, digits, signif.stars` | see `summary.lm`
`dispersion` | see `summary.glm`

`vcov.`

either a matrix giving the estimated covariance matrix of the estimates, or a function that when called with `object` as an argument returns an estimated covariance matrix of the estimates. The default of `vcov. = vcov` uses the usual estimated covariance matrix. Other choices include the functions documented at `hccm`. See example below for using a bootstrap to estimate the covariance matrix. For the `glm` methods of `Confint` and `S`, if the `vcov.` or `dispersion` argument is specified, then Wald-based confidence limits are computed; otherwise the reported confidence limits are computed by profiling the likelihood. NOTE: The `dispersion` and `vcov.` arguments may not both be specified.

`header` | if `TRUE`, print the header for the summary output, default is `TRUE`
`resid.summary` | if `TRUE`, print the five-number summary of the residuals in the summary, defaults to `FALSE`
`adj.r2` | if `TRUE`, print the adjusted r-squared in the summary, default is `FALSE`
`brief` | if `TRUE`, set `header`, `resid.summary` and `adj.r.squared` to `FALSE`, and suppress exponeniated coefficients for GLMs with log or logit link.
`KR` | if `TRUE` (default is `FALSE`), compute Kenward-Roger standard errors and Satterthwaite degrees of freedom for t-tests. Warning: This computation can be very time-consuming.
`parm, level` | see `confint`
`estimate` | show the estimated coefficients in the confidence-interval table; default is `TRUE`.
`thresholds` | show confidence intervals for the estimated thresholds in the `"polr" model.`
`...` | additional arguments to be passed down, for consistency with `summary` and `confint` methods

### Details

All these functions mimic functions in the stats and other standard R packages for summarizing aspects of linear, generalized linear, and some other statistical models.

The `S` and `Confint` functions add support for the `vcov.` argument for linear models, which allows specifying a covariance matrix for the regression coefficients other than the usual covariance matrix returned by the function `vcov`. This argument may be either the name of a function, so that the call to `vcov.(object)` returns a covariance matrix, or else `vcov.` is set equal to a covariance matrix. For example, setting `vcov.=hccm` uses 'proposal 3' described by Long and Ervin (2000) for a sandwich coefficient-variance estimator that may be robust against nonconstant variance (see `hccm`). Setting `vcov. = hccm(object, type = "hc2")` would use the matrix returned by the `hccm` function using proposal 2. For use with a bootstrap, see the examples below. The overall F-test in the `S.lm` output uses the supplied covariance matrix in a call to the `linearHypothesis` function.

The supplied `print` method for `S.lm` (and for other `S` methods) has additional arguments to customize the standard `summary.lm` output. Standard output is obtained by setting `resid.summary=TRUE, adj.r2=TRUE`.

Using a heterscedasticy-corrected covariance matrix computed using `hccm` with GLMs other than Gaussian is not justified; see the article by Freedman (2006).

The `Summary.glm` method for models fit with the log or logit link by default prints a table of exponentiated coefficients and their confidence limits; `Summary.multinom` and `Summary.polr` optionally print tables of exponentiated coefficients.

### Value

The `S.lm` and `S.glm` functions return a list with all the elements shown at `summary.lm` and `summary.glm`. The `S.multinom` and `S.polr` functions return a list with all the elements shown at `summary.multinom` and `summary.polr` plus potentially a table of exponentiated coefficients and confidence bounds.

The `Confint.lm` function returns either the output from `confint.lm` if `vcov. = vcov` or Wald-type confidence intervals using the supplied covariance matrix for any other choice of `vcov.`.

Finally, `Confint` applied to any object that does not inherit from `"lm"`, `"multinom"`, or `"polr"` simply calls `confint`, along with, by default, using `coef` to add a column of estimates to the confidence limits.

### Author(s)

Sanford Weisberg sandy@umn.edu

### References

Freedman, David A. (2006). On the so-called Huber sandwich estimator and robust standard errors. The American Statistician, 60, 299-302.

Long, J. S. and Ervin, L. H. (2000) Using heteroscedasity consistent standard errors in the linear regression model. The American Statistician 54, 217–224.

White, H. (1980) A heteroskedastic consistent covariance matrix estimator and a direct test of heteroskedasticity. Econometrica 48, 817–838.

### See Also

`brief`, `summary`, `confint`, `coef`, `summary.lm`, `confint`, `vcov.lm`, `hccm`, `Boot`, `linearHypothesis`

### Examples

    mod.prestige <- lm(prestige ~ education + income + type, Prestige)
    S(mod.prestige, vcov.=hccm)
    S(mod.prestige, brief=TRUE)
    Confint(mod.prestige, vcov.=hccm)

    # A logit model
    mod.mroz <- glm(lfp ~ ., data=Mroz, family=binomial)
    S(mod.mroz)

    ## Not run:
    # Using the bootstrap for standard errors
    b1 <- Boot(mod.prestige)
    S(mod.prestige, vcov.= vcov(b1))
    Confint(b1) # run with the boot object to get corrected confidence intervals

    ## End(Not run)

[Package car version 3.0-0 Index]