Linear Model Summary with Sandwich Standard Errors

Description

summarySandwich creates a summary of a "lm" object similar to the standard one, with sandwich estimates of the coefficient standard errors in the place of the usual OLS standard errors, also modifying as a consequence the reported t-tests and p-values for the coefficients. Standard errors may be computed from a heteroscedasticity-consistent ("HC") covariance matrix for the coefficients (of several varieties), or from a heteroscedasticity-and-autocorrelation-consistent ("HAC") covariance matrix.

Usage

summarySandwich(model, ...)
    
## S3 method for class 'lm'
summarySandwich(model, 
  type=c("hc3", "hc0", "hc1", "hc2", "hc4", "hac"), ...)

Arguments

Value

an object of class "summary.lm", with sandwich standard errors substituted for the usual OLS standard errors; the omnibus F-test is similarly adjusted.

Author(s)

John Fox jfox@mcmaster.ca

See Also

hccm, vcovHAC.

Examples

    mod <- lm(prestige ~ income + education + type, data=Prestige)
    summary(mod)
    summarySandwich(mod)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RcmdrMisc)
Loading required package: car
Loading required package: sandwich
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RcmdrMisc/summarySandwich.Rd_%03d_medium.png", width=480, height=480)
> ### Name: summarySandwich
> ### Title: Linear Model Summary with Sandwich Standard Errors
> ### Aliases: summarySandwich summarySandwich.lm
> ### Keywords: misc
> 
> ### ** Examples
> 
>     mod <- lm(prestige ~ income + education + type, data=Prestige)
>     summary(mod)

Call:
lm(formula = prestige ~ income + education + type, data = Prestige)

Residuals:
     Min       1Q   Median       3Q      Max 
-14.9529  -4.4486   0.1678   5.0566  18.6320 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.6229292  5.2275255  -0.119    0.905    
income       0.0010132  0.0002209   4.586 1.40e-05 ***
education    3.6731661  0.6405016   5.735 1.21e-07 ***
typeprof     6.0389707  3.8668551   1.562    0.122    
typewc      -2.7372307  2.5139324  -1.089    0.279    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.095 on 93 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.8349,	Adjusted R-squared:  0.8278 
F-statistic: 117.5 on 4 and 93 DF,  p-value: < 2.2e-16

>     summarySandwich(mod)

Call:
lm(formula = prestige ~ income + education + type, data = Prestige)

Residuals:
     Min       1Q   Median       3Q      Max 
-14.9529  -4.4486   0.1678   5.0566  18.6320 

Coefficients:
              Estimate Std.Err(hc3) t value Pr(>|t|)    
(Intercept) -0.6229292    5.2381025  -0.119 0.905593    
income       0.0010132    0.0002672   3.793 0.000265 ***
education    3.6731661    0.6982758   5.260 9.16e-07 ***
typeprof     6.0389707    3.7951209   1.591 0.114948    
typewc      -2.7372307    2.4384681  -1.123 0.264531    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 7.095 on 93 degrees of freedom
  (4 observations deleted due to missingness)
Multiple R-squared:  0.8349,	Adjusted R-squared:  0.8278 
F-statistic: 120.2 on 4 and 93 DF,  p-value: < 2.2e-16

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>