Dear Steven,

The reason that linearHypothesis() computes a Wald F or chisquare test rather 
than a t or z test is that the (numerator) df for the linear hypothesis need 
not be 1. 

In your case (as has been pointed out) you can get the coefficient standard 
error directly from the model summary. 

More generally, with some work, you could solve for the the SE for a 1 df 
linear hypothesis in terms of the value of the linear function of coefficients 
and the F or chisquare. That said, I'm not sure why you want to do this.

I hope this helps,
 John

-----------------------------
John Fox, Professor
McMaster University
Hamilton, Ontario
Canada L8S 4M4
Web: socserv.mcmaster.ca/jfox


> -----Original Message-----
> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Steven Yen
> Sent: June 28, 2016 9:27 AM
> To: R-help <r-help@r-project.org>
> Subject: [R] t-test for regression estimate
> 
> test option for linearHypothesis in library(car) include "Chisq" and "F". I 
> prefer
> a simple t-test so that I can retrieve the standard error.
> Any options other than linearHypothesis to test the linear hypothesis (with 1
> restriction/degree of freedom)?
> 
>  > summary(ols1)
> 
> Coefficients:
>              Estimate Std. Error t value Pr(>|t|)
> (Intercept) -0.20013    0.09199  -2.176   0.0298 *
> age          0.04054    0.01721   2.355   0.0187 *
> suburb       0.01911    0.05838   0.327   0.7435
> smcity      -0.29969    0.19175  -1.563   0.1184
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 
>  > linearHypothesis(ols1,"suburb")
> Linear hypothesis test
> 
> Hypothesis:
> suburb = 0
> 
> Model 1: restricted model
> Model 2: polideo ~ age + suburb + smcity
> 
>    Res.Df    RSS Df Sum of Sq      F Pr(>F)
> 1    888 650.10
> 2    887 650.02  1  0.078534 0.1072 0.7435
> 
> 
>       [[alternative HTML version deleted]]
> 
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