Dear Steven,
I understand your request now. Maybe you could try to change the function lm
and create another function enabling you to have the information you want
really quickly.
Use lm() to see the code of this command and change this one marginally to get
what you want to. Once this is done, copy paste the code in a new function and
save your code.
Regards,
Adrien.
De : Steven Yen <sye...@gmail.com>
À : "Fox, John" <j...@mcmaster.ca>
Cc : R-help <r-help@r-project.org>
Envoyé le : Mercredi 29 juin 2016 0h43
Objet : Re: [R] t-test for regression estimate
Thanks John. Reason is I am doing linear transformations of many
coefficients (e.g., bi / scalar). Of course I can uncover the
t-statistic from the F statistic and then the standard error. Simply
scaling the estimated coefficients I can also transform the standard
errors. I have since found deltaMethod from library "car" useful. Its
just that, if linearHypothesis had provide the standard errors and
t-statistics then the operation would have been easier, with a one-line
command for each coefficient. Thank you again.
On 6/28/2016 6:28 PM, Fox, John wrote:
> 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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______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
