Also,
Is there a way to get the second command (hypothesis defined with
externally scalars) below to work? Thanks.
linearHypothesis(U,"0.5*eq1_DQ+0.3*eq2_DQ",verbose=T)
w1<-0.5; w2<-0.3
linearHypothesis(U,"w1*eq1_DQ+w2*eq2_DQ",verbose=T) # does not work
On 6/29/2016 12:38 PM, Steven Yen wrote:
> Thanks John. Yes, by using verbose=T, I get the value of the
> hypothesis. But tell me again, how would I get the variance (standard
> error)?
>
> On 6/29/2016 11:56 AM, Fox, John wrote:
>> Dear Steven,
>>
>> OK -- that makes sense, and there was also a previous request for
>> linearHypothesis() to return the value of the hypothesis and its covariance
>> matrix. In your case, where there's only 1 numerator df, that would be the
>> value and estimated sampling variance of the hypothesis.
>>
>> I've now implemented that, using (at least provisionally) attributes in the
>> development version of the car package on R-Forge, which you should be able
>> to install via install.packages("car",
>> repos="http://R-Forge.R-project.org";). Then see ?linearHypothesis for more
>> information.
>>
>> Best,
>> John
>>
>>> -----Original Message-----
>>> From: Steven Yen [mailto:sye...@gmail.com]
>>> Sent: June 28, 2016 3:44 PM
>>> To: Fox, John<j...@mcmaster.ca>
>>> Cc: R-help<r-help@r-project.org>
>>> Subject: 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> <mailto: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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>>> list -- To UNSUBSCRIBE and more, see
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>>> PLEASE do read the posting guidehttp://www.R-
>>> project.org/posting-
>>> guide.html
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>>>
>
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