[R] [FORGED] standard error for regression coefficients corresponding to factor levels
Doran, Harold
HDoran at air.org
Fri Mar 17 10:05:59 CET 2017
A slightly more ³R-ish² way of doing
S <- solve(t(X)%*%X)
Is to instead use
S <- solve(crossprod(X))
And the idea idea of inverting the SSCP matrix only and not actually
solving the linear system is not so great, which is why it is better to do
as Rolf is suggesting and get all things you need from lm, which uses
decompositions and not the algebraic representations for the solution to
the linear system.
On 3/16/17, 7:41 PM, "Rolf Turner" <r.turner at auckland.ac.nz> wrote:
>
>You have been posting to the R-help list long enough so that you should
>have learned by now *not* to post in html. Your code is mangled so as
>to be unreadable.
>
>A few comments:
>
>(1) Your data frame "data1" seems to have a mysterious (and irrelevant?)
>column named "data1" as well.
>
>(2) The covariance matrix of your coefficient estimates is indeed (as
>you hint) a constant multiple of (X^T X)^{-1}. So do:
>
> X <- model.matrix(~response*week,data=data1)
> S <- solve(t(X)%*%X)
> print(S)
>
>and you will see the same pattern of constancy that your results exhibit.
>
>(3) You could get the results you want much more easily, without all the
>fooling around buried in your (illegible) code, by doing:
>
> mod <- lm(response ~ (region - 1)/week,data=data1)
> summary(mod)
>
>cheers,
>
>Rolf Turner
>
>--
>Technical Editor ANZJS
>Department of Statistics
>University of Auckland
>Phone: +64-9-373-7599 ext. 88276
>
>On 17/03/17 07:26, li li wrote:
>> Hi all,
>> I have the following data called "data1". After fitting the ancova
>>model
>> with different slopes and intercepts for each region, I calculated the
>> regression coefficients and the corresponding standard error. The
>>standard
>> error (for intercept or for slope) are all the same for different
>>regions.
>> Is there something wrong?
>> I know the SE is related to (X^T X)^-1, where X is design matrix. So
>>does
>> this happen whenever each factor level has the same set of values for
>> "week"?
>> Thanks.
>> Hanna
>>
>>
>>
>>> mod <- lm(response ~ region*week, data1)> tmp <- coef(summary(mod))>
>>>res <- matrix(NA, 5,4)> res[1,1:2] <- tmp[1,1:2]> res[2:5,1] <-
>>>tmp[1,1]+tmp[2:5,1]> res[2:5,2] <- sqrt(tmp[2:5,2]^2-tmp[1,2]^2)>
>>>res[1,3:4] <- tmp[6,1:2]> res[2:5,3] <- tmp[6,1]+tmp[7:10,1]>
>>>res[2:5,4] <- sqrt(tmp[7:10,2]^2-tmp[6,2]^2)
>>
>>> colnames(res) <- c("intercept", "intercept SE", "slope", "slope SE")>
>>>rownames(res) <- letters[1:5]> res intercept intercept SE
>>>slope slope SE
>> a 0.18404464 0.08976301 -0.018629310 0.01385073
>> b 0.17605666 0.08976301 -0.022393789 0.01385073
>> c 0.16754130 0.08976301 -0.022367770 0.01385073
>> d 0.12554452 0.08976301 -0.017464385 0.01385073
>> e 0.06153256 0.08976301 0.007714685 0.01385073
>>
>>
>>
>>
>>
>>
>>
>>> data1 week region response
>> 5 3 c 0.057325067
>> 6 6 c 0.066723632
>> 7 9 c -0.025317808
>> 12 3 d 0.024692613
>> 13 6 d 0.021761492
>> 14 9 d -0.099820335
>> 19 3 c 0.119559235
>> 20 6 c -0.054456186
>> 21 9 c 0.078811180
>> 26 3 d 0.091667189
>> 27 6 d -0.053400777
>> 28 9 d 0.090754363
>> 33 3 c 0.163818085
>> 34 6 c 0.008959741
>> 35 9 c -0.115410852
>> 40 3 d 0.193920693
>> 41 6 d -0.087738914
>> 42 9 d 0.004987542
>> 47 3 a 0.121332285
>> 48 6 a -0.020202707
>> 49 9 a 0.037295785
>> 54 3 b 0.214304603
>> 55 6 b -0.052346480
>> 56 9 b 0.082501222
>> 61 3 a 0.053540767
>> 62 6 a -0.019182819
>> 63 9 a -0.057629113
>> 68 3 b 0.068592791
>> 69 6 b -0.123298216
>> 70 9 b -0.230671818
>> 75 3 a 0.330741562
>> 76 6 a 0.013902905
>> 77 9 a 0.190620360
>> 82 3 b 0.151002874
>> 83 6 b 0.086177696
>> 84 9 b 0.178982656
>> 89 3 e 0.062974799
>> 90 6 e 0.062035391
>> 91 9 e 0.206200831
>> 96 3 e 0.123102197
>> 97 6 e 0.040181790
>> 98 9 e 0.121332285
>> 103 3 e 0.147557564
>> 104 6 e 0.062035391
>> 105 9 e 0.144965770
>>
>> [[alternative HTML version deleted]]
>
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