Please check your statistical methods lecture notes. var(e) has divisor
n-1, and that is not an unbiased estimator of the residual variance when
'e' are residuals. From summary.lm (and you are allowed to read the code)
rdf <- n - p
if (is.na(z$df.residual) || rdf != z$df.residual)
warning("residual degrees of freedom in object suggest this is not
an \"lm\" fit")
p1 <- 1:p
r <- z$residuals
f <- z$fitted.values
w <- z$weights
if (is.null(w)) {
mss <- if (attr(z$terms, "intercept"))
sum((f - mean(f))^2)
else sum(f^2)
rss <- sum(r^2)
}
else {
mss <- if (attr(z$terms, "intercept")) {
m <- sum(w * f/sum(w))
sum(w * (f - m)^2)
}
else sum(w * f^2)
rss <- sum(w * r^2)
r <- sqrt(w) * r
}
resvar <- rss/rdf
the correct divisor is n-p. Since p=3 in your example, that explains a 2%
difference in variances and hence a 1% difference in SEs.
On Tue, 26 Feb 2008, Daniel Malter wrote:
> Hi,
>
> the standard errors of the coefficients in two regressions that I computed
> by hand and using lm() differ by about 1%. Can somebody help me to identify
> the source of this difference? The coefficient estimates are the same, but
> the standard errors differ.
>
> ####Simulate data
>
> happiness=0
> income=0
> gender=(rep(c(0,1,1,0),25))
> for(i in 1:100){
> happiness[i]=1000+i+rnorm(1,0,40)
> income[i]=2*i+rnorm(1,0,40)
> }
>
> ####Run lm()
>
> reg=lm(happiness~income+factor(gender))
> summary(reg)
>
> ####Compute coefficient estimates "by hand"
>
> x=cbind(income,gender)
> y=happiness
>
> z=as.matrix(cbind(rep(1,100),x))
> beta=solve(t(z)%*%z)%*%t(z)%*%y
>
> ####Compare estimates
>
> cbind(reg$coef,beta) ##fine so far, they both look the same
>
> reg$coef[1]-beta[1]
> reg$coef[2]-beta[2]
> reg$coef[3]-beta[3] ##differences are too small to cause a 1%
> difference
>
> ####Check predicted values
>
> estimates=c(beta[2],beta[3])
>
> predicted=estimates%*%t(x)
> predicted=as.vector(t(as.double(predicted+beta[1])))
>
> cbind(reg$fitted,predicted) ##inspect fitted values
> as.vector(reg$fitted-predicted) ##differences are marginal
>
> #### Compute errors
>
> e=NA
> e2=NA
> for(i in 1:length(happiness)){
> e[i]=y[i]-predicted[i] ##for "hand-computed" regression
> e2[i]=y[i]-reg$fitted[i] ##for lm() regression
> }
>
> #### Compute standard error of the coefficients
>
> sqrt(abs(var(e)*solve(t(z)%*%z))) ##for "hand-computed" regression
> sqrt(abs(var(e2)*solve(t(z)%*%z))) ##for lm() regression using e2 from
> above
>
> ##Both are the same
>
> ####Compare to lm() standard errors of the coefficients again
>
> summary(reg)
>
>
> The diagonal elements of the variance/covariance matrices should be the
> standard errors of the coefficients. Both are identical when computed by
> hand. However, they differ from the standard errors reported in
> summary(reg). The difference of 1% seems nonmarginal. Should I have
> multiplied var(e)*solve(t(z)%*%z) by n and divided by n-1? But even if I do
> this, I still observe a difference. Can anybody help me out what the source
> of this difference is?
>
> Cheers,
> Daniel
>
>
> -------------------------
> cuncta stricte discussurus
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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