[R] regression parms var-cov matrix
Martin Maechler
maechler at stat.math.ethz.ch
Thu Apr 24 11:23:20 CEST 2003
>>>>> "DavidP" == Paul, David A <paulda at BATTELLE.ORG>
>>>>> on Wed, 23 Apr 2003 18:51:39 -0400 writes:
DavidP> Win2k, R1.6.2.
DavidP> I've been using Splus 6.1 and wanted to try the same
DavidP> regression analysis in R. Using "names( blah.lm )"
DavidP> in R yields
DavidP> [1] "coefficients" "residuals" "effects" "rank"
DavidP> [5] "fitted.values" "assign" "qr" "df.residual"
DavidP> [9] "xlevels" "call" "terms" "model"
DavidP> In Splus, the same command yields
DavidP> [1] "coefficients" "residuals" "fitted.values" "effects"
DavidP> [5] "R" "rank" "assign" "df.residual"
DavidP> [9] "contrasts" "terms" "call"
DavidP> and blah.lm$R gives the variance-covariance matrix of the
DavidP> model parameters. How do get the variance-covariance matrix out
DavidP> of R? Apologies for such a simple question.
The most recommended way is to use the generic function vcov()
which has methods for many classes, included "lm".
A bit more low level, but still reliable approach is to do what
vcov.lm does:
vcov.lm <- function (object, ...)
{
so <- summary.lm(object, corr = FALSE)
so$sigma^2 * so$cov.unscaled
}
A much more low level (non-recommended) way would be to look at
<your lm object> $ qr etc and replicate what summary.lm() is doing
for its $sigma and $cov.unscaled computations.
DavidP> Much thanks in advance,
you're welcome!
Martin Maechler ETH Zurich
More information about the R-help
mailing list