[R] regression analyses using a vector of means and a variance-covariance matrix

[Gabor Grothendieck]

Here is another approach using the same data as in
John Fox's reply.   His is probably superior but this
does have the advantage that its very simple.  Note
that it gives the same coefficients and R squared
to several decimal places.  We just simulate a
data set with the given means and variance covariance
matrix


> library(car) # for Duncan
> library(MASS) # for mvrnorm
> set.seed(1)
> DF <- mvrnorm(10000, colMeans(data), var(data), empirical = TRUE)
> summary(lm(prestige ~ income + education, as.data.frame(DF)))

Call:
lm(formula = prestige ~ income + education, data = as.data.frame(DF))

Residuals:
     Min       1Q   Median       3Q      Max
-49.0377  -8.7332   0.2068   8.8342  50.2737

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.064663   0.277576  -21.85   <2e-16 ***
income       0.598733   0.007756   77.19   <2e-16 ***
education    0.545834   0.006368   85.71   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 13.06 on 9997 degrees of freedom
Multiple R-Squared: 0.8282,     Adjusted R-squared: 0.8281
F-statistic: 2.409e+04 on 2 and 9997 DF,  p-value: < 2.2e-16

On 10/14/06, John Sorkin <jsorkin at grecc.umaryland.edu> wrote:
> R 2.2.0
> windows XP
>
> How can I perform a regression analyses using a vector of means, a
> variance-covariance matrix? I looked at the help screen for lm and did
> not see any option for using the afore mentioned structures as input to
> lm.
> Thanks,
> John
>
> John Sorkin M.D., Ph.D.
> Chief, Biostatistics and Informatics
> Baltimore VA Medical Center GRECC,
> University of Maryland School of Medicine Claude D. Pepper OAIC,
> University of Maryland Clinical Nutrition Research Unit, and
> Baltimore VA Center Stroke of Excellence
>
> University of Maryland School of Medicine
> Division of Gerontology
> Baltimore VA Medical Center
> 10 North Greene Street
> GRECC (BT/18/GR)
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>
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