sigma(model)^2 will give the correct MSE. Also note that your model
matrix has intercept at
the end whereas vcov will have it at the beginning so you will need to
permute the rows
and columns to get them to be the same/
On Wed, Sep 4, 2024 at 3:34 PM Daniel Lobo <danielobo9...@gmail.com> wrote:
>
> Hi,
>
> I am trying to replicate the R's result for VCV matrix of estimated
> coefficients from linear model as below
>
> data(mtcars)
> model <- lm(mpg~disp+hp, data=mtcars)
> model_summ <-summary(model)
> MSE = mean(model_summ$residuals^2)
> vcov(model)
>
> Now I want to calculate the same thing manually,
>
> library(dplyr)
> X = as.matrix(mtcars[, c('disp', 'hp')] %>% mutate(Intercept = 1));
> solve(t(X) %*% X) * MSE
>
> Unfortunately they do not match.
>
> Could you please help where I made mistake, if any.
>
> Thanks
>
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