The code bellow, illustrate the examination of a model:

fit = lm(y ~ x1 + x2, data=mydata2) # Your model

summary(fit) # The summary of the results

vcov(fit) # The covariance matrix for model parameters

Also, at the following link [1] you can find a full tutorial about Logistic Regression in R language.

[1] http://ww2.coastal.edu/kingw/statistics/R-tutorials/logistic.html

Dimitris

A slight edit to Dimitris' code, to make it a logistic regression, use:

fit = glm(y ~ x1 + x2 + xn, family='binomial', data=mydata)

As a new R user, I strongly recommend to use help commands like

> ?lm

> ?glm

?function_name give you information about the parameters and the objects that the program creates, including the covariance matrix...

you should use glm function and after estimation the model parameters, vcov function obtains covariance matrix of estimated parameters. This cod is an example for poisson response:

Poi