🚀 go-pugleaf

I have a simple linear model (fitted with lm()) with 2 independant
variables : one categorical and one integer.

When I run summary.lm() on this model, I get a standard linear
regression summary (in which one categorical variable has to be
converted into many indicator variables) which looks like :

            Estimate Std. Error t value Pr(>|t|)
(Intercept)  -3595.3     2767.1  -1.299   0.2005
physicianB     802.0     2289.5   0.350   0.7277
physicianC    4906.8     2419.8   2.028   0.0485 *
severity      7554.4      906.3   8.336 1.12e-10 ***

and when I run summary.aov() I get similar ANOVA table : 

           Df     Sum Sq    Mean Sq F value    Pr(>F)
physician    2  294559803  147279901  3.3557   0.04381 *
severity     1 3049694210 3049694210 69.4864 1.124e-10 ***
Residuals   45 1975007569   43889057

What is absolutely unclear to me is how F-value and Pr(>F) for the
categorical "physician" variable of the summary.aov() is calculated
from the t-value of the summary.lm() table.

I looked at the summary.aov() source code but still could not figure
it.

Thanks a lot.