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.
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