Alexander Sirotkin [at Yahoo] wrote:
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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