pompon wrote:> Hello,
>
> I am a beginner in R and statistics, so my question may be trivial. Sorry
in
> advance.
> I performed a Cox proportion hazard regression with 2 categorical variables
> with cph{design}. Then an anova on the results.
> the output is
>
>> anova(cph(surv(survival, censor) ~ plant + leaf.age + plant*leaf.age,
>> Mpnymph)
>
> Wald Statistics Response: Surv(survival, censored)
>
> Factor Chi-Square d.f.
P
> plant (Factor+Higher Order Factors) 96.96 12 <.0001
> All Interactions 10.58
> 6 0.1022
> leaf.age (Factor+Higher Order Factors) 29.11 7 0.0001
> All Interactions 10.58
> 6 0.1022
> plant * leaf.age (Factor+Higher Order Factors) 10.58 6 0.1022
> TOTAL 106.63 13 <.0001
>
> What do "All interaction" stand for?
> The real df of for plant is 6 and 1 for leaf.age. Then, which chi square is
> one for my main factors anf their interaction.
>
> thank you,
> Julien.
Julien,
I know what you mean when you say 'real df' but that's not the whole
story as plant has 6 more df by interacting with a single df variable.
There is no such thing as 'the' main effect test for plant. The 12 df
test is unique and tests whether plant is associated with Y for any
level of leaf.age.
You can see exactly what is being tested by using various print options
for anova.Design, as described in the help file. The "dots" option is
easy on the eyes.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University