anova(cph(..) output
Hi, Thank you very much for the answer. However, I have still some misunderstandings. from the output, can we say that plant and leaf age are significant but not their interaction? And the last question I promise, what would you advise me to write in the paper to explain the different method and ackonwledge for the df? Thank you again, julien.
Frank E Harrell Jr wrote:
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
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