[R] anova(cph(..) output

[Frank E Harrell Jr]

pompon wrote:
> 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.

I would say there is moderate evidence for an interaction (P=0.10) and 
strong evidence for both a plant effect (at least at some level of leaf) 
and a leaf effect (at least at some level of plant).

Frank

> 
>  
> 
> 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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>>
>>
> 


-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University