Ok! Thank you very much!!!
-Etienne
Gregory Warnes a écrit :
> Hi Etienne,
>
>
>> I imagine that the fact that the fact that av will be a true R object
>> will help the summary() function to find the good method summary.aov
>> ().
>>
>
> Not only do you need to have R call the correct R fucntion, it must
> have a correct R object. When the conversion R-->Python happens,
> some information is lost, so the av object itself isn't a valid aov R
> object when Python--R conversion happens.
>
> -G
>
>
>
>
>> However, I tried to call explicitly summary_aov() with no success.
>> But I have to test it before to go further.
>> Thanks again !!
>> -Etienne
>>
>>
>> Gregory Warnes a écrit :
>>
>>> Hi Etienne,
>>>
>>> The basic problem is that under the default conversion mode
>>> (BASIC_CONVERSION) all R objects are converted to roughly-equivalent
>>> python structures. As a consequence, the object 'av' isn't actually
>>> an R object, so r.summary(av) won't treat it as such. The simplest
>>> solution is to change the conversion mode to NO_CONVERSION adn then
>>> explicitly request conversion of an object when you need the python
>>> version. IE:
>>>
>>>
>>>> set_default_mode(NO_CONVERSION)
>>>> v = r.aov(r.formula("score~factor+Error(id_subject/factor)"),
>>>> data=Res)set_default_mode(BASIC_CONVERSION)
>>>> set_default_mode(BASIC_CONVERSION)
>>>> r.summary(av)
>>>>
>>> -G
>>>
>>> On Dec 18, 2007, at 1:52PM , Etienne Gaudrain wrote:
>>>
>>>
>>>> Hi everyone,
>>>>
>>>> I'm new to RPy, and I came to this terrific module as I was used to
>>>> make some of my analyses in R, and I came to Python in
>>>> replacement of
>>>> Matlab. Formerly, I manipulated data with Matlab, put it in a MySQL
>>>> database, and made my stats in R via ODBC. I'm now thinking about
>>>> jumping one step by calling R directly from Python with RPy.
>>>>
>>>> The analysis I almost always have to do is a repeated measure ANOVA.
>>>> The way I do this in R is :
>>>>
>>>>
>>>> # /after odbc connection and sql query, Res contains my data
>>>> /
>>>> library('stats')
>>>> av <- aov( score~factor+Error(id_subject/factor), data=Res)
>>>> summary(av)
>>>>
>>>>
>>>> Now I tried the same in RPy :
>>>>
>>>>
>>>> # /retrieve data from sql query, Res is a dictionnary
>>>> /
>>>> r.library('stats')
>>>> av = r.aov("score~factor+Error(id_subject/factor)", data=Res)
>>>>
>>>>
>>>> This fails saying that "Error" isn't defined in the dataframe...
>>>> After reading some R doc about GLM, I found that using the R
>>>> function
>>>> formula() seemed to solve this problem:
>>>>
>>>> av = r.aov(r.formula("score~factor+Error(id_subject/factor)"),
>>>> data=Res)
>>>> r.summary(av)
>>>>
>>>> However, a new problem rose in r.summary(). This function returns
>>>> something that isn't readable, and that does not contain the p
>>>> values, or anything similar. It seems that the r.summary_aov()
>>>> function might be adequat, but this function returns an Error saying
>>>> that there is a NaN somewhere...
>>>>
>>>> Does anybody have an advice on how to perform the repeated
>>>> measure ANOVA?
>>>> Thanks!
>>>> -Etienne
>>>>
>>>>
>>>> PS : I use Windows XP, Python 2.5.1, Numpy 1.0.3.1 and RPy
>>>> 1.0.1-Numpy-py2.5 and R 2.6.1.
>>>>
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