I see. I changed the code to the following and it worked!
import rpy2
import rpy2.robjects as robj
robj.packages.importr("kernlab")
robj.r.load("MPDR.training_size2000.RData")
model = robj.globalenv["model"]
outcome = robj.r.predict(model)
Many thanks!
--Elizabeth
On Sun, Apr 1, 2012 at 10:59 PM, Laurent Gautier <lgaut...@gmail.com> wrote:
> On 2012-04-02 03:07, Elizabeth Tseng wrote:
>
> Hi,
>
> I've just started using rpy/rpy2. I have a SVM model trained using the
> kernlab package. I stored the model in a R file called
> "MPDR.training_size2000.RData" (so if I call 'load' in R, the object
> 'model' is the trained model).
>
> I'm trying to load the model through rpy2, then run predict() on it. My
> code is as follows:
>
> import rpy2.rpy_classic as rpy
> import rpy2.robjects as robj
> rpy.set_default_mode(rpy.NO_CONVERSION)
> model = rpy.r.load("MPDR.training_size2000.RData")
> rpy.r.predict(model, <some data here>)
>
>
> and I get the error:
>
> no applicable method for 'predict' applied to an object of class
> "character"
>
>
> It seems like I'm not loading the model correctly. What can be done?
>
>
> The R function loads objects in R's "global environment"; what is returned
> by load() is the names/symbols of the objects loaded AFAIR. Your model is
> somewhere in R's global environment.
>
> L.
>
> PS: rpy2 has a more Python-like interface to that
> http://rpy.sourceforge.net/rpy2/doc-2.2/html/robjects_serialization.html
>
>
>
> Thanks,
> --Elizabeth
>
>
>
>
>
>
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