Gary Strangman wrote:
>
>>> (Intercept) 10.9205792 2.9934159 3.6481997 0.001341692
>>> prerbmt 0.4226101 0.1729016 2.4442236 0.022599474
>>> mri -0.3495370 0.7497424 -0.4662095 0.645450898
>>>
>>> ... and if I want the estimate for the 2nd coefficient ...
>>>
>> print summary
>> (Intercept) 10.9205792 2.9934159 3.6481997 0.001341692
>> prerbmt 0.4226101 0.1729016 2.4442236 0.022599474
>> mri -0.3495370 0.7497424 -0.4662095 0.645450898
>>
>> ... and if I want the estimate for the 2nd coefficient ...
>>
> print summary[3][1]
>> 0.4226101
>
> You c
Gary Strangman wrote:
> Hi again,
>
> Forgive me if this is a completely silly question for rpy2. I'm trying to
> wean myself from rpy, but am confused by the new approach to conversion.
> If I fit a model and collect summary info as follows ...
>
> fit = robjects.r.lm(fmla,data)
> summary = ro
laurent oget wrote:
> I do std-error=r['$'](summary,'sigma')
Smart use of "$".
Other possible way:
summary.r["sigma"][0][0]
> I think r.names(summary) will give you the components.
It gives the names of the components.
Consider the following example:
import rpy2.robjects as ro
fit = ro.
Thanks! (I probably never would have come up with those.) FWIW, I also
just stumbled across the option of doing numpy.array(summary[3]) which
gives me a 3x4 array that I can index normally. So, I appear to be fully
up-and-running now. Thanks!
Gary
P.S. The impetus for the switch was a kind of
I do std-error=r['$'](summary,'sigma')
I think r.names(summary) will give you the components.
Laurent
2008/12/12 Gary Strangman :
>
> Hi again,
>
> Forgive me if this is a completely silly question for rpy2. I'm trying to
> wean myself from rpy, but am confused by the new approach to conversion.
Hi again,
Forgive me if this is a completely silly question for rpy2. I'm trying to
wean myself from rpy, but am confused by the new approach to conversion.
If I fit a model and collect summary info as follows ...
fit = robjects.r.lm(fmla,data)
summary = robjects.r.summary(fit)
... the summar
