[R] nonmonotonic glm?
Ben Bolker
bbolker at gmail.com
Sun Jan 11 23:28:04 CET 2015
If you're going to use splines, another possibility is mgcv::gam (also
part of standard R installation)
require(mgcv)
gam(DV ~ s(IV), data= YourDataFrame, family=binomial)
this has the advantage that the complexity of the spline is
automatically adjusted/selected by the fitting algorithm (although
occasionally you need to use s(IV,k=something_bigger) to adjust the
default *maximum* complexity chosen by the code)
On Sun, Jan 11, 2015 at 5:23 PM, Marc Schwartz <marc_schwartz at me.com> wrote:
>
>> On Jan 11, 2015, at 4:00 PM, Ben Bolker <bbolker at gmail.com> wrote:
>>
>> Stanislav Aggerwal <stan.aggerwal <at> gmail.com> writes:
>>
>>>
>>> I have the following problem.
>>> DV is binomial p
>>> IV is quantitative variable that goes from negative to positive values.
>>>
>>> The data look like this (need nonproportional font to view):
>>
>>
>> [snip to make gmane happy]
>>
>>> If these data were symmetrical about zero,
>>> I could use abs(IV) and do glm(p
>>> ~ absIV).
>>> I suppose I could fit two glms, one to positive and one to negative IV
>>> values. Seems a rather ugly approach.
>>>
>>
>> [snip]
>>
>>
>> What's wrong with a GLM with quadratic terms in the predictor variable?
>>
>> This is perfectly respectable, well-defined, and easy to implement:
>>
>> glm(y~poly(x,2),family=binomial,data=...)
>>
>> or y~x+I(x^2) or y~poly(x,2,raw=TRUE)
>>
>>> (To complicate things further, this is within-subjects design)
>>
>> glmer, glmmPQL, glmmML, etc. should all support this just fine.
>
>
> As an alternative to Ben's recommendation, consider using a piecewise cubic spline on the IV. This can be done using glm():
>
> # splines is part of the Base R distribution
> # I am using 'df = 5' below, but this can be adjusted up or down as may be apropos
> require(splines)
> glm(DV ~ ns(IV, df = 5), family = binomial, data = YourDataFrame)
>
>
> and as Ben's notes, is more generally supported in mixed models.
>
> If this was not mixed model, another logistic regression implementation is in Frank's rms package on CRAN, using his lrm() instead of glm() and rcs() instead of ns():
>
> # after installing rms from CRAN
> require(rms)
> lrm(DV ~ rcs(IV, 5), data = YourDataFrame)
>
>
> Regards,
>
> Marc Schwartz
>
>
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