[R] gam.check() NA results (k-index, p-value) of a gam logistic regression model
Fotis Fotiadis
fotisfotiadis at gmail.com
Wed May 18 14:57:29 CEST 2016
Dear Prof. Wood,
Thank you for your reply!
Best,
Fotis
On Wed, May 18, 2016 at 12:05 PM, Simon Wood <simon.wood at bath.edu> wrote:
> Dear Fotis,
>
> The test is a randomization test, based on comparing differences of
> residuals, ordered with respect to the covariate of the smooth, to
> differences of residuals in randomized order. Random effect terms are
> excluded because there is not basis size to choose. Currently smooths with
> factor by variables are also excluded for reasons of maintainer laziness,
> as this would require special case code to exclude the covariate values
> that are irrelevant given the factor level. Sorry about that.
>
> My guess is that you don't have a problem here anyway, given the fairly
> low edfs relative to the basis dimension. In general as a double check I
> would plot the residuals against ctrial, colour coded by level of igc, just
> to check that there doesn't seem to be missed pattern in them. However with
> binary residuals you are unlikely to see much.
>
> best,
> Simon
>
>
> On 17/05/16 20:39, Fotis Fotiadis wrote:
>
>> Hello all
>>
>> I am using bam for a mixec-effects logistic regression model:
>>
>> b0<-bam(acc~ 1 + igc + s(ctrial, by=igc) + s(sbj, bs="re") + s(ctrial,
>> sbj,
>> bs="re") , data=data, family=binomial)
>>
>> summary(b0)
>>>
>> Family: binomial
>> Link function: logit
>>
>> Formula:
>> acc ~ 1 + igc + s(ctrial, by = igc) + s(sbj, bs = "re") + s(ctrial,
>> sbj, bs = "re")
>>
>> Parametric coefficients:
>> Estimate Std. Error z value Pr(>|z|)
>> (Intercept) 2.8334 0.2030 13.955 < 2e-16 ***
>> igcPA.pseudo 0.4692 0.1285 3.650 0.000262 ***
>> igcCAT.ideo 0.3276 0.2906 1.127 0.259734
>> igcCAT.pseudo 0.6701 0.2945 2.275 0.022888 *
>> ---
>> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>> Approximate significance of smooth terms:
>> edf Ref.df Chi.sq p-value
>> s(ctrial):igcPA.ideo 3.827 4.733 295.0 < 2e-16 ***
>> s(ctrial):igcPA.pseudo 3.317 4.110 356.1 < 2e-16 ***
>> s(ctrial):igcCAT.ideo 3.979 4.911 308.6 < 2e-16 ***
>> s(ctrial):igcCAT.pseudo 4.937 5.974 383.8 < 2e-16 ***
>> s(sbj) 54.326 62.000 3032.8 < 2e-16 ***
>> s(ctrial,sbj) 43.045 62.000 2706.6 1.31e-08 ***
>> ---
>> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>> R-sq.(adj) = 0.362 Deviance explained = 38.9%
>> fREML = 25436 Scale est. = 1 n = 18417
>>
>>
>> I want to know if the wigglyness of the smooths [s(ctrial, by=igc)] is
>> appropriate, so I used the gam.check() function. The values though for
>> k-index and p-value are NAs:
>>
>> gam.check(b0)
>>>
>> Method: fREML Optimizer: perf newton
>> full convergence after 5 iterations.
>> Gradient range [-7.60152e-08,8.12795e-06]
>> (score 25436.12 & scale 1).
>> Hessian positive definite, eigenvalue range [0.6271375,24.46625].
>> Model rank = 168 / 168
>>
>> Basis dimension (k) checking results. Low p-value (k-index<1) may
>> indicate that k is too low, especially if edf is close to k'.
>>
>> k' edf k-index p-value
>> s(ctrial):igcPA.ideo 9.00 3.83 NA NA
>> s(ctrial):igcPA.pseudo 9.00 3.32 NA NA
>> s(ctrial):igcCAT.ideo 9.00 3.98 NA NA
>> s(ctrial):igcCAT.pseudo 9.00 4.94 NA NA
>> s(sbj) 64.00 54.33 NA NA
>> s(ctrial,sbj) 64.00 43.04 NA NA
>>
>> Does anyone know why is this?
>>
>> Thank you in advance for your time,
>> Fotis
>>
>> P.S. I am using RStudio Version 0.99.896, R 3.3.0, and mgcv package
>> version
>> 1.8.12.
>> --
>> PhD Candidate
>> Department of Philosophy and History of Science
>> University of Athens, Greece.
>> http://users.uoa.gr/~aprotopapas/LLL/en/members.html#fotisfotiadis
>>
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>
>
> --
> Simon Wood, School of Mathematics, University of Bristol BS8 1TW UK
> +44 (0)117 33 18273 http://www.maths.bris.ac.uk/~sw15190
>
>
--
PhD Candidate
Department of Philosophy and History of Science
University of Athens, Greece.
http://users.uoa.gr/~aprotopapas/LLL/en/members.html#fotisfotiadis
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