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.
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