Any chance you could send me the data and exact code that produces this (I'll only use the data for investigating this issue of course - often data with the predictor replaced by noise will produce the same error, if sending the raw data is a problem)?
best, Simon (mgcv maintainer)

On 20/09/16 17:22, Fotis Fotiadis wrote:
Hi all

I am using the bam function of the mgcv package to model behavioral data of
a learning experiment. To model individual variation in learning rate, I am
testing models with (a) by-participant random intercepts of trial, (b)
by-participant random slopes and random intercepts of trial, and (c)
by-participant random smooth terms.

While all (a) and (c) models converge, I am getting an error for every
possible variation of a model with random intercepts and random slopes. For
example:<-bam(acc~ 1 + igc + s(ctrial) + s(sbj, bs="re") + s(ctrial, sbj,
bs="re") , data=data_a, family=binomial)
Error in G$smooth[[i]]$first.para:G$smooth[[i]]$last.para :
   argument of length 0

Any idea on what that error might be?

Thank you in advance for your time.

P.S.: R version: 3.3.1, mgcv version: 1.8.15

Simon Wood, School of Mathematics, University of Bristol BS8 1TW UK
+44 (0)117 33 18273

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