rial' being a (one column) matrix. An immediate
> fix is
>
> data_a$ctrial <- as.numeric(data_a$ctrial)
>
> - mgcv 1.8-16 will catch the problem automatically internally.
>
> best,
> Simon
>
> On 20/09/16 17:22, Fotis Fotiadis wrote:
>
>> Hi all
>>
>
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
ackexchange.com or consult
> a local statistician.
>
> Cheers,
> Bert
>
>
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along
> and sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County"
Hi all
I am using bam to analyse the data from my experiment.
It's a learning experiment, "acc" denotes accuracy and "cnd" denotes a
within-subjects variable (with two levels, "label" and "ideo")."Ctrial" is
centered trial (ranging from 1 to 288).
The model is:
bam(acc~ 1 + cnd + s(ctrial) +
gc then you need to set igc to be an ordered factor, and
> use something like...
> ~ igc + s(ctrial) + s(ctrial,by=igc)
> - see section on `by' variables in ?gam.models.
>
> best,
> Simon
>
>
> On 22/05/16 23:29, Fotis Fotiadis wrote:
>
>> Hallo all
>>
>>
Hallo all
I am using a gam model for my data.
m2.4<-bam(acc~ 1 + igc + s(ctrial, by=igc) + shape + s(ctrial, by=shape) +
s(ctrial, sbj, bs = "fs", m = 1) , data=data, family=binomial)
igc codes condition and there are four levels (CAT.pseudo,
CAT.ideo,PA.pseudo, PA.ideo), and shape is a factor
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 usin
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,
: Fotis Fotiadis fotisfotia...@yahoo.gr
Κοιν.: r-help@r-project.org
Ημερομηνία: Τετάρτη, 27 Οκτώβριος 2010, 20:38
It could be more elegant, but I think
this does what you want.
...
lineplot.CI(blck, perf, group = cnd, xlab=Block,
ylab=% Optimal Responses, cex.leg=1.2, x.leg = 18,
y.leg=0.4
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