Constraining error variance to 0 in an lmer() model.

On 25/03/18 12:40, Rolf Turner wrote:

<SNIP>

The model that I wanted to fit was (adapting Maarten Jung's notation
slightly to suit my own tastes):

     (*) lmer(test ~ 0+person + (0+person | occasion),data=Dat)

<SNIP>

Berwin Turlach has just pointed out to me that I got the preceding
expression arse-backwards, as is so often my propensity.  It should
be:

     (*) lmer(test ~ 0+occasion + (0+occasion | person),data=Dat)

The fixed effect is *occasion* and the random effect is *person*.

Likewise

     lmer(test ~ 0+person + (0+person | occasion),data=Dat,
                 control=lmerControl(check.nobs.vs.nRE = "ignore"))

should be

       lmer(test ~ 0+occasion + (0+occasion | person),data=Dat,
                   control=lmerControl(check.nobs.vs.nRE = "ignore"))

Psigh!  Sorry for the confusion, everybody.

<SNIP>

cheers,

Rolf Turner

P.S.  Berwin also asked me to provide the data I used.  I simply took
the data provided by Ben Pelzer, in a posting in the relevant thread,
just before the posting in which Maarten Jung explained how to get the
fit to run, by setting the appropriate control variable.

For the record, Ben Pelzer provided the data through a URL:

Dat <-
read.table(url("

https://surfdrive.surf.nl/files/index.php/s/XfE3mtbFCTUejIz/download"),

header=TRUE)

(Actually Ben Pelzer wrote "mydata" where I have written "Dat" above.  I
*refuse* to use this "mydata" construction since it is *so*
Micro$oft! :-) )

R. T.

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