[R-sig-ME] [R] Different random intercepts but same random slope for groups

[Ben Bolker]

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  I don't understand your model specification.  What do you mean,
precisely, by "a different random intercept for each group but the
same random slope effect for all three groups"?  That sounds a lot
like a random intercept model? Do you mean that you want to specify a
single random-effects *variance* among slopes for all lots (regardless
of group) but different among-lot intercept variances for each group?

  If group1, group2, group3 are numeric dummy variables, then

lmer(result  ~ group*time+(0+time|lot) + (group1|lot) + (group2|lot) +
(group3|lot), data=alldata)

might work.

  That said, Thierry may still be right.  You have 15 groups and are
trying to fit 3 separate intercept-variance parameters ...


On 15-06-09 04:49 PM, Thierry Onkelinx wrote:
> Your model is too complex for the data. This gives you two options:
> a) simplify the model and b) get more data.
> 
> Best regards,
> 
> ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek /
> Research Institute for Nature and Forest team Biometrie &
> Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat
> 25 1070 Anderlecht Belgium
> 
> To call in the statistician after the experiment is done may be no
> more than asking him to perform a post-mortem examination: he may
> be able to say what the experiment died of. ~ Sir Ronald Aylmer
> Fisher The plural of anecdote is not data. ~ Roger Brinner The
> combination of some data and an aching desire for an answer does
> not ensure that a reasonable answer can be extracted from a given
> body of data. ~ John Tukey
> 
> 2015-06-09 21:57 GMT+02:00 li li <hannah.hlx at gmail.com>:
> 
>> Hi all, I'd like to fit a random intercept and random slope
>> model. In my data, there are three groups. I want to have
>> different random intercept for each group but the same random
>> slope effect for all three groups. I used the following R
>> command. However, there seems to be some problem. Any
>> suggestions?
>> 
>> 
>> 
>> mod2 <- lmer(result  ~ group*time+(0+group1+ group2 + 
>> group3+time|lot), na.action=na.omit, data=alldata)
>> 
>>> summary(mod2)
>> Model is not identifiable... summary from lme4 is returned some
>> computational error has occurred in lmerTest Linear mixed model
>> fit by REML ['merModLmerTest'] Formula: result ~ group * time +
>> (0 + group1 + group2 + group3 + time | lot) Data: alldata
>> 
>> REML criterion at convergence: 807.9
>> 
>> Scaled residuals: Min      1Q  Median      3Q     Max -3.0112
>> -0.3364  0.0425  0.2903  3.2017
>> 
>> Random effects: Groups   Name     Variance Std.Dev. Corr lot
>> group1   0.00000 0.000 group2   86.20156 9.284      NaN group3
>> 55.91479 7.478      NaN  0.06 time      0.02855 0.169      NaN
>> -0.99  0.10 Residual          39.91968 6.318 Number of obs: 119,
>> groups:  lot, 15
>> 
>> Fixed effects: Estimate Std. Error t value (Intercept)
>> 100.1566     2.5108   39.89 group  group2        -2.9707
>> 3.7490   -0.79 group  group3           -0.0717     2.8144
>> -0.03 time                         -0.1346     0.1780   -0.76 
>> group  group2 :time   0.1450     0.2939    0.49 group
>> group3:time        0.1663     0.2152    0.77
>> 
>> Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par,
>> ctrl = control$checkConv,  : Model failed to converge with
>> max|grad| = 0.147314 (tol = 0.002, component 2) 2: In
>> checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>> : Model failed to converge: degenerate  Hessian with 2 negative
>> eigenvalues
>> 
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>> 
> 
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