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

[Thierry Onkelinx]

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 op 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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