Different random intercepts but same random slope for groups

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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