Different random intercepts but same random slope for groups

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

  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

[[alternative HTML version deleted]]

-----BEGIN PGP SIGNATURE-----
Version: GnuPG v1.4.11 (GNU/Linux)

iQEcBAEBAgAGBQJVd2PnAAoJEOCV5YRblxUHmRoH/22AjJ5RyFl3YEOeJ4/YerMf
byc8etuVyjX/QpB5VG5XidwdaLCb9tf98SttwR9iWKeYUB6sm+UEtPhzmhNuQVSS
O1aL8XgiTuoK0kNy1W8h7ZJovbaTeEl1FLy2sQ3ma5sRMvxfG30FZXI99NgvdLZf
qhClz3egICgwa7sz4DiT/UwPjvbgY993ZEKhjiWpzu2Zgy6HPVo1GrT/OKEqJwdD
4gczHoAOMkGdc38c+br1AmgPK1Me+Gc/OoZ2gQwniSTpTN0lrluvs3l6Y20/zVjD
0CmvQ+hSKkpPLWRENWGMwBlNpzINXMc+OXcE+LIqaxTuCWFOu9uh78ZjyD8EaHA=
=9aTo
-----END PGP SIGNATURE-----