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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