Hi R-users,
>From the last week I've been working fitting a linear mixed model with
random intercept and fixed shape (model4) for a data set with 37 individuals
measured over time, using lme package. Results are at the end of this
message. The outcome is score and the covariate is age.
My question is: is possible (and how) to estimate both 95% confidence and
prediction intervals for the mixed model as in linear regression models?
I've tried this using predict(modelc,interval="confidence",level=0.95) and
predict(modelc,interval="prediction",level=0.95) but the result is always
the same given by fitted(model4).
Thanks in advance,
Jorge Velez
# Results for the lme model
Linear mixed-effects model fit by maximum likelihood
Data: NPC.n
AIC BIC logLik
805.3955 822.3171 -396.6977
Random effects:
Formula: ~1 + age | subject
Structure: General positive-definite, Log-Cholesky parametrization
StdDev Corr
(Intercept) 13.1125118 (Intr)
age 0.7672007 -0.087
Residual 2.5729797
Fixed effects: total ~ age
Value Std.Error DF t-value p-value
(Intercept) -10.658901 2.731439 86 -3.902303 2e-04
age 1.872654 0.178246 86 10.506009 0e+00
Correlation:
(Intr)
age -0.421
Standardized Within-Group Residuals:
Min Q1 Med Q3 Max
-2.04501756 -0.42833252 -0.01521936 0.30176415 2.16862549
Number of Observations: 124
Number of Groups: 37
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