Class: LinearMixedModel
Compare linear mixed-effects models
returns the results of a likelihood ratio
test that compares the linear mixed-effects models
results
= compare(lme
,altlme
)lme
and altlme
. Both models must use
the same response vector in the fit and lme
must be nested in
altlme
for a valid theoretical likelihood ratio test.
Always input the smaller model first, and the larger model second.
compare
tests the following null and alternate
hypotheses:
H0: Observed response vector is
generated by lme
.
H1: Observed response vector is
generated by model altlme
.
It is recommended that you fit lme
and
altlme
using the maximum likelihood (ML) method prior to
model comparison. If you use the restricted maximum likelihood (REML) method,
then both models must have the same fixed-effects design matrix.
To test for fixed effects, use compare
with the simulated likelihood ratio
test when lme
and altlme
are
fit using ML or use the fixedEffects
,
anova
, or coefTest
methods.
also returns the results of a likelihood ratio test that compares linear
mixed-effects models results
= compare(___,Name,Value
)lme
and altlme
with
additional options specified by one or more Name,Value
pair
arguments.
For example, you can check if the first input model is nested in the second input model.
[
returns the results of a simulated likelihood ratio test that compares linear
mixed-effects models results
,siminfo
]
= compare(lme
,altlme
,'NSim',nsim
)lme
and
altlme
.
You can fit lme
and altlme
using ML or
REML. Also, lme
does not have to be nested in
altlme
. If you use the restricted maximum likelihood
(REML) method to fit the models, then both models must have the same
fixed-effects design matrix.
[
also returns the results of a simulated likelihood ratio test that compares
linear mixed-effects models results
,siminfo
]
= compare(___,Name,Value
)lme
and altlme
with additional options specified by one or more Name,Value
pair arguments.
For example, you can change the options for performing the simulated likelihood ratio test, or change the confidence level of the confidence interval for the p-value.
[1] Hox, J. Multilevel Analysis, Techniques and Applications. Lawrence Erlbaum Associates, Inc., 2002.
[2] Stram D. O. and J. W. Lee. “Variance components testing in the longitudinal mixed-effects model”. Biometrics, Vol. 50, 4, 1994, pp. 1171–1177.
anova
| covarianceParameters
| fitlme
| fitlmematrix
| fixedEffects
| LinearMixedModel
| randomEffects