27 lines
920 B
R
27 lines
920 B
R
library(nlme)
|
|
# data(bdf)
|
|
## Fit the null model
|
|
## Compare with Table 4.1, p. 47
|
|
fm1 <- lme(langPOST ~ 1, data = bdf, random = ~ 1 | schoolNR)
|
|
VarCorr(fm1)
|
|
-2*c(logLik(fm1)) # deviance
|
|
## Fit model with fixed IQ term and random intercept
|
|
## Compare with Table 4.2, p. 49
|
|
## From the results in Tables 4.2 and 4.4, it appears that
|
|
## maximum likelihood fits are used, not REML fits.
|
|
fm2 <- update(fm1, langPOST ~ IQ.ver.cen)
|
|
summary(fm2)
|
|
VarCorr(fm2)
|
|
-2 * c(logLik(fm2)) # deviance
|
|
## Purely fixed-effects model for comparison
|
|
## Compare with Table 4.3, p. 51
|
|
fm3 <- lm(langPOST ~ IQ.ver.cen, data = bdf)
|
|
summary(fm3)
|
|
-2 * c(logLik(fm3)) # deviance
|
|
## Model with average IQ for the school
|
|
## Compare with Table 4.4, p. 55
|
|
fm4 <- update(fm2, langPOST ~ IQ.ver.cen + avg.IQ.ver.cen)
|
|
summary(fm4)
|
|
VarCorr(fm4)
|
|
-2 * c(logLik(fm4)) # deviance
|