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