34 lines
1.2 KiB
R
34 lines
1.2 KiB
R
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library(nlme)
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## ==> ~/R/Pkgs/MASS_CRAN/tests/lme.R : tests stepAIC() for *both* lme and gls
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if (!require(MASS)) q("no")
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## deviance.lme() and extractAIC.lme() :
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set.seed(321) # to be sure
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a <- data.frame( resp=rnorm(250), cov1=rnorm(250),
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cov2=rnorm(250), group=rep(letters[1:10],25) )
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mod1 <- lme(resp~cov1, a, ~cov1|group, method="ML")
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mod2 <- stepAIC(mod1, scope = list(upper=~(cov1+cov2)^2, lower=~1) )
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stopifnot(all.equal(logLik(mod1), logLik(mod2)),
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all.equal( coef(mod1), coef(mod2)),
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all.equal(logLik(mod2),
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structure(-344.190316608, class = "logLik",
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nall = 250L, nobs = 250, df = 6)))
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## deviance.gls() and extractAIC.gls() :
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data(beav2, package = "MASS")
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set.seed(123)
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beav <- beav2; beav$dummy <- rnorm(nrow(beav))
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beav.gls <- gls(temp ~ activ + dummy, data = beav,
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corr = corAR1(0.8), method = "ML")
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stopifnot(all.equal(sigma(beav.gls), 0.2516395, tol = 1e-6),
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all.equal(coef(beav.gls),
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c("(Intercept)" = 37.191057,
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activ = 0.61602059, dummy =0.006842112)))
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st.beav <- stepAIC(beav.gls)
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stopifnot(all.equal(coef(st.beav),
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c("(Intercept)" = 37.1919453124,
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"activ" = 0.614177660082)),
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all.equal(sigma(st.beav), 0.2527856, tol = 1e-6))
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