Nothing
#### Saved fits for lme4 testing
#### ----------------------------------
fn <- system.file("testdata", (fn0 <- "lme-tst-fits.rda"),
package="lme4", mustWork=TRUE)
run_Pix_prof <- FALSE
if(FALSE) ### "Load" these by load(fn)
## or "better"
attach(fn)
library(lme4)
str(packageDescription("lme4")[c("Version", "Packaged", "Built")])
## intercept only in both fixed and random effects
fit_sleepstudy_0 <- lmer(Reaction ~ 1 + (1|Subject), sleepstudy)
## fixed slope, intercept-only RE
fit_sleepstudy_1 <- lmer(Reaction ~ Days + (1|Subject), sleepstudy)
## fixed slope, intercept & slope RE
fit_sleepstudy_2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)
## fixed slope, independent intercept & slope RE
fit_sleepstudy_3 <- lmer(Reaction ~ Days + (1|Subject)+ (0+Days|Subject), sleepstudy)
cbpp$obs <- factor(seq(nrow(cbpp)))
## intercept-only fixed effect
fit_cbpp_0 <- glmer(cbind(incidence, size-incidence) ~ 1 + (1|herd),
cbpp, family=binomial)
## include fixed effect of period
fit_cbpp_1 <- update(fit_cbpp_0, . ~ . + period)
## include observation-level RE
fit_cbpp_2 <- update(fit_cbpp_1, . ~ . + (1|obs))
## specify formula by proportion/weights instead
fit_cbpp_3 <- update(fit_cbpp_1, incidence/size ~ period + (1 | herd), weights = size)
fit_penicillin_1 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin)
fit_cake_1 <- lmer(angle ~ temp + recipe + (1 | replicate), data=cake)
## an example with >20 fixed effects (for testing print.summary.merMod)
if (require(agridat)) {
## Define main plot and subplot
d.apple.agridat <- transform(archbold.apple, rep=factor(rep),
spacing=factor(spacing), trt=factor(trt),
mp = factor(paste0(row,spacing)),
sp = factor(paste0(row,spacing,stock)))
fit_agridat_archbold <- lmer(yield ~ -1 + trt + (1|rep/mp/sp), d.apple.agridat)
to.save <- "d.apple.agridat"
} else
to.save <- character()
##
data("Pixel", package="nlme")
fit_Pix.full <- lmer(pixel ~ day + I(day^2) + (day | Dog) + (1 | Side/Dog),
data = Pixel)
fit_Pix.1Dog <- lmer(pixel ~ day + I(day^2) + (1 | Dog) + (1 | Side/Dog),
data = Pixel)
fit_Pix.noD <- update(fit_Pix.1Dog, .~. - (1 | Dog))
anova(fit_Pix.full,
fit_Pix.1Dog,
fit_Pix.noD)
if (run_Pix_prof) {
## Warnings about non-monotonic profile (and more):
options(warn=1) # print as they happen {interspersed in verbose profile() msgs}:
system.time(prof.fit_Pix.f <- profile(fit_Pix.full, verbose=1))
## ~ 90 sec on nb-mm4 [i7-5600U, 2015]
signif(confint(prof.fit_Pix.f), digits=3)
## Results in Nov.2014: -- now .sig03 now shows [-1, 1]
## 2.5 % 97.5 %
## .sig01 10.449 28.909
## .sig02 12.951 48.203
## .sig03 NA NA <<
## .sig04 1.073 3.066
## .sig05 0.000 27.794
## .sigma 7.651 10.592
## (Intercept) NA NA <<
## day NA NA <<
## I(day^2) -0.434 -0.298
try( ## FIXME --> ../../R/profile.R [FIXME: show plots for the *valid* parts!]
lattice::xyplot(prof.fit_Pix.f)
)
## FIXME: Error is ok, but error *message* is unhelpful
## Error in approx(bspl$x, bspl$y, xout = zeta) :
## need at least two non-NA values to interpolate
}
save(list=c(to.save, ls(pattern="fit_")), file=fn0)
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