On 8/31/23 1:27 AM, Spencer Graves wrote:
Hello, All:
I want to simulate future observations from fits to
heteroscedastic data. A simple example is as follows:
(DF3_2 <- data.frame(y=c(1:3, 10*(1:3)),
gp=factor(rep(1:2, e=3))))
# I want to fit 4 models
# and simulate future observations from all 4:
fit11 <- lm(y~1, DF3_2)
fit21 <- lm(y~gp, DF3_2)
library(nlme)
(fit12 <- lme(y~1, data=DF3_2, random=~1|gp))
(fit22 <- lme(y~gp, data=DF3_2, random=~1|gp))
library(lme4)
(fit12r <- lmer(y~1+(1|gp), data=DF3_2, REML=FALSE))
(fit22r <- lmer(y~gp+(1|gp), data=DF3_2, REML=FALSE))
# I can simulate what I want for fit11 and fit21
# as follows:
simPred <- function(object, nSims=2){
pred <- predict(object, DF3_2[6,], se.fit=TRUE,
interval='prediction')
with(pred, fit[1, 'fit'] +
se.fit*rt(nSims, df))
}
simPred(fit11)
simPred(fit21)
# How can I do the same with either fit12 and fit22
# or fit12r and fit22r?
I think I found it:
simPred4 <- function(object, nSims=2){
# class(object) = lmeeMod
sim <- simulate(object, nsim=nSims,
newdata=DF3_2[6,])
sim
}
simPred4(fit12r)
simPred4(fit22r)
Thanks,
Spencer Graves
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______________________________________________
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https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
