# NOT RUN {
#------------- Example 1 from Pinheiro and Bates (2000) page 15-----------------
# bring nlme
library(nlme)
data(ergoStool)
# lme model
l1<-lme(effort~Type, data=ergoStool, random=~1|Subject, method="ML")
# use random()
t1<-gamlss(effort~Type+random(Subject), data=ergoStool )
# use re() with fixed effect within re()
t2<-gamlss(effort~re(fixed=~Type, random=~1|Subject), data=ergoStool )
# use re() with fixed effect in gamlss formula
t3<-gamlss(effort~Type+re(random=~1|Subject), data=ergoStool )
# compare lme fitted values with random
plot(fitted(l1), fitted(t1))
# compare lme fitted values with random
plot(fitted(l1), fitted(t2))
lines(fitted(l1), fitted(t3), col=2)
# getting the fitted coefficients
getSmo(t2)
#-------------------------------------------------------------------------------
# }
# NOT RUN {
#-------------Example 2 Hodges data---------------------------------------------
data(hodges)
plot(prind~state, data=hodges)
m1<- gamlss(prind~random(state), sigma.fo=~random(state), nu.fo=~random(state),
tau.fo=~random(state), family=BCT, data=hodges)
m2<- gamlss(prind~re(random=~1|state), sigma.fo=~re(random=~1|state),
nu.fo=~re(random=~1|state), tau.fo=~re(random=~1|state), family=BCT,
data=hodges)
# comparing the fitted effective degrees of freedom
m1$mu.df
m2$mu.df
m1$sigma.df
m2$sigma.df
m1$nu.df
m2$nu.df
m1$tau.df
m2$tau.df
# random effect for tau is not needed
m3<- gamlss(prind~random(state), sigma.fo=~random(state), nu.fo=~random(state),
family=BCT, data=hodges, start.from=m1)
plot(m3)
# term plots work for random but not at the moment for re()
op <- par(mfrow=c(2,2))
term.plot(m3, se=TRUE)
term.plot(m3, se=TRUE, what="sigma")
term.plot(m3, se=TRUE, what="nu")
par(op)
# getting information from a fitted lme object
coef(getSmo(m2))
ranef(getSmo(m2))
VarCorr(getSmo(m2))
summary(getSmo(m2))
intervals(getSmo(m2))
fitted(getSmo(m2))
fixef(getSmo(m2))
# plotting
plot(getSmo(m2))
qqnorm(getSmo(m2))
#----------------Example 3 from Pinheiro and Bates (2000) page 42---------------
data(Pixel)
l1 <- lme(pixel~ day+I(day^2), data=Pixel, random=list(Dog=~day, Side=~1),
method="ML")
# this will fail
#t1<-gamlss(pixel~re(fixed=~day+I(day^2), random=list(Dog=~day, Side=~1)),
# data=Pixel)
# but this is working
t1<-gamlss(pixel~re(fixed=~day+I(day^2), random=list(Dog=~day, Side=~1),
opt="optim"), data=Pixel)
plot(fitted(l1)~fitted(t1))
#---------------Example 4 from Pinheiro and Bates (2000)page 146----------------
data(Orthodont)
l1 <- lme(distance~ I(age-11), data=Orthodont, random=~I(age-11)|Subject,
method="ML")
t1<-gamlss(distance~I(age-11)+re(random=~I(age-11)|Subject), data=Orthodont)
plot(fitted(l1)~fitted(t1))
# checking the model
plot(t1)
wp(t1, ylim.all=2)
# two observation fat try LO
t2<-gamlss(distance~I(age-11)+re(random=~I(age-11)|Subject, opt="optim",
numIter=100), data=Orthodont, family=LO)
plot(t2)
wp(t2,ylim.all=2)
# a bit better but not satisfactory Note that 3 paramters distibutions fail
#------------example 5 from Venable and Ripley (2002)--------------------------
library(MASS)
data(bacteria)
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
s1 <- gamlss(y ~ trt + I(week > 2)+random(ID), family = BI, data = bacteria)
s2 <- gamlss(y ~ trt + I(week > 2)+re(random=~1|ID), family = BI,
data = bacteria)
s3 <- gamlss(y ~ trt + I(week > 2)+re(random=~1|ID, method="REML"), family = BI,
data = bacteria)
# the esimate of the random effect sd sigma_b
sqrt(getSmo(s1)$tau2)
getSmo(s2)
getSmo(s3)
#-------------Example 6 from Pinheiro and Bates (2000) page 239-244-------------
# using corAR1()
data(Ovary)
# AR1
l1 <- lme(follicles~sin(2*pi*Time)+cos(2*pi*Time), data=Ovary,
random=pdDiag(~sin(2*pi*Time)), correlation=corAR1())
# ARMA
l2 <- lme(follicles~sin(2*pi*Time)+cos(2*pi*Time), data=Ovary,
random=pdDiag(~sin(2*pi*Time)), correlation=corARMA(q=2))
# now gamlss
# AR1
t1 <- gamlss(follicles~re(fixed=~sin(2*pi*Time)+cos(2*pi*Time),
random=pdDiag(~sin(2*pi*Time)),
correlation=corAR1()), data=Ovary)
plot(fitted(l1)~fitted(t1))
# ARMA
t2 <- gamlss(follicles~re(fixed=~sin(2*pi*Time)+cos(2*pi*Time),
random=pdDiag(~sin(2*pi*Time)),
correlation=corARMA(q=2)), data=Ovary)
plot(fitted(l2)~fitted(t2))
AIC(t1,t2)
wp(t2, ylim.all=1)
#-------------------------------------------------------------------------------
# }
Run the code above in your browser using DataLab