Nothing
#
# BMI data on twin pairs
#
library(mets)
data(twinbmi)
str(twinbmi)
head(twinbmi)
# restrict to data, where response is not missing
twinbmi <- twinbmi[!is.na(twinbmi$bmi),]
# install.packages("lattice")
library(lattice)
plot( histogram( ~ bmi| gender, type="density", col="red", xlab="kg/m^2",
main="Histogram of BMI", data=twinbmi) )
# BMI is often studied on log-scale.
# boxcox(bmi ~ age*gender, data = twinbmi)
twinbmi$logbmi <- log(twinbmi$bmi)
## Saturated model
a <- twinlm(logbmi~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="sat",control=list(refit=TRUE))
mean(score(a)^2)
aa <- twinlm(logbmi~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="sat",control=list(method="NR",start=coef(a)))
mean(score(aa)^2)
mean((coef(a)-coef(aa))^2)
## Ace model
ace <- twinlm(logbmi~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="ace")
mean(score(ace)^2) ## Convergence?
#
lnbmi.flex <- twinlm(logbmi~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="flex")
lnbmi.flex$estimate$opt$message
mean(score(lnbmi.flex)^2)
compare(a,lnbmi.flex)
#
lnbmi.u <- twinlm(logbmi~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="u")
lnbmi.u$estimate$opt$message
lnbmi.u
cl <- lnbmi.u$call
cl$control <- list(method="NR",start=coef(lnbmi.u))
aa <- eval(cl)
compare(lnbmi.u,lnbmi.flex)
#
lnbmi.ace <- twinlm(logbmi~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="ace")
mean(score(lnbmi.ace)^2)
lnbmi.ace$estimate$opt
lnbmi.ace$estimate$opt$message
lnbmi.ace
#
lnbmi.ade <- twinlm(logbmi~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="ade")
lnbmi.ade$estimate$opt
AIC(lnbmi.ace,lnbmi.ade)
#
lnbmi.ae <- twinlm(logbmi~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="ae",control=list(method="NR"))
lnbmi.ae$estimate$opt$message
lnbmi.ae
compare(lnbmi.ace,lnbmi.ae)
#CE
lnbmi.ce <- twinlm(logbmi~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="ce",control=list(method="NR"))
lnbmi.ce$estimate$opt$message
lnbmi.ce
AIC(lnbmi.ace,lnbmi.ce)
twinbmi$y <- twinbmi$bmi>25
lnbmi.ae <- twinlm(y~age*gender, id="tvparnr", DZ="DZ", zyg="zyg",data=twinbmi,
type="ace",control=list(trace=1))
# GOF-Table?
# mx and openmx for same data
# reshape wide - twin-twin plot.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.