```
y <- rt(100, df=1)
m1<-fitDist(y, type="realline")
m1$fits
m1$failed
# an example of using extra
if (FALSE) {
#---------------------------------------
# Example of using the argument extra
library(gamlss.tr)
data(tensile)
gen.trun(par=1,family="GA", type="right")
gen.trun(par=1,"LOGNO", type="right")
gen.trun(par=c(0,1),"TF", type="both")
ma<-fitDist(str, type="real0to1", trace=T,
extra=c("GAtr", "LOGNOtr", "TFtr"),
data=tensile)
ma$fits
ma$failed
#-------------------------------------
# selecting model using the prediction global deviance
# Using fitDistPred
# creating training data
y <- rt(1000, df=2)
m1 <- fitDist(y, type="realline")
m1$fits
m1$fails
# create validation data
yn <- rt(1000, df=2)
# choose distribution which fits the new data best
p1 <- fitDistPred(y, type="realline", newdata=yn)
p1$fits
p1$failed
#---------------------------------------
# using the function chooseDist()
# fitting normal distribution model
m1 <- gamlss(y~pb(x), sigma.fo=~pb(x), family=NO, data=abdom)
# choose a distribution on the real line
# and save GAIC(k=c(2,4,6.4), i.e. AIC, Chi-square and BIC.
t1 <- chooseDist(m1, type="realline", parallel="snow", ncpus=4)
# the GAIC's
t1
# the distributions which failed are with NA's
# ordering according to BIC
getOrder(t1,3)
fm<-update(m1, family=names(getOrder(t1,3)[1]))
}
```

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