NLMIXED in R

NLIN for grouping - Diagnostics

#### NLIN - Use identification variable to already look into NLMixed
# Simple model, ignoring groups
try1<-nls(y~f1(x,U,D,Kd), data=(Grass[complete.cases(Grass$y),]),start=list(U=19.7,D=52,Kd=0.035))
summary(try1)

# Full model with different parameters for the two groups (which makes it the larger model, because of more parameters)
try2<-nls(y~f1(x,
               U[Standard.sample2],
               D[Standard.sample2],
               Kd[Standard.sample2]), 
          data=(Grass[complete.cases(Grass$y),]),
          start=list(U=rep(19.7,2),D=rep(52,2),Kd=rep(0.035,2)))
summary(try2)

# Assume Kd similar across groups 
try3<-nls(y~f1(x,
               U[Standard.sample2],
               D[Standard.sample2],
               Kd), 
          data=(Grass[complete.cases(Grass$y),]),
          start=list(U=rep(19.7,2),D=rep(52,2),Kd=0.035))

# Residual plots 
p1<-plot(try1);p2<-plot(try2);p3<-plot(try3); grid.arrange(p1,p2,p3,ncol=3) 
p1<-qqnorm(try1);p2<-qqnorm(try2);p3<-qqnorm(try3); grid.arrange(p1,p2,p3,ncol=3)
nlstools::overview(try3) # shows t based confidence intervals 
confint(try3) # better confidence intervals then provided via overview (uses profiling)
confint.default(try3)
nlstools::plotfit(try1, smooth=TRUE)
try3.resid<-nlstools::nlsResiduals(try3)
plot(try3.resid)
plot(try3)
qqnorm(try3)
head(residuals(try3)[-1])
head(residuals(try3.resid))
plot(residuals(try3), c(residuals(try3)[-1],NA))
plot(try3.resid,which=4)