mysterious syntax error in call to nlmer

Jacob Wegelin <jacobwegelin at ...> writes:


?Thank you for your very clear and reproducible example!

I'm trying to fit my first nlmer model. I follow Doug Bates's code
at
http://lme4.r-forge.r-project.org/slides/2009-07-01-Lausanne/8NLMMD.pdf
and use lme4_0.999375-39. ?A call to nls works fine, but when I turn
it into a call to nlmer and add the random component, errors are
returned. Providing start as a named vector returns one kind of
error; providing start as a list returns a different error. ?My data
are simulated but they contain noise (per the warning on the nlmer
help page). ?You can see a plot of the data at
http://jacobwegelin.net/r-help/2011_03_27_1311_simulated.pdf. ?The
error messages are as follows. Code to reproduce the entire problem
is below under Details.

set.seed(1)
N<-100; nIDs<-20
X<-seq(1,19, length=N)
Y<-sin(X) + rnorm(N)/10
IDs<-1:nIDs

## I have a silly tangential question. ?Why put the commas
## ?on the next line? ?I haven't seen this convention much before,
## ?but it has started appearing on this list. ?In general it
## ?makes much more sense to me (and occasionally makes a
## ?difference) to put connectors (commas, arithmetic operators etc.)
## ?at the end of the previous line rather than the beginning of
## ?the next ...

trueRandomEffects<-data.frame(
? ? ? ?xintercept=rnorm(nIDs)/3
? ? ? ?, amplitude=1+ rnorm(nIDs)/9
? ? ? ?, frequency =1+rnorm(nIDs)/10
? ? ? ?, xshift =runif(nIDs) * pi
? ? ? ?)
rownames( trueRandomEffects)<- IDs
mdoot<- DAT<-data.frame(ID=IDs[1], X=X, Y=Y)
for(thisperson in IDs[-1]) {
? ? ? ?nextOne<-DAT
? ? ? ?nextOne$Y<- ?(
? ? ? ? ? ? ? ? ? ? trueRandomEffects[thisperson,"xintercept"]
? ? ? ? ? ? ? ?+ trueRandomEffects[thisperson,"amplitude"]
? ? ? ? ? ? ? ?* sin(
? ? ? ? ? ? ? ?trueRandomEffects[thisperson,"frequency"] ?* X
? ? ? ? ? ? ? ?- trueRandomEffects[thisperson,"xshift"]
? ? ? ? ? ? ? ?)
? ? ? ? ? ? ? ?+ rnorm(N)/10
? ? ? ? ? ? ? ? ? ? ? ?)
? ? ? ?nextOne$ID<- thisperson
? ? ? ?mdoot<-rbind( mdoot, nextOne)
}
attr(mdoot, "ranef")<-trueRandomEffects

require(ggplot2)
print(
ggplot(data=mdoot, mapping=aes(x=X, y=Y, group=ID, color=ID))
+ geom_path())

whump<-nls(
? ? ? ?Y ~ xintercept + amplitude * sin( frequency * X - xshift),
?data=mdoot,
?start=list(xintercept=1, amplitude=1.5, frequency=1, xshift=pi/2)
? ? ? ? ? ? ? ?)
print(whump)

print(summary(whump))
mdoot$nlsfit<-predict(whump)
print(head(mdoot))

print(
ggplot(data=mdoot, mapping=aes(x=X, y=Y, group=ID, color=ID)) +
?geom_path() +
geom_point(mapping=aes(x=X, y=nlsfit))
)

require(lme4)

## at the moment nlmer assumes that the objective function has
## ?a gradient attribute associated. ?*If* you have a simple
## ?objective function (i.e. one that R's built-in deriv()
## ?function can handle), this is simple to address

parnames <- c("xintercept","amplitude","frequency","xshift")
dd <- deriv(expression(xintercept + amplitude *
? ? ? ?sin( frequency * X - xshift)),
? ?namevec=parnames, function.arg=c(parnames,"X"))

roop <-nlmer(
? ? ? ? ? ? Y ~ dd(xintercept,amplitude,frequency,xshift,X)~xshift|ID,
? ? ? ? ? ? data=mdoot,
? ? ? ? ? ? start=list(fixef=c(xintercept=1, amplitude=1.5,
? ? ? ? ? ? ? ? ? ? frequency=1, xshift=pi/2))
? ? ? ? ? ? )
summary(roop)
detach("package:lme4")

## however, you can also do this (possibly better?)
## with nlme:

library(nlme)


roop2 <-nlme( ?Y ~ dd(xintercept,amplitude,frequency,xshift,X),
? ? ? ? ? ? ?data=mdoot,
? ? ? ? ? ? fixed = xintercept + amplitude + frequency + xshift ~ 1,
? ? ? ? ? ? random = xshift~1|ID,
? ? ? ? ? ? ?start=c(xintercept=1, amplitude=1.5,
? ? ? ? ? ? ? ? ? ? ? ? ? ?frequency=1, xshift=pi/2)
? ? ? ? ? ? )

summary(roop2)

## it's a little bit alarming that nlme finds a non-zero xshift
## variance while nlmer doesn't ...

roop <-nlmer(