Sorry to report further difficulties with
nls and profiling and constraints ... the problem
this time (which I didn't check for in my last
round of testing) is that the nls profiler doesn't
seem to respect constraints that have been
set when using the port algorithm.
See test code below ...
If I can I will try to hack the code, but I will
probably start by redefining my function with
some workarounds to make the fit quadratically "bad" (but
well-defined)
when the parameters are negative ...
As always, please don't hesitate to correct me
if I'm being an idiot ...
cheers
Ben Bolker
-----------------------
rm(list=ls())
npts=10
set.seed(1001)
a =2
b =0.5
x= runif(npts)
y = a*x/(1+a*b*x)+rnorm(npts,sd=0.2)
gfun <- function(a,b,x) {
if (a<0 || b<0) stop("bounds violated")
a*x/(1+a*b*x)
}
m1 = nls(y~gfun(a,b,x),algorithm="port",
lower=c(0,0),start=c(a=1,b=1))
try(confint(m1))
----------------
for what it's worth, the logic appears to be OK in mle in the stats4
library:
--------------
library(stats4)
mfun <- function(a,b,s) {
if (a<0 || b<0 || s<0) stop("bounds violated")
-sum(dnorm(y,a*x/(1+a*b*x),sd=s,log=TRUE))
}
m2 = mle(minuslogl=mfun,
start=list(a=1,b=1,s=0.1),
method="L-BFGS-B",lower=c(0.002,0.002,0.002))
confint(m2)
m2b = mle(minuslogl=mfun,
fixed=list(b=0),start=list(a=1,s=0.1),
method="L-BFGS-B",lower=c(0.002,0.002,0.002))
## set boundary slightly above zero to avoid
## boundary cases
dev <- 2*(-logLik(m2b)+logLik(m2))
as.numeric(pchisq(dev,lower.tail=FALSE,df=1))
--
620B Bartram Hall bolker at zoo.ufl.edu
Zoology Department, University of Florida http://www.zoo.ufl.edu/bolker
Box 118525 (ph) 352-392-5697
Gainesville, FL 32611-8525 (fax) 352-392-3704
