# [R] About object of class mle returned by user defined functions

From: Christophe Pouzat <christophe.pouzat_at_univ-paris5.fr>
Date: Fri 22 Jul 2005 - 02:01:36 EST

Hi,

I've written a short function (see below) whose arguments are: 1) A univariate sample (arising from a gamma, log-normal or whatever). 2) A character string standing for one of the R densities, eg, "gamma",
"lnorm", etc. That's the density the user wants to fit to the data.
3) A named list with initial values for the density parameters; that will be passed to optim via mle.
4) The method to be used by optim via mle. That can be change by the code if parameter boundaries are also supplied. 5) The lowest allowed values for the parameters. 6) The largest allowed values.

The "big" thing this short function does is writing on-fly the corresponding log-likelihood function before calling "mle". The object of class "mle" returned by the call to "mle" is itself returned by the function.

Here is the code:

newFit <- function(isi, ## The data set

isi.density = "gamma", ## The name of the density
used as model
initial.para = list( shape = (mean(isi)/sd(isi))^2,
scale = sd(isi)^2 / mean(isi) ), ## Inital
parameters passed to optim
optim.method = "BFGS", ## optim method
optim.lower = numeric(length(initial.para)) + 0.00001,
optim.upper = numeric(length(initial.para)) + Inf,
...) {

require(stats4)

## Create a string with the log likelihood definition   minusLogLikelihood.txt <- paste("function( ",

paste(names(initial.para), collapse =

", "),
" ) {", "isi <- eval(", deparse(substitute(isi)), ", envir = .GlobalEnv);", "-sum(", paste("d", isi.density, sep = ""), "(isi, ", paste(names(initial.para), collapse =
", "),
", log = TRUE) ) }" )

## Define logLikelihood function
minusLogLikelihood <- eval( parse(text = minusLogLikelihood.txt) )   environment(minusLogLikelihood) <- .GlobalEnv

if ( all( is.infinite( c(optim.lower,optim.upper) ) ) ) {

getFit <- mle(minusLogLikelihood,
start = initial.para,
method = optim.method,
...
)

} else {
getFit <- mle(minusLogLikelihood,
start = initial.para,
method = "L-BFGS-B",
lower = optim.lower,
upper = optim.upper,
...
)

} ## End of conditional on all(is.infinite(c(optim.lower,optim.upper)))

getFit

}

It seems to work fine on examples like:

> isi1 <- rgamma(100, shape = 2, scale = 1)  > fit1 <- newFit(isi1) ## fitting here with the "correct" density (initial parameters are obtained by the method of moments)  > coef(fit1)

shape scale
1.8210477 0.9514774
> vcov(fit1)

shape scale
shape 0.05650600 0.02952371
scale 0.02952371 0.02039714
> logLik(fit1)
'log Lik.' -155.9232 (df=2)

If we compare with a "direct" call to "mle":

> llgamma <- function(sh, sc) -sum(dgamma(isi1, shape = sh, scale = sc, log = TRUE))
> fitA <- mle(llgamma, start = list( sh = (mean(isi1)/sd(isi1))^2, sc = sd(isi1)^2 / mean(isi1) ),lower = c(0.0001,0.0001), method = "L-BFGS-B")  > coef(fitA)

sh sc
1.821042 1.051001
> vcov(fitA)

sh sc
sh 0.05650526 -0.03261146
sc -0.03261146 0.02488714
> logLik(fitA)
'log Lik.' -155.9232 (df=2)

I get almost the same estimated parameter values, same log-likelihood but not the same vcov matrix.

A call to "profile" or "confint" on fit1 does not work, eg:  > confint(fit1)
Profiling...

Erreur dans approx(sp\$y, sp\$x, xout = cutoff) :

need at least two non-NA values to interpolate De plus : Message d'avis :
collapsing to unique 'x' values in: approx(sp\$y, sp\$x, xout = cutoff)

Although calling the log-likelihood function defined in fit1 (fit1@minuslogl) with argument values different from the MLE does return something sensible:

> fit1@minuslogl(coef(fit1)[1],coef(fit1)[2]) [1] 155.9232
> fit1@minuslogl(coef(fit1)[1]+0.01,coef(fit1)[2]+0.01) [1] 155.9263

There is obviously something I'm missing here since I thought for a while that the problem was with the environment "attached" to the function "minusLogLikelihood" when calling "eval"; but the lines above make me think it is not the case...

Any help and/or ideas warmly welcomed.

Thanks,

Christophe.

--
A Master Carpenter has many tools and is expert with most of them.If you
only know how to use a hammer, every problem starts to look like a nail.
Stay away from that trap.
Richard B Johnson.
--

Christophe Pouzat
Laboratoire de Physiologie Cerebrale
CNRS UMR 8118
UFR biomedicale de l'Universite Paris V
45, rue des Saints Peres
75006 PARIS
France

tel: +33 (0)1 42 86 38 28
fax: +33 (0)1 42 86 38 30
web: www.biomedicale.univ-paris5.fr/physcerv/C_Pouzat.html

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Received on Fri Jul 22 02:28:02 2005

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