Message: 36
Date: Tue, 13 Aug 2013 10:38:05 +0200
From: Carlos Nasher<carlos.nas...@googlemail.com>
To:r-help@r-project.org
Subject: [R] [optim/bbmle] function returns NA at ... distance from x
Message-ID:
<CAP=bvwpxj991fbyt9ou5x1jf9nol3vtq1svtjvw82jwfjyz...@mail.gmail.com>
Content-Type: text/plain
Dear R helpers,
I try to find the model parameters using mle2 (bbmle package). As I try to
optimize the likelihood function the following error message occurs:
Error in grad.default(objectivefunction, coef) :
function returns NA at
1e-040.001013016911639890.0003166929388711890.000935163594829395 distance
from x.
In addition: Warning message:
In optimx(par = c(0.5, 10, 0.7, 10), fn = function (p) :
Gradient not computable after method Nelder-Mead
I can't figure out what that means exactly and how to fix it. I understand
that mle2 uses optim (or in my case optimx) to optimize the likelihood
function. As I use the Nelder-Mead method it should not be a problem if the
function returns NA at any iteration (as long as the initial values don't
return NA). Can anyone help me with that?
Here a small example of my code that reproduces the problem:
library(plyr)
library(optimx)
### Sample data ###
x <- c(1,1,4,2,3,0,1,6,0,0)
tx <- c(30.14, 5.14, 24.43, 10.57, 25.71, 0.00, 14.14, 32.86, 0.00, 0.00)
T <- c(32.57, 29.14, 33.57, 34.71, 27.71, 38.14, 36.57, 37.71, 35.86, 30.57)
data <- data.frame(x=x, tx=tx, T=T)
### Likelihood function ###
Likelihood <- function(data, r, alpha, s, beta) {
with(data, {
if (r<=0 | alpha<=0 | s<=0 | beta<=0) return (NaN)
f <- function(x, tx, T)
{
g <- function(y)
(y + alpha)^(-( r + x))*(y + beta)^(-(s + 1))
integrate(g, tx, T)$value
}
integral <- mdply(data, f)
L <-
exp(lgamma(r+x)-lgamma(r)+r*(log(alpha)-log(alpha+T))-x*log(alpha+T)+s*(log(beta)-log(beta+T)))+exp(lgamma(r+x)-lgamma(r)+r*log(alpha)+log(s)+s*log(beta)+log(integral$V1))
f <- sum(log(L))
return (f)
})
}
### ML estimation function ###
Estimate_parameters_MLE <- function(data, initValues) {
llhd <- function(r, alpha, s, beta) {
return (Likelihood(data, r, alpha, s, beta))
}
library(bbmle)
fit <- mle2(llhd, initValues, skip.hessian=TRUE, optimizer="optimx",
method="Nelder-Mead", control=list(maxit=1e8))
return (fit)
}
### Parameter estimation ###
Likelihood(data=data, r=0.5, alpha=10, s=0.7, beta=10) ### check initial
parameters --> -72.75183 --> initial parameters do return value
MLE_estimation <- Estimate_parameters_MLE(data=data, list(r=0.5, alpha=10,
s=0.7, beta=10))
'Error in grad.default(objectivefunction, coef) :
function returns NA at
1e-040.001013016911639890.0003166929388711890.000935163594829395 distance
from x.
In addition: Warning message:
In optimx(par = c(0.5, 10, 0.7, 10), fn = function (p) :
Gradient not computable after method Nelder-Mead'
Best regards,
Carlos
-----------------------------------------------------------------
Carlos Nasher
Buchenstr. 12
22299 Hamburg
tel: +49 (0)40 67952962
mobil: +49 (0)175 9386725
mail:carlos.nas...@gmail.com
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