I am trying to generalize a working piece of code for a single parameter to a
multiple parameter problem. Reproducible code is below. The parameters to be
estimated are a, b, and c. The estimation problem is such that there is one set
of a, b, c parameters for each column of the data. Hence, in this sample data
with 20 columns, there are 20 a params, 20 b-params, and 20 c-params.
Because I am estimating so many parameters, I am not certain that I have
indicated to the function properly the right number of params to estimate and
also if I have generated starting values in a sufficient way.
Thanks for any help.
Harold
dat <- replicate(20, sample(c(0,1), 2000, replace = T))
library(stat mod)
qq <- gauss.quad.prob(Q, dist = 'normal', mu = 0, sigma = 1)
nds <- qq$nodes
wts <- qq$weights
fn <- function(params){
a <- params[1:ncol(dat)]
b <- params[1:ncol(dat)]
c <- params[1:ncol(dat)]
L <- sapply(1:ncol(dat), function(i) dbinom(dat[,i], 1, c + ((1 - c)/(1 +
exp(-1.7 * a * (nds[i] - b)))) * wts[i]))
r1 <- prod(colSums(L * wts))
-log(r1)
}
startVal <- rep(.5, ncol(dat))
opt <- optim(startVal, fn)
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