fitmzsm <- function(x, trunc, start.value, upper = length(x), ...){
dots <- list(...)
if (any(x <= 0) | any(!is.wholenumber(x))) stop ("All x must be positive integers")
if(sum(x)<100) warning("\n small sample size (J<100); \n mzsm may not be a good approximation")
if (!missing(trunc)){
if (min(x)<=trunc) stop("truncation point should be lower than the lowest data value")
}
if(missing(start.value)){
thetahat <- length(x)
}
else{
thetahat <- start.value
}
if (missing(trunc)){
LL <- function(J, theta) -sum(dmzsm(x, J=J, theta = theta, log = TRUE))
}
else
{
LL <- function(J, theta) -sum(dtrunc("mzsm", x=x, coef = list(J = J, theta = theta), trunc = trunc, log = TRUE))
}
result <- do.call("mle2", c(list(LL, start = list(theta = thetahat), fixed=list(J=sum(x)), data = list(x = x), method ="Brent", lower=0.001, upper=upper), dots))
if(abs(as.numeric(result@coef) - upper) < 0.0000001) warning("mle equal to upper bound provided. \n Try value for the 'upper' arguent")
new("fitsad", result, sad="mzsm", distr = distr.depr, trunc = ifelse(missing(trunc), NaN, trunc))
}
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