Dear all,
I am using optim() to estimate unknown parameters by minimizing the
residual sums of squares. I created a function with the model. The model is
working fine. The optim function is producing negative parameter values, even
I have introduced upper and lower bounds (given in code). Therefore,
the model produces *NAs*.
Following is my code.
param <<- c(0.002,0.002, 0.14,0.012,0.01,0.02, 0.03, 0.001)# initial
> parameter values
> opt <- optim(param, fn= f.opt, obsdata =obsdata_10000, method= "L-BFGS-B",
> lower = c(0.001, 0.001, 0.08,0.008, 0.009, 0.008, 0.009, 0.001),
upper = c(0.00375, 0.002, 0.2, 0.018, 0.08, 0.08, 0.08, 0.01),
> control=list(maxit=10), hessian = T)
Error:
*"NAs producedError in if (rnd_1 < liferisk) { : missing value where
TRUE/FALSE needed "*
The model function which produces NA due to negative parameter values
liferisk <- rnorm(n = 1, mean =
(calib_para[which(names(calib_para)=="r_mu")]),sd =
(calib_para[which(names(calib_para)=="r_sd")]))
rnd_1 <- runif(1, 0, 1)
if (rnd_1 < liferisk) { ca_case <- 1} else {ca_case <- 0}
How to design/ modify optim() function, and upper-lower bounds to stop
producing negative values during parameter search?
Thanks
Best regards,
Shah
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