Damokun wrote:
>
> Dear all,
>
> I am trying to optimize a logistic function using optim, inside the
> following functions:
> #Estimating a and b from thetas and outcomes by ML
>
> IRT.estimate.abFromThetaX <- function(t, X, inits, lw=c(-Inf,-Inf),
> up=rep(Inf,2)){
> optRes <- optim(inits, method="L-BFGS-B", fn=IRT.llZetaLambdaCorrNan,
> gr=IRT.gradZL,
> lower=lw, upper=up, t=t, X=X)
> c(optRes$par[2], -(optRes$par[1]/optRes$par[2]) )
> }
>
> #Estimating a and b from thetas and outcomes by ML, avoiding 0*log(0)
> IRT.estimate.abFromThetaX2 <- function(tar, Xes, inits, lw=c(-Inf,-Inf),
> up=rep(Inf,2)){
>
> optRes <- optim(inits, method="L-BFGS-B", fn=IRT.llZetaLambdaCorrNan,
> gr=IRT.gradZL,
> lower=lw, upper=up, t=tar, X=Xes)
> c(optRes$par[2], -(optRes$par[1]/optRes$par[2]) )
> }
>
> The problem is that this does not work:
>> IRT.estimate.abFromThetaX(sx, st, c(0,0))
> Error in optim(inits, method = "L-BFGS-B", fn = IRT.llZetaLambdaCorrNan,
> :
> L-BFGS-B needs finite values of 'fn'
> But If I try the same optim call on the command line, with the same data,
> it works fine:
>> optRes <- optim(c(0,0), method="L-BFGS-B", fn=IRT.llZetaLambdaCorrNan,
> + gr=IRT.gradZL,
> + lower=c(-Inf, -Inf), upper=c(Inf, Inf), t=st, X=sx)
>> optRes
> $par
> [1] -0.6975157 0.7944972
> $convergence
> [1] 0
> $message
> [1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
>
In your command line you have set t=st and X=sx.
However in the alternative you do: IRT.estimate.abFromThetaX(sx, st,
c(0,0))
Therefore you are assigning sx to t and st to X in the
IRT.estimate.abFromThetaX function, which is reversed from your command line
call.
You should switch sx and st in the function call:
IRT.estimate.abFromThetaX(st, sx, c(0,0))
and then all will be well.
best
Berend
If yoou
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