problem with MLE estimation using Kalman filter

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problem with MLE estimation using Kalman filter

 Hello, I've recently run into a problem when trying to do some MLE parameter estimation in R using a non-linear Kalman filter.  I've written some of the functions myself and everything seems to be working fine until the end when I try to use the mle2 function written for R.  The error message states that the argument "minuslog1" is missing with no default, but I've specified this argument using my written function. Any help with this would much appreciated. ##script that codes for a non-linear Kalman filter #simulate starting conditions and data N.obs=seq(from=5,to=200,by=5) y.procobs2=Nobs #write the function called "nlkfpred" to predict the mean and variance of #each observed value; Nobs is the data set nlkfpred=function(r,K,procvar,obsvar,M.n.start,Var.n.start,Nobs){ nt = length(Nobs) M.nobs = numeric(nt) Var.nobs = numeric(nt) M.n = M.n.start Var.n = Var.n.start M.nobs[1] = M.n.start Var.nobs[1] = Var.n.start+obsvar for (t in 2:nt) { M.ni = M.n+r*M.n(1-M.n/K) b = 1+r-2*r*M.n/K Var.ni = b^2*Var.n+procvar M.nobs[t] = M.ni Var.nobs[t] = Var.ni+obsvar M.n = M.ni+(Var.ni/Var.nobs[t])*(Nobs[t]-M.nobs[t]) Var.n = Var.ni*(1-(Var.ni/Var.nobs[t])) } list(mean = M.nobs,var = Var.nobs) } ##write the function called "nlkflik" to make a normal likelihood comparison ##using log values of the parameters so that we don't have to worry about ##negative parameter values; "obs.data" is an observed data set nlkflik = function(logr,logK,logprocvar,logobsvar,logM.n.start,logVar.n.start, obs.data){ pred = nlkfpred(r = exp(logr),K = exp(logK),procvar = exp(logprocvar), obsvar = exp(logobsvar),M.n.start = exp(logM.n.start),Var.n.start = exp(logVar.n.start), N.obs=y.procobs2) -sum(dnorm(obs.data,mean = pred\$mean,sd = sqrt(pred\$var),log = TRUE)) } #designate starting parameter values startvec=list(logr=log(0.25),logK=log(10),logprocvar=log(0.5), logobsvar=log(0.5),logM.n.start=log(3),logVar.n.start=-2) #load two required packages require(bbmle) require(optimx) #obtain MLE estimates of paramter values m4 = mle2(minuslog1 = nlkflik,start = startvec,data = list(obs.data=Nobs), method = "Nelder-Mead", control = list(maxit=2000)) Thanks -- Curtis         [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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Re: problem with MLE estimation using Kalman filter

 On Wed, Oct 16, 2013 at 8:30 PM, Curtis Burkhalter <[hidden email]> wrote: > I try to use the mle2 function written for R.  The error message states > that the argument "minuslog1" is missing with no default, but I've The argument is minuslogl Note: l instead of 1 hth, Ingmar ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.
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Re: problem with MLE estimation using Kalman filter

 Thanks, that always gets me for some reason.  Now when I run it though I get an error message at the very end that states "could not find function "M.n". I don't understand why I'm getting this message b/c there is no where that calls a function named "M.n" and I don't define a function with that name either. Thanks On Wed, Oct 16, 2013 at 2:38 PM, Ingmar Visser <[hidden email]> wrote: > On Wed, Oct 16, 2013 at 8:30 PM, Curtis Burkhalter > <[hidden email]> wrote: > > I try to use the mle2 function written for R.  The error message states > > that the argument "minuslog1" is missing with no default, but I've > > > The argument is minuslogl > > Note: l instead of 1 > > hth, Ingmar > -- Curtis Burkhalter         [[alternative HTML version deleted]] ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.