search for: vecpar

Displaying 4 results from an estimated 4 matches for "vecpar".

2010 Feb 12
1
using mle2 for multinomial model optimization
...t;) #names for the initial vector nrs<-c(1:(l-1),1) #nrs for the initial vector svec = log(c) #transformation of the data to avoid constraints (x>0) names(svec) <- paste(nvec,nrs,sep="") parnames(mfun) <- names(svec) m1 = mle2(mfun,start=svec, vecpar=TRUE,fixed=svec[1:(l-1)]) #only estimate "N-x1-x2-x3",everything else is fixed coef(m1) ############################################################################ The function "mfun" is working, however the mle2 won't work. How can I fix this? Thanks so much for y...
2012 Jan 12
2
Function accepted by optim but not mle2 (?)
Dear Sir/ Madam, I'm having trouble de-bugging the following - which works perfectly well with optim or optimx - but not with mle2. I'd be really grateful if someone could show me what is wrong. Many thanks in advance. JSC: gompertz<- function (x,t=data) { a3<-x[1] b3<-x[2] shift<-data[1] h.t<-a3*exp(b3*(t-shift))
2010 Mar 24
0
optimize a joint lieklihood with mle2
...d is called mfun(logN, s, h=h, cohort=cohort). I want to maximize the vectors logN and s. I define the starting values for logN as svec1 and for s as svec2 then I write for the optimization: m1 = mle2(mfun,start=list(svec1=logN,svec2=s), method="L-BFGS-B",lower=lower,upper=10, vecpar=TRUE, data=list(h=h,cohort=cohort)) but I get the error: Error in mle2(mfun, start = list(svec1 = logN, svec2 = s), method = "L-BFGS-B", : some named arguments in 'start' are not arguments to the specified log-likelihood function How can I correctly write the fucntion f...
2011 Apr 12
1
2-parameter MLE problems
Hi all, Sorry for the re-post, I sent my previous e-mail before it was complete. I am trying to model seroprevalence using the differential equation: dP/dt = beta*seronegative*.001*(seropositive)-0.35*(0.999)*(seropositive)-r*seropositive. I would like to estimate my two parameters, beta and r, using maximum likelihood methods. I have included my code below: