STAR (version 0.3-7)

rexpMLE: Maximum Likelihood Parameter Estimation of a Refractory Exponential Model with Possibly Censored Data

Description

Estimate refractory exponential model parameters by the maximum likelihood method using possibly censored data.

Usage

rexpMLE(yi, ni = numeric(length(yi)) + 1, si = numeric(length(yi)) + 1)

Arguments

yi

vector of (possibly binned) observations or a spikeTrain object.

ni

vector of counts for each value of yi; default: numeric(length(yi))+1.

si

vector of counts of uncensored observations for each value of yi; default: numeric(length(yi))+1.

Value

A list of class durationFit with the following components:

estimate

the estimated parameters, a named vector.

se

the standard errors, a named vector.

logLik

the log likelihood at maximum.

r

a function returning the log of the relative likelihood function.

mll

a function returning the opposite of the log likelihood function using the log of the parameters.

call

the matched call.

Details

The MLE are available in closed form even in the censored case for this model. The likelihood function cannot be differentiated with respect to the rp (refractory period) parameter at the maximum. COnfidence intervals for this parameter are therefore not available.

See Also

drexp, invgaussMLE, lnormMLE, gammaMLE, weibullMLE

Examples

## Not run: 
# ## Simulate sample of size 100 from a refractory exponential distribution
# set.seed(1102006,"Mersenne-Twister")
# sampleSize <- 100
# rate.true <- 20
# rp.true <- 0.01
# sampRE <- rrexp(sampleSize,rate=rate.true,rp=rp.true)
# sampREmleRE <- rexpMLE(sampRE)
# rbind(est = sampREmleRE$estimate,se = sampREmleRE$se,true = c(rate.true,rp.true))
# 
# ## make a parametric boostrap to check the distribution of the deviance
# nbReplicate <- 10000
# system.time(
#             devianceRE100 <- replicate(nbReplicate,{
#               sampRE <- rrexp(sampleSize,rate=rate.true,rp=rp.true)
#               sampREmleRE <- rexpMLE(sampRE)
#               -2*sampREmleRE$r(rate.true,rp.true)
#             }
#                                        )
#             )[3]
# 
# ## Get 95 and 99% confidence intervals for the QQ plot
# ci <- sapply(1:nbReplicate,
#                  function(idx) qchisq(qbeta(c(0.005,0.025,0.975,0.995),
#                                             idx,
#                                             nbReplicate-idx+1),
#                                       df=2)
#              )
# ## make QQ plot
# X <- qchisq(ppoints(nbReplicate),df=2)
# Y <- sort(devianceRE100)
# X11()
# plot(X,Y,type="n",
#      xlab=expression(paste(chi[2]^2," quantiles")),
#      ylab="MC quantiles",
#      main="Deviance with true parameters after ML fit of refractory Poisson data",
#      sub=paste("sample size:", sampleSize,"MC replicates:", nbReplicate)
#      )
# abline(a=0,b=1)
# lines(X,ci[1,],lty=2)
# lines(X,ci[2,],lty=2)
# lines(X,ci[3,],lty=2)
# lines(X,ci[4,],lty=2)
# lines(X,Y,col=2)
# ## End(Not run)