neglogLik {PtProcess} | R Documentation |
Calculates the log-likelihood multiplied by negative one. It is in a format that can be used with the functions nlm
and optim
.
neglogLik(params, object, pmap)
params |
a vector of revised parameter values. |
object |
an object of class "mpp" . |
pmap |
a user provided function mapping the revised parameter values params into the appropriate locations in object . |
This function can be used with the two functions nlm
and optim
(see “Examples” below) to maximise the likelihood function of a model specified in object
. Both nlm
and optim
are minimisers, hence the “negative” log-likelihood. The topic distribution
gives examples of their use in the relatively easy situation of fitting standard probability distributions to data assuming independence.
The maximisation of the model likelihood function can be restricted to be over a subset of the model parameters. Other parameters will then be fixed at the values stored in the model object
. Let Theta denote the model parameter space, and let Psi denote the parameter sub-space (Psi subseteq Theta) over which the likelihood function is to be maximised. The argument params
contains values in Psi, and pmap
is assigned a function that maps these values into the model parameter space Theta. See “Examples” below.
The mapping function assigned to pmap
can also be made to impose restrictions on the domain of the parameter space Psi so that the minimiser cannot jump to values such that Psi notsubseteq Theta. For example, if a particular parameter must be positive, one can work with a transformed parameter that can take any value on the real line, with the model parameter being the exponential of this transformed parameter. Similarly a modified logit like transform can be used to ensure that parameter values remain within a fixed interval with finite boundaries. Examples of these situations can be found in the topic distribution
and the “Examples” below.
Value of the log-likelihood times negative one.
# SRM: magnitude is iid exponential with bvalue=1 # maximise exponential mark density too TT <- c(0, 1000) bvalue <- 1 params <- c(-2.5, 0.01, 0.8, bvalue*log(10)) x <- mpp(data=NULL, gif=srm_gif, mark=list(dexp_mark, rexp_mark), params=params, gmap=expression(params[1:3]), mmap=expression(params[4]), TT=TT) x <- simulate(x, seed=5) allmap <- function(y, p){ # map all parameters into model object # transform exponential param so it is positive y$params[1:3] <- p[1:3] y$params[4] <- exp(p[4]) return(y) } params <- c(-2.5, 0.01, 0.8, log(bvalue*log(10))) z <- nlm(neglogLik, params, object=x, pmap=allmap, print.level=2, iterlim=500, typsize=abs(params)) print(z$estimate) # these should be the same: print(exp(z$estimate[4])) print(1/mean(x$data$magnitude))