R: Update a fitted 'hmm.discnp' model.

update.hmm.discnp {hmm.discnp}

R Documentation

Update a fitted `hmm.discnp` model.

Description

An update() method for objects of class hmm.discnp.

Usage

## S3 method for class 'hmm.discnp'
update(object,..., data, Kplus1=FALSE,
        tpm2=NULL, verbose=FALSE, method=NULL, optimiser=NULL,
        stationary=NULL, mixture=NULL, cis=NULL, tolerance=NULL,
        itmax=NULL, crit=NULL, X=NULL, addIntercept=NULL)

Arguments

`object`	An object of class `hmm.discnp` as returned by `hmm()`.
`...`	Not used.
`data`	The data set to which the (updated) model is to be fitted. See the description of the `y` argument of `hmm()` for more detail.
`Kplus1`	Logical scalar. Should the number of states be incremented by 1? If so then `tpm` (the transition probability matrix) is re-formed by `rbind()`-ing on a row all of whose entries are `1/K` (where `K` is the “old” number of states) and then `cbind()`-ing on a column of zeroes. The emission probability matrix `Rho` is reformed by `cbind()`-ing on a column all of whose entries are `1/m` where `m` is the number of discrete values of the emissions. Note that the intial likelihood of the “new” model with `K+1` states will (should?) be exactly the same as that of of the “old” fitted `K`-state model. The `Kplus1` argument is provided mainly so as to provide a set of starting values for the fitting process which will guarantee the log likelihood of a `K+1`-state model will be at least as large as that of a `K`-state model fitted to the same data set. Experience indicates that when `Kplus1=TRUE` is used, the fitting process does not “move very far” from the maximum log likelihood found for the `K`-state model. It is then advisable to try (many) random starting values so as to (try to) find the “true” maximum for the `K+1`-state model.
`tpm2`	The transtion probability matrix to use when updating a model fitted with `K=1` and `Kplus1=TRUE`. This argument is ignored otherwise. The default value of this argument is `matrix(0.5,2,2)`. The value of `tpm2` makes no difference to the initial value of the likelihood of the `K=2` model (which will be identical to the likelihood of the fitted `K=1` model that is being updated). Any two-by-two transition probability matrix “will do”. However the value of `tpm2` could conceivably have an impact on the final likelihood of the `K=2` model to which the fitting procedure converges. This is particularly true if the `method` is (or is switched to) `"LM"`.
`verbose`	See the help for `hmm()`.
`method`	See the help for `hmm()`.
`optimiser`	See the help for `hmm()`.
`stationary`	See the help for `hmm()`.
`mixture`	See the help for `hmm()`.
`cis`	See the help for `hmm()`.
`tolerance`	See the help for `hmm()`.
`itmax`	See the help for `hmm()`.
`crit`	See the help for `hmm()`.
`X`	See the help for `hmm()`.
`addIntercept`	See the help for `hmm()`.

Details

Except for argument X, any arguments that are left NULL have their values supplied from the args component of object.

Value

An object of class hmm.discnp with an additional component init.log.like which is the initial log likelihood calculated at the starting values of the parameters (which may be modified from the parameters returned in the object being updated, if Kplus1 is TRUE). The calculation is done by the function logLikHmm(). Barring the strange and unforeseen, init.log.like should be (reassuringly) equal to object$log.like. See hmm() for details of the other components of the returned value.

Author(s)

Rolf Turner r.turner@auckland.ac.nz

Examples

set.seed(294)
fit  <- hmm(WoodPeweeSong,K=2,rand.start=list(tpm=TRUE,Rho=TRUE),itmax=10)
xxx  <- rhmm(fit,nsim=1)
sfit <- update(fit,data=xxx,itmax=10)
yyy  <- with(SydColDisc,split(y,f=list(locn,depth)))
f1   <- hmm(yyy,K=1)
f2   <- update(f1,data=yyy,Kplus1=TRUE) # Big improvement, but ...
## Not run: 
g2   <- hmm(yyy,K=2) # Substantially better than f2. 

## End(Not run)

[Package hmm.discnp version 3.0-9 Index]

Update a fitted hmm.discnp model.