saved_states {pomp}

Saved states

Description

Retrieve latent state trajectories from a particle filter calculation.

Usage

## S4 method for signature 'pfilterd_pomp'
saved_states(object, ..., format = c("list", "data.frame"))

## S4 method for signature 'pfilterList'
saved_states(object, ..., format = c("list", "data.frame"))

Arguments

object

result of a filtering computation

...

ignored

format

character; format of the returned object (see below).

Details

When one calls pfilter with save.states=TRUE, the latent state vector associated with each particle is saved. This can be extracted by calling saved_states on the ‘pfilterd.pomp’ object. These are the unweighted particles, saved after resampling.

Value

According to the format argument, the saved states are returned either as a list or a data frame.

If format="data.frame", then the returned data frame holds the state variables and (optionally) the unnormalized log weight of each particle at each observation time. The .id variable distinguishes particles.

If format="list" and pfilter was called with save.states="unweighted" or save.states="TRUE", the returned list contains one element per observation time. Each element consists of a matrix, with one row for each state variable and one column for each particle. If pfilter was called with save.states="weighted", the list itself contains two lists: the first holds the particles as above, the second holds the corresponding unnormalized log weights. In particular, it has one element per observation time; each element is the vector of per-particle log weights.

See Also

More on sequential Monte Carlo methods: bsmc2(), cond_logLik(), eff_sample_size(), filter_mean(), filter_traj(), kalman, mif2(), pfilter(), pmcmc(), pred_mean(), pred_var(), wpfilter()

Other extraction methods: coef(), cond_logLik(), covmat(), eff_sample_size(), filter_mean(), filter_traj(), forecast(), logLik, obs(), pred_mean(), pred_var(), spy(), states(), summary(), time(), timezero(), traces()