filter_traj {pomp} | R Documentation |

## Filtering trajectories

### Description

Drawing from the smoothing distribution

### Usage

```
## S4 method for signature 'pfilterd_pomp'
filter_traj(object, vars, ..., format = c("array", "data.frame"))
## S4 method for signature 'listie'
filter_traj(object, vars, ..., format = c("array", "data.frame"))
## S4 method for signature 'pmcmcd_pomp'
filter_traj(object, vars, ...)
```

### Arguments

`object` |
result of a filtering computation |

`vars` |
optional character; names of variables |

`...` |
ignored |

`format` |
format of the returned object |

### Details

The smoothing distribution is the distribution of

`X(t_k) | Y(t_1)=y^*_1, \dots, Y(t_n)=y^*_n,`

where `X(t_k)`

is the latent state process and `Y(t_k)`

is the observable process at time `t_k`

, and `n`

is the number of observations.

To draw samples from this distribution, one can run a number of independent particle filter (`pfilter`

) operations, sampling the full trajectory of *one* randomly-drawn particle from each one.
One should view these as *weighted* samples from the smoothing distribution, where the weights are the *likelihoods* returned by each of the `pfilter`

computations.

One accomplishes this by setting `filter.traj = TRUE`

in each `pfilter`

computation and extracting the trajectory using the `filter_traj`

command.

In particle MCMC (`pmcmc`

), the tracking of an individual trajectory is performed automatically.

### See Also

More on sequential Monte Carlo methods:
`bsmc2()`

,
`cond_logLik()`

,
`eff_sample_size()`

,
`filter_mean()`

,
`kalman`

,
`mif2()`

,
`pfilter()`

,
`pmcmc()`

,
`pred_mean()`

,
`pred_var()`

,
`saved_states()`

,
`wpfilter()`

Other extraction methods:
`coef()`

,
`cond_logLik()`

,
`covmat()`

,
`eff_sample_size()`

,
`filter_mean()`

,
`forecast()`

,
`logLik`

,
`obs()`

,
`pred_mean()`

,
`pred_var()`

,
`saved_states()`

,
`spy()`

,
`states()`

,
`summary()`

,
`time()`

,
`timezero()`

,
`traces()`

*pomp*version 5.11.0.0 Index]