Description Usage Arguments Value Methods References See Also Examples

An implementation of the algorithm of Park and Ionides (2020), following the pseudocode in Asfaw et al. (2020).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
## S4 method for signature 'missing'
girf(object, ...)
## S4 method for signature 'ANY'
girf(object, ...)
## S4 method for signature 'spatPomp'
girf(
object,
Np,
Ninter,
lookahead = 1,
Nguide,
kind = c("bootstrap", "moment"),
tol,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'girfd_spatPomp'
girf(
object,
Np,
Ninter,
lookahead,
Nguide,
kind = c("bootstrap", "moment"),
tol,
...
)
``` |

`object` |
A |

`...` |
If a |

`Np` |
The number of particles used within each replicate for the adapted simulations. |

`Ninter` |
the number of intermediate resampling time points. |

`lookahead` |
The number of future observations included in the guide function. |

`Nguide` |
The number of simulations used to estimate state process uncertainty for each particle. |

`kind` |
One of two types of guide function construction. Defaults to |

`tol` |
If all of the guide function evaluations become too small (beyond floating-point precision limits), we set them to this value. |

`verbose` |
logical; if |

Upon successful completion, `girf()`

returns an object of class
‘girfd_spatPomp’ which contains the algorithmic parameters that were used to
run `girf()`

and the resulting log likelihood estimate.

The following methods are available for such an object:

`logLik`

yields an unbiased estimate of the log-likelihood of the data under the model.

2020

\asfaw2020

Other particle filter methods:
`abfir()`

,
`abf()`

,
`bpfilter()`

,
`enkf()`

,
`ienkf()`

,
`igirf()`

,
`iubf()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
# Create a simulation of a Brownian motion
b <- bm(U=3, N=10)
# Run GIRF
girfd_bm <- girf(b,
Np = 100,
Ninter = length(unit_names(b)),
lookahead = 1,
Nguide = 50
)
# Get the likelihood estimate from GIRF
logLik(girfd_bm)
# Compare with the likelihood estimate from particle filter
pfd_bm <- pfilter(b, Np = 500)
logLik(pfd_bm)
``` |

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