Description Objects from the Class Slots Methods Author(s) See Also Examples

This class holds parameters for the Sequential Monte Carlo sampler.

Objects can be created by calls of the function `"smcparameters"`

.

`nparticles`

:Object of class

`"numeric"`

: an integer representing the desired number of particles.`temperatures`

:Object of class

`"numeric"`

: a vector of temperatures, default being`"seq(from = 0.01, to = 1, length.out = 100)"`

.`nmoves`

:Object of class

`"numeric"`

: number of move steps to be performed after each resampling step, default being 1.`ESSthreshold`

:Object of class

`"numeric"`

: resampling occurs when the Effective Sample Size goes below`"ESSthreshold"`

multiplied by the number of particles`"nparticles"`

.`movetype`

:Object of class

`"character"`

: type of Metropolis-Hastings move step to be performed; can be either set to`"independent"`

or`"randomwalk"`

, default being`"independent"`

.`movescale`

:Object of class

`"numeric"`

: if`movetype`

is set to`"randomwalk"`

, this parameter specifies the amount by which the estimate of the standard deviation of the target distribution is multiplied; the product being used to propose new points in the random-walk MH step. Default is 10%, ie a new point is proposed from a Normal distribution, centered on the latest point, with standard deviation equal to 10% of the standard deviation of the already-generated chain.`resamplingscheme`

:Object of class

`"character"`

: type of resampling to be used; either "multinomial", "residual" or "systematic", the default being "systematic".

- show
`signature(object = "smcparameters")`

: provides a little summary of a binning object when called (or when`print`

is called).

Luke Bornn <bornn@stat.harvard.edu>, Pierre E. Jacob <pierre.jacob.work@gmail.com>

1 2 3 4 5 | ```
showClass("smcparameters")
smcparam<- smcparameters(nparticles=5000,
temperatures = seq(from = 0.0001, to = 1, length.out= 100),
nmoves = 5, ESSthreshold = 0.5, movetype = "randomwalk",
movescale = 0.1)
``` |

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