LGC: Latent Gaussian Count model builder

In jlivsey/LatentGaussCounts: Integer-valued Time Series Models based on Gaussian Copula

Description

Main execution function for latentGaussCounts package.

Usage

LGC(x, count.family = c("Poisson", "mixed-Poisson", "negbinom",
  "GenPoisson"), gauss.series = c("AR", "MA", "FARIMA"),
  estim.method = c("gaussianLik", "particlesSIS"), max.terms = 30,
  p = NULL, d = NULL, q = NULL, n.mix = NULL, n = NULL,
  print.progress = FALSE, print.initial.estimates = FALSE, ...)

Arguments

x	data
count.family	desired marginal distribution (Poisson,mixed-Poisson,etc)
gauss.series	desired structure of your latent Gaussian process
estim.method	method used for estimating parameters
max.terms	maximum number of terms used to truncate Hermite expansions
p	AR order
d	don't use it
q	don't use it
n.mix	number of Poisson distributions in mixed-Poisson count.family
n	Not sure what this is
print.progress	Should progress be printed as optim() is run
print.initial.estimates	Should initial estiamtes be printed
...	additional parameters to pass to optim

Value

list object which are results from optim() run

jlivsey/LatentGaussCounts documentation built on May 1, 2020, 6:16 a.m.