Model.FaddyJMV.general {CountsEPPM}

Function for a general Faddy distribution modeled by means and scale-factors.

Description

Outputs probabilities for a general Faddy distribution modeled by means and scale-factors i.e. with the design matrices for mean and scale-factor input together with data and offsets.

Usage

Model.FaddyJMV.general(parameter, link, covariates.matrix.mean,
 covariates.matrix.scalef, offset.mean, offset.scalef,
 fixed.b, vnmax)

Arguments

parameter

A vector of the parameters of the model which is set to initial estimates on function call.

link

Takes one of one values i.e., 'log'. The default is 'log'.

covariates.matrix.mean

A matrix of covariates for the mean where rows are the number of values in listcounts and columns the covariates. This matrix is extracted from the formulae in function CountsEPPM. However, in the accompanying example it is shown how it can be constructed independently of function CountsEPPM.

covariates.matrix.scalef

A matrix of covariates for the scale factor where rows are the number of values in listcounts and columns the covariates. The default is a vector of ones. This matrix is extracted from the formulae in function CountsEPPM. However, in the accompanying example it is shown how it can be constructed independently of function CountsEPPM.

offset.mean

An offset vector for the mean. The default is a vector of ones.

offset.scalef

An offset vector for the scale-factor. The default is a vector of ones.

fixed.b

Set to the value of the parameter b if a fixed.b model is being used.

vnmax

A vector of the maximum counts for each vector in list.counts i.e. the list of grouped counts.

Value

The list output with elements

model

The model being fitted

estimate

Estimates of parameters

probabilities

Vector of probabilities

Author(s)

David M. Smith <[e-mail]>

References

Faddy M, Smith D. (2011). Analysis of count data with covariate dependence in both mean and variance. Journal of Applied Statistics, 38, 2683-2694. doi:10.1002/bimj.201100214.

Examples

all.counts=c(rep(0,5),352,479,530,291,101,17)
nmax1  <- length(all.counts)
nmax   <- nmax1 - 1
cnum   <- 0:nmax
ncount <- sum(all.counts)  
all.mean <- sum(cnum*all.counts)/ncount 
all.scalef <- ((sum(cnum*cnum*all.counts) - ncount*all.mean*all.mean) / (ncount - 1)) / all.mean
alldata <- data.frame(all.mean, all.scalef)
mf <- model.frame(formula = all.mean~1, data=alldata)
covariates.matrix.mean <- model.matrix(attr(mf, "terms"), data=mf)
mf <- model.frame(formula = all.scalef~1, data = alldata)
covariates.matrix.scalef <- model.matrix(attr(mf, "terms"), data = mf)
list.counts <- list(all.counts)
parameter <- c(1.8386079, 0.6021198, 6.0714071)
names(parameter) <- c("beta0 log(mean)", "beta0 log(scale-factor)", "log(b)") 
link <- "log"
attr(link, which = "mean") <- make.link(link)
offset.mean <- matrix(c(rep(0,nrow(covariates.matrix.mean))), ncol = 1)
offset.scalef <- matrix(c(rep(0,nrow(covariates.matrix.mean))), ncol = 1)
output <- Model.FaddyJMV.general(parameter, link,
 covariates.matrix.mean, covariates.matrix.scalef,
 offset.mean, offset.scalef, fixed.b = NA, vnmax = c(10))
print(output)