bayescount (version 0.9.9-1)

likelihood: Calculate the (Log) Likelihood of Obtaining Data from a Distribution

Description

Function to calculate the (log) likelihood of obtaining a set of data from one of the following distributions: Poisson (P), gamma (G), lognormal (L), Weibull (W), gamma Poisson (GP), lognormal Poisson (LP), Weibull Poisson (WP), all with or without zero-inflation (ZI). For mixture models, the likelihood is calculated for the data by integrating over each possible value of lambda for each data point, which may take some time for large datasets. *THIS SOFTWARE IS INTENDED FOR EDUCATIONAL PURPOSES ONLY AND SHOULD NOT BE RELIED UPON FOR REAL WORLD APPLICATIONS*

Usage

likelihood(model, data, mean=NA, variance=NA, 
   zi=NA, shape=NA, scale=NA, 
   iterations=min(1000, (length(as.matrix(mean)[,1])*length(shape))),
   log=TRUE, silent=FALSE, raw.output=FALSE)

Value

The (log) likelihood is returned either as a vector of length equal to the number of iterations representing the likelihood at each iteration if raw.output==TRUE, or a summary of the results (median, maximum and 95% highest posterior density interval (see HPDinterval) if raw.output==FALSE. It is possible for the functions that use integrate to produce incalculable probabilities for some iterations, in which case the likelihood for these iterations are returned as missing data (and therefore the summary statistics for the whole simulation are also returned as missing data). If only 1 iteration of values for mean/variance/shape/scale are provided, a single value for likelihood is output.

See Also

bayescount, bayescount.single, maximise.likelihood for a simple maximum likelihood solution using likelihood()

Examples

# calculate the likelihood of obtaining a set of count data from a zero-inflated Poisson distribution with set mean and zero-inflation values

data <- rpois(100, 10)
data[1:15] <- 0

likelihood('ZIP', data, mean=10, zi=15)

# now calculate the likelihood for the same data using an MCMC object to provide the values for mean and zero-inflation

values <- bayescount.single(data, model='ZISP', raw.output=TRUE)
means <- c(values[,'mean'][[1]], values[,'mean'][[2]])
zis <- (1-c(values[,'prob'][[1]], values[,'prob'][[2]]))*100  # The function outputs the prevalence of disease when raw.ouput is TRUE, so zero-inflation must be calculated from this

likes <- likelihood('ZIP', data, mean=means, zi=zis, raw.output=TRUE)$likelihood
hist(likes, breaks='fd', col='red')