MLE of count data (univariate discrete distributions) {Rfast}

MLE of count data

Description

MLE of count data.

Usage

zip.mle(x, tol = 1e-09)
ztp.mle(x, tol = 1e-09)
negbin.mle(x, type = 1, tol = 1e-09) 
binom.mle(x, N = NULL, tol = 1e-07)
borel.mle(x)
geom.mle(x, type = 1)
logseries.mle(x, tol = 1e-09)
poisson.mle(x)
betageom.mle(x, tol = 1e-07)
betabinom.mle(x, N, tol = 1e-07)

Arguments

x

A vector with discrete valued data.

type

This argument is for the negative binomial and the geometric distribution. In the negative binomial you can choose which way your prefer. Type 1 is for smal sample sizes, whereas type 2 is for larger ones as is faster. For the geometric it is related to its two forms. Type 1 refers to the case where the minimum is zero and type 2 for the case of the minimum being 1.

N

This is for the binomial distribution only, specifying the total number of successes. If NULL, it is sestimated by the data. It can also be a vector of successes.

tol

The tolerance level up to which the maximisation stops set to 1e-09 by default.

Details

Instead of maximising the log-likelihood via a numerical optimiser we used a Newton-Raphson algorithm which is faster.

See wikipedia for the equation to be solved in the case of the zero inflated distribution. https://en.wikipedia.org/wiki/Zero-inflated_model. In order to avoid negative values we have used link functions, log for the lambda and logit for the \pi as suggested by Lambert (1992). As for the zero truncated Poisson see https://en.wikipedia.org/wiki/Zero-truncated_Poisson_distribution.

zip.mle is for the zero inflated Poisson, whereas ztp.mle is for the zero truncated Poisson distribution.

Value

The following list is not inclusive of all cases. Different functions have different names. In general a list including:

mess

This is for the negbin.mle only. If there is no reason to use the negative binomial distribution a message will appear, otherwise this is NULL.

iters

The number of iterations required for the Newton-Raphson to converge.

loglik

The value of the maximised log-likelihood.

prob

The probability parameter of the distribution. In some distributions this argument might have a different name. For example, param in the zero inflated Poisson.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[e-mail]> and Manos Papadakis <[e-mail]>.

References

Lambert Diane (1992). Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing. Technometrics. 34 (1): 1-14

Johnson Norman L., Kotz Samuel and Kemp Adrienne W. (1992). Univariate Discrete Distributions (2nd ed.). Wiley

See Also

poisson_only, colrange

Examples

x <- rpois(100, 2)
res<-zip.mle(x)
res<-poisson.mle(x)
## small difference in the two log-likelihoods as expected.

x <- rpois(100, 10)
x[x == 0 ] <- 1
res<-ztp.mle(x)
res<-poisson.mle(x)
## significant difference in the two log-likelihoods. 

x <- rnbinom(100, 10, 0.6)
res<-poisson.mle(x)
res<-negbin.mle(x)