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mpcmp (version 0.3.6)

Mean-Parametrized Conway-Maxwell Poisson (COM-Poisson) Regression

Description

A collection of functions for estimation, testing and diagnostic checking for the mean-parametrized Conway-Maxwell-Poisson (COM-Poisson) regression model of Huang (2017) .

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Version

Install

install.packages('mpcmp')

Monthly Downloads

22

Version

0.3.6

License

GPL-3

Issues

5

Pull Requests

0

Stars

8

Forks

3

Maintainer

Thomas Fung

Last Published

October 26th, 2020

Functions in mpcmp (0.3.6)

autoplot.cmp

Plot Diagnostic for a glm.cmp Object in ggplot style
augment.cmp

Augment data with information from a(n) CMP model object
CBIND

Combine R Objects by Columns
COM_Poisson_Distribution

The Conway-Maxwell-Poisson (COM-Poisson) Distribution.
AIC.cmp

Akaike's Information Criterion
LRTnu

Likelihood Ratio Test for nu = 1 of a COM-Poisson model
PIT_ggPlot

ggplot version of PIT Plots for a CMP Object
Z

Calculate the Normalizing Constant for COM-Poisson distribution
PIT_Plot

PIT Plots for a CMP Object
comp_mu_loglik

Calculate the Log-Likelihood of the COM-Poisson model
cmplrtest

Likelihood Ratio Test for nested COM-Poisson models
is.wholenumber

Test for a whole number
confint.cmp

Confidence Intervals for CMP Model Parameters
comp_expected_values

Functions to Compute Various Expected Values for the COM-Poisson Distribution
comp_lambdas

Solve for Lambda for a Particular Mean Parametrized COM-Poisson Distribution
rPIT

Random Normal Probability Integral Transform
model.frame.cmp

Extract the Model Frame from a COM-Poisson Model Fit
coef.cmp

Extract Model Coefficients from a COM-Poisson Model Fit
model.matrix.cmp

Extract the Design Matrix from a COM-Poisson Model Fit
reexports

Objects exported from other packages
logLik.cmp

Extract the (Maximized) Log-Likelihood from a COM-Poisson Model Fit
cottonbolls

Cotton Bolls data set
attendance

Attendance data set
fit_glm_cmp_const_nu

Fit a Mean Parametrized Conway-Maxwell Poisson Generalized Linear Model with constant dispersion.
fit_glm_cmp_vary_nu

Fit a Mean Parametrized Conway-Maxwell Poisson Generalized Linear Model with varying dispersion.
fish

Fish data set
logZ_c

Calculate the Normalizing Constant in log scale for COM-Poisson distribution The calculation of the function logZ will be performed here. This function is used to approximate the normalizing constant for COM-Poisson distributions via truncation. The standard COM-Poisson parametrization is being used here.
predict.cmp

Model Predictions for a glm.cmp Object
logZ

Calculate the Normalizing Constant in log scale for COM-Poisson distribution
print.cmp

Print Values of COM-Poisson Model
fitted.cmp

Extract Fitted Values from a COM-Poisson Model Fit
glance.cmp

Glance at a(n) CMP model object
mpcmp-package

Mean-parametrized Conway-Maxwell Poisson Regression
nobs.cmp

Extract the Number of Observation from a COM-Poisson Model Fit
residuals.cmp

Extract COM-Poisson Model Residuals
takeoverbids

Takeover Bids data set
regression.diagnostic.cmp

CMP Regression Diagnostic
getnu

Parameter Generator for nu
summary.cmp

Summarizing COM-Poisson Model Fit
tidy.cmp

Tidy a(n) CMP model object
sitophilus

Sitophilus data set
glm.cmp

Fit a Mean Parametrized Conway-Maxwell Poisson Generalized Linear Model
nrPIT

Non-randomized Probability Integral Transform
plot.cmp

Plot Diagnostic for a glm.cmp Object
update.cmp

Update and Re-fit a COM-Poisson Model
vcov.cmp

Extracting the Variance-Covariance Matrix from a COM-Poisson Model Fit