Eduardo E. R. Junior - [email protected], IME-USP
The cmpreg package contains functions to fit Conway-Maxwell-Poisson
(COM-Poisson) models with varying dispersion (model mean and dispersion
parameters as functions of covariates.) in the mean-type parametrization
proposed by Ribeiro Jr, et al. 2018 <arxiv.org/abs/1801.09795>. The
functions to computate the normalizing constant are written in C++, so
the computations is reasonably fast.
Joint work with Walmes M. Zeviani and Clarice G.B. Demétrio.
You can install the development version of cmpreg from
GitHub with:
# install.packages("devtools")
devtools::install_github("jreduardo/cmpreg")Basically, this package implements methods similar to those related to
glm objects. The main function is cmp(...).
library(cmpreg)
# Fit model ------------------------------------------------------------
model <- cmp(formula = ninsect ~ extract,
dformula = ~extract,
data = sitophilus)
# Methods --------------------------------------------------------------
print(model)
#>
#> COM-Poisson regression models
#> Call: cmp(formula = ninsect ~ extract,
#> dformula = ~extract,
#> data = sitophilus)
#>
#> Mean coefficients:
#> (Intercept) extractLeaf extractBranch extractSeed
#> 3.449861 -0.006596 -0.052377 -3.311192
#>
#> Dispersion coefficients:
#> (Intercept) extractLeaf extractBranch extractSeed
#> -0.6652 -0.3832 -0.3724 -0.1177
#>
#> Residual degrees of freedom: 32
#> Minus twice the log-likelihood: 242.8279
summary(model)
#>
#> Individual Wald-tests for COM-Poisson regression models
#> Call: cmp(formula = ninsect ~ extract,
#> dformula = ~extract,
#> data = sitophilus)
#>
#> Mean coefficients:
#> Estimate Std. Error Z value Pr(>|z|)
#> (Intercept) 3.449861 0.077995 44.232 < 2e-16 ***
#> extractLeaf -0.006596 0.122210 -0.054 0.957
#> extractBranch -0.052377 0.123462 -0.424 0.671
#> extractSeed -3.311192 0.541399 -6.116 9.6e-10 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Dispersion coefficients:
#> Estimate Std. Error Z value Pr(>|z|)
#> (Intercept) -0.6652 0.4573 -1.455 0.146
#> extractLeaf -0.3832 0.6509 -0.589 0.556
#> extractBranch -0.3724 0.6514 -0.572 0.568
#> extractSeed -0.1177 1.5464 -0.076 0.939
#>
#> Residual degrees of freedom: 32
#> Minus twice the log-likelihood: 242.8279
equitest(model)
#>
#> Likelihood ratio test for equidispersion
#>
#> Resid.df Loglik LRT_stat LRT_df Pr(>LRT_stat)
#> Model 1 32 -121.414 18.338 4 0.00106 **
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Predict new data -----------------------------------------------------
newdf <- sitophilus[c(1, 11, 21, 31), -2, drop = FALSE]
predict(model,
newdata = newdf,
what = "all",
type = "response",
se.fit = TRUE,
augment_data = TRUE)
#> extract what fit ste
#> 1 Leaf mean 31.2889190 2.9438074
#> 2 Branch mean 29.8887880 2.8605146
#> 3 Seed mean 1.1487432 0.3120589
#> 4 Control mean 31.4959985 2.4565378
#> 5 Leaf dispersion 0.3505090 0.1623430
#> 6 Branch dispersion 0.3542998 0.1643400
#> 7 Seed dispersion 0.4570660 0.3423450
#> 8 Control dispersion 0.5141684 0.2351420Currently, the methods implemented for "cmpreg" objects are
methods(class = "cmpreg")
#> [1] anova coef equitest fitted logLik
#> [6] model.matrix predict print summary vcov
#> see '?methods' for accessing help and source codeThere are other R packages to deal with COM-Poisson models that have
somehow contributed to the writing of cmpreg.
compoisson: Routines for density and moments of the COM-Poisson distribution under original parametrization.CompGLM: Fit COM-Poisson models under original parametrization (includes dispersion modeling).COMPoissonReg: Fit COM-Poisson models under original parametrization (includes zero-inflation and dispersion modeling).glmmTMB: Fit (among other) COM-Poisson models under a different mean-parametrization (includes zero-inflation, dispersion modeling and random effects).
The gammacount package is licensed under the GNU General Public
License, version 3, see file
LICENSE.md, © 2019 E. E., Ribeiro Jr.