DGLMExtPois is a package that contains statistical functions for the model estimation, dispersion testing and diagnosis of hyper-Poisson and Conway-Maxwell-Poisson regression models.
You can install the released version of DGLMExtPois from CRAN with:
install.packages("DGLMExtPois")and the development version from github with:
devtools::install_github("franciscomartinezdelrio/DGLMExtPois")This is a basic example which shows you how to solve a common problem:
library(DGLMExtPois)
library(Ecdat)
#> Loading required package: Ecfun
#>
#> Attaching package: 'Ecfun'
#> The following object is masked from 'package:base':
#>
#> sign
#>
#> Attaching package: 'Ecdat'
#> The following object is masked from 'package:datasets':
#>
#> Orange
Bids$size.sq <- Bids$size ^ 2
hP.bids <- glm.hP(formula.mu = numbids ~ leglrest + rearest + finrest +
whtknght + bidprem + insthold + size + size.sq +
regulatn,
formula.gamma = numbids ~ 1,
data = Bids)
summary(hP.bids)
#>
#> Call:
#> glm.hP(formula.mu = numbids ~ leglrest + rearest + finrest +
#> whtknght + bidprem + insthold + size + size.sq + regulatn,
#> formula.gamma = numbids ~ 1, data = Bids)
#>
#> Mean model coefficients (with log link):
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 1.042145 0.387027 2.693 0.00709 **
#> leglrest 0.240887 0.109638 2.197 0.02801 *
#> rearest -0.268646 0.144930 -1.854 0.06379 .
#> finrest 0.104245 0.163049 0.639 0.52260
#> whtknght 0.487929 0.110133 4.430 9.41e-06 ***
#> bidprem -0.709086 0.273832 -2.589 0.00961 **
#> insthold -0.363993 0.304749 -1.194 0.23232
#> size 0.173023 0.048291 3.583 0.00034 ***
#> size.sq -0.007371 0.002479 -2.973 0.00295 **
#> regulatn -0.008751 0.118167 -0.074 0.94097
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Dispersion model coefficients (with logit link):
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -2.6219 0.4766 -5.501 3.77e-08 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> AIC: 362.3
AIC(hP.bids)
#> [1] 362.3072
BIC(hP.bids)
#> [1] 393.5063
coef(hP.bids)
#> $mean_model
#> (Intercept) leglrest rearest finrest whtknght bidprem
#> 1.042145307 0.240886951 -0.268645993 0.104245100 0.487928599 -0.709086033
#> insthold size size.sq regulatn
#> -0.363993485 0.173023482 -0.007370863 -0.008751012
#>
#> $dispersion_model
#> (Intercept)
#> -2.621855
confint(hP.bids)
#> 2.5 % 97.5 %
#> (Intercept) 0.28358635 1.800704268
#> leglrest 0.02600000 0.455773898
#> rearest -0.55270282 0.015410837
#> finrest -0.21532507 0.423815275
#> whtknght 0.27207160 0.703785596
#> bidprem -1.24578669 -0.172385379
#> insthold -0.96129006 0.233303088
#> size 0.07837414 0.267672828
#> size.sq -0.01223058 -0.002511151
#> regulatn -0.24035456 0.222852539
head(fitted(hP.bids))
#> 1 2 3 4 5 6
#> 2.733621 1.331997 2.196977 1.176840 1.231121 2.088129
head(residuals(hP.bids))
#> 1 2 3 4 5 6
#> -0.5421896 -1.7307986 -1.0424542 -0.2501029 -0.3175111 0.8264357CMP.bids <- glm.CMP(formula.mu = numbids ~ leglrest + rearest + finrest +
whtknght + bidprem + insthold + size + size.sq +
regulatn,
formula.nu = numbids ~ 1,
data = Bids)
summary(CMP.bids)
#>
#> Call:
#> glm.CMP(formula.mu = numbids ~ leglrest + rearest + finrest +
#> whtknght + bidprem + insthold + size + size.sq + regulatn,
#> formula.nu = numbids ~ 1, data = Bids)
#>
#> Mean model coefficients (with log link):
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.990004 0.435140 2.275 0.022898 *
#> leglrest 0.267903 0.122808 2.181 0.029148 *
#> rearest -0.173273 0.154704 -1.120 0.262703
#> finrest 0.067916 0.174300 0.390 0.696794
#> whtknght 0.481172 0.131654 3.655 0.000257 ***
#> bidprem -0.685007 0.307470 -2.228 0.025889 *
#> insthold -0.367923 0.346620 -1.061 0.288481
#> size 0.179279 0.047604 3.766 0.000166 ***
#> size.sq -0.007580 0.002483 -3.052 0.002270 **
#> regulatn -0.037561 0.130235 -0.288 0.773031
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Dispersion model coefficients (with logit link):
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.5621 0.1534 3.665 0.000248 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> AIC: 382.2
AIC(CMP.bids)
#> [1] 382.1753
BIC(CMP.bids)
#> [1] 413.3744
coef(CMP.bids)
#> $mean_model
#> (Intercept) leglrest rearest finrest whtknght bidprem
#> 0.990003872 0.267902606 -0.173272510 0.067916284 0.481171553 -0.685006796
#> insthold size size.sq regulatn
#> -0.367923118 0.179279126 -0.007580393 -0.037561467
#>
#> $dispersion_model
#> (Intercept)
#> 0.5620821
confint(CMP.bids)
#> 2.5 % 97.5 %
#> (Intercept) 0.13714473 1.842863012
#> leglrest 0.02720360 0.508601611
#> rearest -0.47648689 0.129941873
#> finrest -0.27370514 0.409537708
#> whtknght 0.22313460 0.739208506
#> bidprem -1.28763746 -0.082376130
#> insthold -1.04728529 0.311439049
#> size 0.08597638 0.272581868
#> size.sq -0.01244781 -0.002712976
#> regulatn -0.29281835 0.217695412
head(fitted(CMP.bids))
#> 1 2 3 4 5 6
#> 2.736143 1.296889 2.140255 1.189666 1.205770 2.092416
head(residuals(CMP.bids))
#> 1 2 3 4 5 6
#> -0.5646967 -1.3669047 -0.9753079 -0.2069397 -0.2233074 0.7839429