flexCountReg

The goal of flexCountReg is to provide functions that allow the analyst to estimate count regression models that can handle multiple analysis issues including excess zeros, overdispersion as a function of variables (i.e., generalized count models), random parameters, etc.

Installation

You can install the development version of flexCountReg like using:

# install.packages("devtools")
devtools::install_github("jwood-iastate/flexCountReg")

Functions and Data

The following functions are included in the flexCountReg package, grouped by continuous and count distributions.

Distribution Functions

Continuous Distributions

• Inverse Gamma Distribution

  • dinvgamma for the density function
  • pinvgamma for the cumulative density function
  • qinvgamma for the quantile function
  • rinvgamma for random number generation

• Triangle Distribution

  • dtri for the density function
  • ptri for the cumulative density function
  • qtri for the quantile function
  • rtri for random number generation

• Lognormal Distribution

  • mgf_lognormal for estimating the moment generating function

Count Distributions

• Generalized Waring Distribution

  • dgwar for the density function
  • pgwar for the cumulative density function
  • qgwar for the quantile function
  • rgwar for random number generation

• Poisson-Generalized-Exponential Distribution

  • dpge for the density function
  • ppge for the cumulative density function
  • qpge for the quantile function
  • rpge for random number generation

• Poisson-Inverse-Gaussian Distribution (Types 1 and 2)

  • dpinvgaus for the density function
  • ppinvgaus for the cumulative density function
  • qpinvgaus for the quantile function
  • rpinvgaus for random number generation

• Poisson-Inverse-Gamma Distribution

  • dpinvgamma for the density function
  • ppinvgamma for the cumulative density function
  • qpinvgamma for the quantile function
  • rpinvgamma for random number generation

• Poisson-Lindley Distribution

  • dplind for the density function
  • pplind for the cumulative density function
  • qplind for the quantile function
  • rplind for random number generation

• Poisson-Lindley-Gamma (Negative Binomial-Lindley) Distribution

  • dplindGamma for the density function
  • pplindGamma for the cumulative density function
  • qplindGamma for the quantile function
  • rplindGamma for random number generation

• Poisson-Lindley-Lognormal Distribution

  • dplindLnorm for the density function
  • pplindLnorm for the cumulative density function
  • qplindLnorm for the quantile function
  • rplindLnorm for random number generation

• Poisson-Lognormal Distribution

  • dpLnorm for the density function
  • ppLnorm for the cumulative density function
  • qtpLnorm for the quantile function
  • rpLnorm for random number generation

• Poisson-Weibull Distribution

  • dpoisweibull for the density function
  • ppoisweibull for the cumulative density function
  • qpoisweibull for the quantile function
  • rpoisweibull for random number generation

• Sichel Distribution

  • dsichel for the density function
  • psichel for the cumulative density function
  • qsichel for the quantile function
  • rsichel for random number generation

• Conway-Maxwell-Poisson Distribution

  • dcom for the density function
  • pcom for the cumulative density function
  • qcom for the quantile function
  • rcom for random number generation

Model Estimation Functions

• countreg is a general function for estimating the non-panel, non-random parameters count regression models
• countreg.rp estimates the random parameters count models.
• poislind.re estimates the random effects Poisson-Lindley model
• renb estimates the random effects negative binomial regression model.

Model Evaluation, Comparison, and Convenience Functions

• cureplot generates a CURE plot for the specified model, based on the cureplots package.
• mae computes the Mean Absolute Error (MAE).
• myAIC computes the Akaike Information Criterion (AIC) value.
• myBIC computes the Bayesian Information Criterion (BIC) value.
• regCompTable creates a publication-ready table comparing multiple models. This can include the regression estimate results, AIC, BIC, and Pseudo R-Square values.
• regCompTest compares any given model with a base model. This can be used to perform a likelihood ratio test between models.
• rmse computes the Root Mean Squared Error (RMSE).
• predict allows the predict function to be used for out-of-sample predictions for any of the flexCountReg models.
• summary allows the use of the summary function to get a model summary from a flexCountReg regression object.

Data A dataset, washington_roads, is included. It is based on a sample of Washington primary 2-lane roads from the years 2016-2018. Data for the roads, traffic volumes (AADT) and associated crashes were obtained from the Highway Safety Information System (HSIS).

Probability Distributions

As noted in the list of functions, the probability distributions below are included in the flexCountReg package. Details of the distributions are provided in the documentation (help files).

Continuous Distributions

• Inverse Gamma Distribution
• Triangle Distribution

Count Distributions Distributions that Handle Equidispersion

• Poisson

Distributions that handle Underdispersion

• Conway-Maxwell-Poisson (COM) Distribution

Distributions that Handle Overdispersion

• Negative Binomial in various forms (NB-1, NB-2, and NB-P)
• Poisson-Inverse-Gaussian Distribution (Types 1 and 2)
• Poisson-Inverse-Gamma
• Poisson-Lognormal Distribution
• Poisson-Weibull Distribution
• Sichel Distribution
• Generalized Waring Distribution

Distributions that Handle Excess Zeros

• Poisson-Generalized-Exponential Distribution
• Poisson-Lindley Distribution
• Poisson-Lindley-Gamma (Negative Binomial-Lindley) Distribution
• Poisson-Lindley-Lognormal Distribution

Example

The following is an example of using flexCountReg to estimate a negative binomial (NB-2) regression model with the overdispersion parameter as a function of predictor variables:

library(gt) # used to format summary tables here
library(flexCountReg)
library(knitr)

data("washington_roads")
washington_roads$AADT10kplus <- ifelse(washington_roads$AADT > 10000, 1, 0)
gen.nb2 <- countreg(Total_crashes ~ lnaadt + lnlength + speed50 + AADT10kplus,
               data = washington_roads, family = "NB2",
               dis_param_formula_1 = ~ speed50,  method='BFGS')

kable(summary(gen.nb2), caption = "NB-2 Model Summary")
#> Call:
#>  Total_crashes ~ lnaadt + lnlength + speed50 + AADT10kplus 
#> 
#>  Method:  countreg 
#> Iterations:  44 
#> Convergence:  successful convergence  
#> Log-likelihood:  -1064.876 
#> 
#> Parameter Estimates:
#> (Using bootstrapped standard errors)
#> # A tibble: 7 × 7
#>   parameter           coeff `Std. Err.` `t-stat` `p-value` `lower CI` `upper CI`
#>   <chr>               <dbl>       <dbl>    <dbl>     <dbl>      <dbl>      <dbl>
#> 1 (Intercept)        -7.40        0.043  -172.       0         -7.49      -7.32 
#> 2 lnaadt              0.912       0.005   182.       0          0.902      0.921
#> 3 lnlength            0.843       0.037    22.9      0          0.771      0.915
#> 4 speed50            -0.47        0.102    -4.62     0         -0.669     -0.27 
#> 5 AADT10kplus         0.77        0.089     8.61     0          0.594      0.945
#> 6 ln(alpha):(Interc… -1.62        0.291    -5.57     0         -2.19      -1.05 
#> 7 ln(alpha):speed50   1.31        0.458     2.85     0.004      0.409      2.20

NB-2 Model Summary

teststats <- regCompTest(gen.nb2)
kable(teststats$statistics)

Checking the CURE plot:

cureplot(gen.nb2, indvar  ="lnaadt")
#> Covariate: indvar_values
#> CURE data frame was provided. Its first column, lnaadt, will be used.

Modifying the model to fit better:

gen.nb2 <- countreg(Total_crashes ~ lnaadt  + lnlength + speed50 +
                                ShouldWidth04 + AADT10kplus + 
                                I(AADT10kplus/lnaadt),
                                data = washington_roads, family = "NB2",
                                dis_param_formula_1 = ~ lnlength,  method='BFGS')

kable(summary(gen.nb2), caption = "Modified NB-2 Model Summary")
#> Call:
#>  Total_crashes ~ lnaadt + lnlength + speed50 + ShouldWidth04 +      AADT10kplus + I(AADT10kplus/lnaadt) 
#> 
#>  Method:  countreg 
#> Iterations:  56 
#> Convergence:  successful convergence  
#> Log-likelihood:  -1061.914 
#> 
#> Parameter Estimates:
#> (Using bootstrapped standard errors)
#> # A tibble: 9 × 7
#>   parameter           coeff `Std. Err.` `t-stat` `p-value` `lower CI` `upper CI`
#>   <chr>               <dbl>       <dbl>    <dbl>     <dbl>      <dbl>      <dbl>
#> 1 (Intercept)        -7.68        0.043  -180.       0         -7.76      -7.59 
#> 2 lnaadt              0.93        0.005   188.       0          0.92       0.939
#> 3 lnlength            0.853       0.038    22.6      0          0.779      0.927
#> 4 speed50            -0.4         0.091    -4.38     0         -0.579     -0.221
#> 5 ShouldWidth04       0.261       0.06      4.36     0          0.143      0.378
#> 6 AADT10kplus         5.96        0.092    64.6      0          5.78       6.14 
#> 7 I(AADT10kplus/ln… -50.1         0.938   -53.5      0        -52.0      -48.3  
#> 8 ln(alpha):(Inter…  -1.91        0.324    -5.91     0         -2.55      -1.28 
#> 9 ln(alpha):lnleng…  -0.43        0.244    -1.76     0.078     -0.908      0.048

Modified NB-2 Model Summary

teststats <- regCompTest(gen.nb2)
kable(teststats$statistics)

cureplot(gen.nb2, indvar  ="lnaadt")
#> Covariate: indvar_values
#> CURE data frame was provided. Its first column, lnaadt, will be used.

Estimating another model (NB-P) - without the interaction:

gen.nbp <- countreg(Total_crashes ~ lnaadt  + lnlength + speed50 +
                                ShouldWidth04 + AADT10kplus,
                                data = washington_roads, family = "NBp",
                                dis_param_formula_1 = ~ lnlength,  method='BFGS')
kable(summary(gen.nbp), caption = "NB-P Model Summary")
#> Call:
#>  Total_crashes ~ lnaadt + lnlength + speed50 + ShouldWidth04 +      AADT10kplus 
#> 
#>  Method:  countreg 
#> Iterations:  53 
#> Convergence:  successful convergence  
#> Log-likelihood:  -1062.195 
#> 
#> Parameter Estimates:
#> (Using bootstrapped standard errors)
#> # A tibble: 9 × 7
#>   parameter           coeff `Std. Err.` `t-stat` `p-value` `lower CI` `upper CI`
#>   <chr>               <dbl>       <dbl>    <dbl>     <dbl>      <dbl>      <dbl>
#> 1 (Intercept)        -7.76        0.043 -181.        0         -7.85      -7.68 
#> 2 lnaadt              0.938       0.005  189.        0          0.928      0.948
#> 3 lnlength            0.836       0.037   22.3       0          0.763      0.91 
#> 4 speed50            -0.384       0.093   -4.13      0         -0.567     -0.202
#> 5 ShouldWidth04       0.258       0.059    4.34      0          0.141      0.374
#> 6 AADT10kplus         0.689       0.088    7.87      0          0.518      0.861
#> 7 ln(alpha):(Interc… -1.50        0.294   -5.09      0         -2.07      -0.92 
#> 8 ln(alpha):lnlength -0.167       0.245   -0.682     0.495     -0.648      0.314
#> 9 ln(p)               0.525       0.173    3.03      0.002      0.186      0.864

NB-P Model Summary

teststats <- regCompTest(gen.nbp)
kable(teststats$statistics)

Checking the CURE plot (notice that the CURE plot is MUCH better in this case than the NB-2 without the interaction and still better than the modified NB-2):

cureplot(gen.nbp, indvar  ="lnaadt")
#> Covariate: indvar_values
#> CURE data frame was provided. Its first column, lnaadt, will be used.

Creating a table to compare the models:

regCompTable(list("Generalized NB-2"=gen.nb2, "Generalized NB-P"=gen.nbp), tableType="tibble") |> 
  kable()

Note that the metrics for comparison are similar. While the models both have the same number of parameters, the NB-P was able to get better performance without requiring the interaction terms (which leads to strange relationships between the exposure metric and the outcome).