tests/testthat/test-all.R

In flexCountReg: Estimation of a Variety of Count Regression Models

# library(flexCountReg)
# 
# ### Name: flexCountReg.predict
# ### Title: Function for generating predictions based on the random
# ###   parameters negative binomial with multiple optional methods
# ### Aliases: flexCountReg.predict
# 
# ### ** Examples
# 
# 
# ## Poisson-Lindley Model
# data("washington_roads")
# washington_roads$AADTover10k <- ifelse(washington_roads$AADT>10000,1,0)
# poislind.mod <- flexCountReg(Animal ~ lnaadt + lnlength + speed50 +
#                                ShouldWidth04 + AADTover10k,
#                              data=washington_roads, dist="Poisson Lindley",
#                              method="BHHH")
# summary(poislind.mod)
# 
# pred <- flexCountReg.predict(poislind.mod, washington_roads)
# 
# hist(pred)
# 
# 
# 
# 
# ### Name: flexCountReg
# ### Title: Estimate flexible count regression models with and without
# ###   random parameters
# ### Aliases: flexCountReg
# 
# ### ** Examples
# 
# ## Poisson-Lindley Model
# data("washington_roads")
# washington_roads$AADTover10k <- ifelse(washington_roads$AADT>10000,1,0)
# poislind.mod <- flexCountReg(Animal ~ lnaadt + lnlength + speed50 +
#                                ShouldWidth04 + AADTover10k,
#                              data=washington_roads, dist="Poisson Lindley",
#                              method="sann")
# summary(poislind.mod)
# 
# 
# 
# 
# ### Name: Generalized-Waring
# ### Title: Generalized Waring Distribution
# ### Aliases: Generalized-Waring dgwar pgwar qgwar rgwar
# 
# ### ** Examples
# 
# dgwar(0, mu=1, alpha=1, rho=3)
# pgwar(c(0,1,2,3), mu=1, alpha=2, rho=3)
# qgwar(c(0.1, 0.5, 0.9), mu=1, alpha=2, rho=3)
# rgwar(10, mu=1, alpha=2, rho=3)
# 
# 
# 
# 
# ### Name: genWaring
# ### Title: Function for estimating a Generalized Waring regression model
# ### Aliases: genWaring
# 
# ### ** Examples
# 
# 
# # Generalized Waring Model
# data("washington_roads")
# genwaring.mod <- genWaring(Total_crashes ~ lnaadt + lnlength,
#                            data=washington_roads,
#                            method='BHHH')
# summary(genwaring.mod)
# 
# 
# 
# ### Name: invgamma
# ### Title: Inverse Gamma Distribution
# ### Aliases: invgamma dinvgamma pinvgamma qinvgamma rinvgamma
# 
# ### ** Examples
# 
# dinvgamma(1, shape=3, scale=2)
# pinvgamma(c(0.1, 0.5, 1, 3, 5, 10, 30), shape=3, scale=2)
# qinvgamma(c(0.1,0.3,0.5,0.9,0.95), shape=3, scale=2)
# rinvgamma(30, shape=3, scale=2)
# 
# 
# 
# 
# ### Name: mae
# ### Title: Calculate Mean Absolute Error (MAE)
# ### Aliases: mae
# 
# ### ** Examples
# 
# y <- c(1, 2, 3)
# mu <- c(1.1, 1.9, 3.2)
# mae(y, mu)
# 
# 
# 
# 
# ### Name: mgf_lognormal
# ### Title: Moment Generating Function for a Lognormal Distribution
# ### Aliases: mgf_lognormal
# 
# ### ** Examples
# 
# mu <- 0
# sigma <- 1
# n <- 1
# mgf_value <- mgf_lognormal(mu, sigma, n)
# print(mgf_value)
# 
# 
# ### Name: myAIC
# ### Title: Calculate Akaike Information Criterion (AIC)
# ### Aliases: myAIC
# 
# ### ** Examples
# 
# LL <- -120.5
# nparam <- 5
# myAIC(LL, nparam)
# 
# 
# 
# 
# ### Name: myBIC
# ### Title: Calculate Bayesian Information Criterion (BIC)
# ### Aliases: myBIC
# 
# ### ** Examples
# 
# LL <- -120.5
# nparam <- 5
# n <- 100
# myBIC(LL, nparam, n)
# 
# 
# 
# 
# ### Name: nbg
# ### Title: Function for estimating a variety of negative binomial models
# ###   (NB-1, NB-2, NB-P and generalized versions of each)
# ### Aliases: nbg
# 
# ### ** Examples
# 
# 
# ## NB-P model
# data("washington_roads")
# washington_roads$AADTover10k <- ifelse(washington_roads$AADT>10000,1,0)
# 
# nbp.base <- nbg(Total_crashes ~ lnaadt + lnlength + speed50 +
#                   ShouldWidth04 + AADTover10k,
#                 data=washington_roads, form = 'nbp', method = 'BHHH',
#                 max.iters=3000)
# summary(nbp.base)
# 
# ## Generalized NB-P model
# 
# nbp.overdispersion <- nbg(Total_crashes ~ lnaadt + lnlength + speed50 +
#                             ShouldWidth04 + AADTover10k,
#                           data=washington_roads,
#                           form = 'nbp',
#                           method = 'NM',
#                           max.iters=3000,
#                           ln.alpha.formula = ~ 1+lnlength)
# summary(nbp.overdispersion)
# 
# ## NB-1 Model
# nb1.base <- nbg(Total_crashes ~ lnaadt + lnlength + speed50 +
#                   ShouldWidth04 + AADTover10k,
#                 data=washington_roads, form = 'nb1',
#                 method = 'NM',
#                 max.iters=3000)
# summary(nb1.base)
# 
# ## Generalize NB-1 Model
# nb1.overdispersion <- nbg(Total_crashes ~ lnaadt + lnlength + speed50 +
#                             ShouldWidth04 + AADTover10k,
#                           data=washington_roads, form = 'nb1',
#                           method = 'NM',
#                           max.iters=3000, ln.alpha.formula = ~ 1+lnlength)
# summary(nb1.overdispersion)
# 
# ## NB-2 Model
# nb2.base <- nbg(Total_crashes ~ lnaadt + lnlength + speed50 +
#                   ShouldWidth04 + AADTover10k,
#                 data=washington_roads, form = 'nb2',
#                 method = 'BFGS',
#                 max.iters=3000)
# summary(nb2.base)
# 
# ## Generalize NB-2 Model
# nb2.overdispersion <- nbg(Total_crashes ~ lnaadt + lnlength + speed50 +
#                             ShouldWidth04 + AADTover10k,
#                           data=washington_roads, form = 'nb2',
#                           method = 'NM',
#                           max.iters=3000, ln.alpha.formula = ~ 1+lnlength)
# summary(nb2.overdispersion)
# 
# 
# 
# 
# ### Name: Negative-Binomial-Lindley
# ### Title: Poisson-Lindley-Gamma (Negative Binomial-Lindley) Distribution
# ### Aliases: Negative-Binomial-Lindley dplindGamma pplindGamma qplindGamma
# ###   rplindGamma
# 
# ### ** Examples
# 
# dplindGamma(0, mean=0.75, theta=7, alpha=2, ndraws=100)
# pplindGamma(c(0,1,2,3,5,7,9,10), mean=0.75, theta=7, alpha=2, ndraws=100)
# qplindGamma(c(0.1,0.3,0.5,0.9,0.95), lambda=4.67, theta=7, alpha=2,
#             ndraws=100)
# rplindGamma(1, lambda=4.67, theta=7, alpha=2, ndraws=100)
# 
# 
# 
# 
# ### Name: poisGE
# ### Title: Poisson-Generalized-Exponential Regression
# ### Aliases: poisGE
# 
# ### ** Examples
# 
# # Generalized Poisson-Generalized-Exponential
# data("washington_roads")
# washington_roads$AADTover10k <- ifelse(washington_roads$AADT>10000,1,0)
# 
# poisge.mod <- poisGE(Total_crashes ~ lnaadt + lnlength + speed50 +
#                        ShouldWidth04 + AADTover10k,
#                      ln.scale.formula = ~ lnaadt,
#                      data=washington_roads[1:500,], 
#                      ndraws = 10,
#                      max.iters = 500)
# summary(poisge.mod)
# 
# 
# 
# 
# ### Name: poisInvGaus
# ### Title: Function for estimating a Poisson-Inverse-Gaussian regression
# ###   model
# ### Aliases: poisInvGaus
# 
# ### ** Examples
# 
# # Poisson-Inverse-Gaussian Model
# data("washington_roads")
# washington_roads$AADTover10k <- 
#   ifelse(washington_roads$AADT>10000,1,0) # create a dummy variable
# 
# poisinvgaus.mod <- poisInvGaus(Total_crashes ~ lnaadt + lnlength + speed50 + 
#                                  ShouldWidth04 + AADTover10k,
#                                data=washington_roads,
#                                method="nm",
#                                max.iters = 1000)
# summary(poisinvgaus.mod)
# 
# 
# 
# ### Name: poisLind
# ### Title: Function for estimating a Poisson-Lindley regression model
# ### Aliases: poisLind
# 
# ### ** Examples
# 
# # Poisson-Lindley Model
# data("washington_roads")
# washington_roads$AADTover10k <- 
#   ifelse(washington_roads$AADT>10000,1,0) # create a dummy variable
# poislind.mod <- poisLind(
#   Animal ~ lnaadt + lnlength + speed50 + ShouldWidth04 + AADTover10k,
#   data=washington_roads,
#   method="nm",
#   max.iters = 1000)
# summary(poislind.mod)
# 
# 
# 
# ### Name: poisLindGamma
# ### Title: Function for estimating a Poisson-Lindley-Gamma (i.e., Negative
# ###   Binomial-Lindley) regression model
# ### Aliases: poisLindGamma
# 
# ### ** Examples
# 
# 
# ## Poisson-Lindley-Gamma Model
# data("washington_roads")
# washington_roads$AADTover10k <- 
#   ifelse(washington_roads$AADT>10000,1,0) # create a dummy variable
# poislindgamma.mod <- poisLindGamma(Animal ~ lnaadt + lnlength + speed50 +
#                                      ShouldWidth04 + AADTover10k,
#                                    data=washington_roads,
#                                    ndraws=10,
#                                    max.iters = 1000)
# summary(poislindgamma.mod)
# 
# 
# 
# ### Name: poisLindLnorm
# ### Title: Function for estimating a Poisson-Lindley-Lognormal regression
# ###   model
# ### Aliases: poisLindLnorm
# 
# ### ** Examples
# 
# 
# ## Poisson-Lindley-Lognormal Model
# data("washington_roads")
# washington_roads$AADTover10k <- 
#   ifelse(washington_roads$AADT>10000,1,0) # create a dummy variable
# poislindlnorm.mod <- poisLindLnorm(Animal ~ lnaadt + lnlength + speed50 
#                                    + ShouldWidth04 + AADTover10k,
#                                    data=washington_roads,
#                                    method="nm",
#                                    ndraws=10,
#                                    max.iters = 1000)
# summary(poislindlnorm.mod)
# 
# 
# 
# ### Name: poisLogn
# ### Title: Poisson-Lognormal Regression
# ### Aliases: poisLogn
# 
# ### ** Examples
# 
# 
# # Generalized Poisson-Lognormal
# data("washington_roads")
# washington_roads$AADTover10k <- ifelse(washington_roads$AADT>10000,1,0)
# 
# poslogn.mod <- poisLogn(Total_crashes ~ lnaadt + lnlength + speed50 +
#                           ShouldWidth04 + AADTover10k,
#                         ln.sigma.formula = ~ lnaadt + AADTover10k,
#                         data=washington_roads, 
#                         ndraws = 10, 
#                         method = 'BHHH')
# summary(poslogn.mod)
# 
# 
# 
# 
# ### Name: Poisson-Generalized-Exponential
# ### Title: Poisson-Generalized-Exponential Distribution
# ### Aliases: Poisson-Generalized-Exponential dpge ppge qpge rpge
# 
# ### ** Examples
# 
# dpge(0, mean=0.75, shape=2, scale=1, ndraws=100)
# ppge(c(0,1,2,3,4,5,6), mean=0.75, shape=2, scale=1, ndraws=100)
# qpge(c(0.1,0.3,0.5,0.9,0.95), mean=0.75, shape=2, scale=1, ndraws=100)
# rpge(30, mean=0.75,  shape=2, scale=1, ndraws=100)
# 
# 
# 
# 
# ### Name: Poisson-Inverse-Gaussian
# ### Title: Poisson-Inverse-Gaussian Distribution
# ### Aliases: Poisson-Inverse-Gaussian dpinvgaus ppinvgaus qpinvgaus
# ###   rpinvgaus
# 
# ### ** Examples
# 
# dpinvgaus(1, mu=0.75, eta=1)
# ppinvgaus(c(0,1,2,3,5,7,9,10), mu=0.75, eta=3, form="Type 2")
# qpinvgaus(c(0.1,0.3,0.5,0.9,0.95), mu=0.75, eta=0.5, form="Type 2")
# rpinvgaus(30, mu=0.75, eta=1.5)
# 
# dpinvgaus(1, mu=0.75, eta=1)
# ppinvgaus(c(0,1,2,3,5,7,9,10), mu=0.75, eta=3, form="Type 2")
# qpinvgaus(c(0.1,0.3,0.5,0.9,0.95), mu=0.75, eta=0.5, form="Type 2")
# rpinvgaus(30, mu=0.75, eta=1.5)
# 
# 
# 
# 
# ### Name: Poisson-Lindley-Lognormal
# ### Title: Poisson-Lindley-Lognormal Distribution
# ### Aliases: Poisson-Lindley-Lognormal dplindLnorm pplindLnorm qplindLnorm
# ###   rplindLnorm
# 
# ### ** Examples
# 
# dplindLnorm(0, mean=0.75, theta=7, sigma=2, ndraws=100)
# pplindLnorm(c(0,1,2,3,5,7,9,10), mean=0.75, theta=7, sigma=2, ndraws=100)
# qplindLnorm(
#   c(0.1,0.3,0.5,0.9,0.95), lambda=4.67, theta=7, sigma=2, ndraws=100)
# rplindLnorm(30, mean=0.75, theta=7, sigma=2, ndraws=100)
# 
# 
# 
# 
# ### Name: Poisson-Lindley
# ### Title: Poisson-Lindley Distribution
# ### Aliases: Poisson-Lindley dplind pplind qplind rplind
# 
# ### ** Examples
# 
# dplind(0, mean=0.75, theta=7)
# pplind(c(0,1,2,3,5,7,9,10), mean=0.75, theta=7)
# qplind(c(0.1,0.3,0.5,0.9,0.95), lambda=4.67, theta=7)
# rplind(30, mean=0.75, theta=7)
# 
# 
# 
# 
# ### Name: Poisson-Lognormal
# ### Title: Poisson-Lognormal Distribution
# ### Aliases: Poisson-Lognormal dpLnorm ppLnorm qpLnorm rpLnorm
# 
# ### ** Examples
# 
# dpLnorm(0, mean=0.75, sigma=2, ndraws=100)
# ppLnorm(c(0,1,2,3,5,7,9,10), mean=0.75, sigma=2, ndraws=100)
# qpLnorm(c(0.1,0.3,0.5,0.9,0.95), mean=0.75, sigma=2, ndraws=100)
# rpLnorm(30, mean=0.75,  sigma=2, ndraws=100)
# 
# 
# 
# 
# ### Name: PoissonWeibull
# ### Title: Poisson-Weibull Distribution Functions
# ### Aliases: PoissonWeibull dpoisweibull ppoisweibull qpoisweibull
# ###   rpoisweibull
# 
# ### ** Examples
# 
# dpoisweibull(4, alpha = 1.5, beta = 0.5)
# ppoisweibull(4, alpha = 1.5, beta = 0.5)
# qpoisweibull(0.95, alpha = 1.5, beta = 0.5)
# rpoisweibull(10, alpha = 1.5, beta = 0.5)
# 
# 
# 
# 
# ### Name: pwiebreg
# ### Title: Poisson-Weibull Regression with Optional Random Parameters
# ### Aliases: pwiebreg
# 
# ### ** Examples
# 
# data("washington_roads")
# pw_rp <- pwiebreg(Total_crashes ~ lnlength + lnaadt,
#                   rpar_formula = ~ speed50,
#                   alpha_formula = ~ lnlength,
#                   beta_formula = ~ lnaadt,
#                   data = washington_roads,
#                   ndraws = 10,
#                   correlated = FALSE)
# print(summary(pw_rp))
# 
# 
# 
# 
# ### Name: regCompTest
# ### Title: Compare Regression Models with Likelihood Ratio Test, AIC, and
# ###   BIC
# ### Aliases: regCompTest
# 
# ### ** Examples
# 
# 
# # Comparing the NBP model with the NB2 model
# data("washington_roads")
# washington_roads$AADTover10k <- ifelse(washington_roads$AADT>10000,1,0)
# 
# nbp.base <- nbg(Total_crashes ~ lnaadt + lnlength + speed50 +
#                   ShouldWidth04 + AADTover10k,
#                 data=washington_roads, form = 'nbp', method = 'NM',
#                 max.iters=3000)
# comptests <- 
#   regCompTest(nbp.base, washington_roads, basemodel="NB2", print=TRUE)
# 
# 
# 
# 
# ### Name: rmse
# ### Title: Calculate Root Mean Squared Error (RMSE)
# ### Aliases: rmse
# 
# ### ** Examples
# 
# y <- c(1, 2, 3)
# mu <- c(1.1, 1.9, 3.2)
# rmse(y, mu)
# 
# 
# 
# 
# ### Name: rpnb.predict
# ### Title: Function for generating predictions based on the random
# ###   parameters negative binomial with multiple optional methods
# ### Aliases: rpnb.predict
# 
# ### ** Examples
# 
# ## No test: 
# 
# ## Random Parameters Negative Binomial model (NB-2)
# 
# data("washington_roads")
# 
# nb2.rp <- rpnb(Total_crashes ~ - 1 + lnlength + lnaadt,
#                rpar_formula = ~ speed50,
#                data = washington_roads,
#                ndraws = 10,
#                correlated = TRUE,
#                form = 'nb2',
#                method = "bfgs",
#                print.level = 1)
# 
# ## Exact Prediction
# hist(rpnb.predict(nb2.rp, washington_roads))
# 
# ## Simulated Prediction
# hist(rpnb.predict(nb2.rp, washington_roads, method="Simulated"))
# 
# ## Individual-Specific Coefficient Based Prediction
# hist(rpnb.predict(nb2.rp, washington_roads, method="Individual"))
# ## End(No test)
# 
# 
# 
# ### Name: rpnb
# ### Title: Function for estimating a random parameter negative binomial
# ###   with the ability to specify if the NB-1, NB-2, or NB-P should be used
# ### Aliases: rpnb
# 
# ### ** Examples
# 
# ## Not run: 
# ##D ## Random Parameters Negative Binomial model (NB-2)
# ##D data("washington_roads")
# ##D nb2.rp <- rpnb(Total_crashes ~ - 1 + lnlength + lnaadt,
# ##D                rpar_formula = ~ speed50,
# ##D                data = washington_roads,
# ##D                ndraws = 10,
# ##D                correlated = TRUE,
# ##D                form = 'nb2',
# ##D                method = "nr",
# ##D                print.level = 1)
# ##D 
# ##D summary(nb2.rp)
# ## End(Not run)
# 
# 
# 
# ### Name: rppoisson
# ### Title: Random Parameters Poisson Model
# ### Aliases: rppoisson
# 
# ### ** Examples
# 
# ## Not run: 
# ##D data("washington_roads")
# ##D poisson_rp <- rppoisson(Total_crashes ~ lnlength + lnaadt,
# ##D                         rpar_formula = ~ speed50,
# ##D                         data = washington_roads,
# ##D                         ndraws = 10,
# ##D                         correlated = FALSE)
# ##D print(summary(poisson_rp))
# ## End(Not run)
# 
# 
# 
# ### Name: Sichel-Distribution
# ### Title: Sichel Distribution
# ### Aliases: Sichel-Distribution dsichel psichel qsichel rsichel
# 
# ### ** Examples
# 
# dsichel(1, mu=0.75, sigma=1, gamma=-3)
# psichel(c(0,1,2,3,5,7,9,10), mu=0.75, sigma=2, gamma=3)
# qsichel(c(0.1,0.3,0.5,0.9,0.95), mu=0.75, sigma=1, gamma=15)
# rsichel(30, mu=0.75, sigma=0.5, gamma=1)
# 
# 
# 
# 
# ### Name: sichel
# ### Title: Function for estimating a Sichel regression model
# ### Aliases: sichel
# 
# ### ** Examples
# 
# 
# # Sichel Model
# data("washington_roads")
# 
# sichel.mod <- sichel(Total_crashes ~ lnaadt + lnlength,
#                      data=washington_roads,
#                      method="NM",
#                      max.iters = 1000)
# summary(sichel.mod)
# 
# 
# 
# ### Name: summary.boot
# ### Title: Custom summary method for models with bootstrapped standard
# ###   errors
# ### Aliases: summary.boot
# 
# ### ** Examples
# 
# # Poisson-Weibull
# pw_rp <- pwiebreg(Total_crashes ~ lnlength + lnaadt,
#                   data = washington_roads,
#                   ndraws = 10,
#                   bootstraps = 10)
# summary.boot(pw_rp)
# 
# 
# 
# 
# ### Name: summary.maxLik
# ### Title: Custom summary method for maxLik objects
# ### Aliases: summary.maxLik
# 
# ### ** Examples
# 
# # NB2 Model
# data("washington_roads")
# washington_roads$AADTover10k <- ifelse(washington_roads$AADT>10000,1,0)
# 
# nb2.base <- nbg(Total_crashes ~ lnaadt + lnlength + speed50 +
#                   ShouldWidth04 + AADTover10k,
#                 data=washington_roads, form = 'nb2',
#                 method = 'NR',
#                 max.iters=3000)
# 
# summary.maxLik(nb2.base)           
# 
# 
# 
# 
# ### Name: Triangular
# ### Title: Triangle Distribution
# ### Aliases: Triangular dtri ptri qtri rtri
# 
# ### ** Examples
# 
# dtri(4, mode=8, upper=13, lower=1)
# ptri(c(0, 1, 2, 3, 5, 7, 9, 10), mode = 3, upper=9, lower = 1)
# qtri(c(0.1, 0.3, 0.5, 0.9, 0.95), mode = 3, upper = 9, lower = 1)
# rtri(30, mode = 5, sigma = 3)