flexCountReg (version 0.1.1)

countreg.rp: Random Parameters Count Regression Models

Description

Random Parameters Count Regression Models

Usage

countreg.rp(
  formula,
  rpar_formula,
  data,
  family = "NB2",
  rpardists = NULL,
  dis_param_formula_1 = NULL,
  dis_param_formula_2 = NULL,
  het_mean_formula = NULL,
  het_var_formula = NULL,
  ndraws = 500,
  scrambled = FALSE,
  correlated = FALSE,
  panel_id = NULL,
  weights = NULL,
  offset = NULL,
  method = "BHHH",
  max.iters = 1000,
  start.vals = NULL,
  verbose = FALSE
)

Value

An object of class `countreg` which is a list with the following components:

• model: the fitted model object.

• data: the data frame used to fit the model.

• call: the matched call.

• formula: the formula used to fit the model.

Arguments

formula

an R formula. This formula should specify the outcome and the independent variables that have fixed parameters.

rpar_formula

a symbolic description of the model related specifically to the random parameters. This should not include an outcome variable. If the intercept is random, include it in this formula. If the intercept is fixed, include it in formula but not in rpar_formula.

data

a dataframe that has all of the variables in the formula and rpar_formula.

family

the name of the distribution/model type to estimate. Default is "NB2". Options include "Poisson", "NB1", "NB2", "NBP", "PIG", "Sichel", etc. (See countreg for full list).

rpardists

an optional named vector whose names are the random parameters and values the distribution. The distribution options include normal ("n"), lognormal ("ln"), triangular ("t"), uniform ("u"), and gamma ("g"). If not provided, normal is used.

dis_param_formula_1

a symbolic description of the model for the first parameter of the count distribution (e.g., ln(alpha) for NB2).

dis_param_formula_2

a symbolic description of the model for the second parameter of the count distribution (if applicable).

het_mean_formula

an optional symbolic description of the model for heterogeneity in the means of the random parameters.

het_var_formula

an optional symbolic description of the model for heterogeneity in the variances of the random parameters.

ndraws

the number of Halton draws to use for estimating the random parameters.

scrambled

if the Halton draws should be scrambled.

correlated

if the random parameters should be correlated. If TRUE, only normal distributions are used.

panel_id

an optional variable name (string) or vector defining the panel structure (repeated measures). If provided, the standard errors and likelihood are estimated accounting for the panel structure.

weights

variable name to be used as frequency weights.

offset

variable name to be used as an offset.

method

optimization method (e.g., "BHHH", "BFGS", "NM").

max.iters

maximum number of iterations.

start.vals

optional vector of starting values.

verbose

logical.

Examples

# \donttest{
# Load data
data("washington_roads")
washington_roads$AADT10kplus <- ifelse(washington_roads$AADT > 10000, 1, 0)

# 1. Basic Random Parameters Negative Binomial (NB2)
rp_nb2 <- countreg.rp(Total_crashes ~ lnaadt + lnlength,
                      rpar_formula = ~ -1 + speed50,
                      data = washington_roads,
                      family = "NB2",
                      rpardists = c(speed50 = "n"),
                      ndraws = 100,
                      method = "BHHH")
summary(rp_nb2)

# 2. Random Parameters with Panel Structure (if 'site_id' exists)
# rp_panel <- countreg.rp(Total_crashes ~ -1 + lnaadt,
#                        rpar_formula = ~ speed50,
#                        data = washington_roads,
#                        panel_id = "site_id",
#                        family = "NB2",
#                        ndraws = 100)

# 3. Generalized Random Parameters Model with Heterogeneity
rp_gen <- countreg.rp(Total_crashes ~ lnaadt,
                      rpar_formula = ~ -1 + speed50,
                      dis_param_formula_1 = ~ lnlength, 
                      het_mean_formula = ~ AADT10kplus,
                      data = washington_roads,
                      family = "NB2",
                      rpardists = c(speed50 = "n"),
                      ndraws = 100)
summary(rp_gen)

# 4. Random Parameters Poisson Model with panel specification
rp_poisson <- countreg.rp(Total_crashes ~ lnaadt,
                      rpar_formula = ~ -1 + speed50,
                      dis_param_formula_1 = ~ lnlength, 
                      het_mean_formula = ~ AADT10kplus,
                      data = washington_roads,
                      family = "POISSON",
                      rpardists = c(speed50 = "n"),
                      ndraws = 100,
                      panel_id = "ID")
summary(rp_poisson)
# }