dbr: Discretised Beta Regression for Survey-Response Analysis

In DBR: Discrete Beta Regression

Discretised Beta Regression for Survey-Response Analysis

Description

Discretised Beta Regression for Survey-Response Analysis

Usage

dbr(
  formula
  , data
  , control = dbr.control()
  , yunique = NULL
  , wghts = rep(1, nrow(data))
)
dbr.control(
  nsmp = 100
  , nburnin = 50
  , estimate_left_buffer = FALSE
  , estimate_right_buffer = FALSE
  , buffer_max = 5.0
)

Arguments

formula	Standard R formula describing the response variable and predictors.
data	Data frame containing response and predictors as described in formula.
control	List of parameters for controlling the MCMC estimation.
yunique	Vector of values/levels that the response variable can assume. If not specified, this will be extracted from the data according to the formula.
wghts	Vector of weights to be applied during model estimation. Default is a uniform weight vector.
nsmp	Number of MCMC samples to collect, including the burnin phase.
nburnin	Number of initial MCMC samples to discard before calculating parameter estimates.
estimate_left_buffer	Boolean flag indicating whether to estimate a left buffer from the data.
estimate_right_buffer	Boolean flag indicating whether to estimate a right buffer from the data.
buffer_max	Maximum size of left/right buffer, only used if above flags are set to TRUE.

Value

An object of class dbr, which is a list containing the following fields:

formula	Same as input.
control	Same as input.
yunique	Same as input.
wghts	Same as input.
est	An internal object containing estimation results. Should not be accessed directly by user. Use summary and predict instead.
data	Same as input.

Author(s)

Mansour T.A. Sharabiani, Alireza S. Mahani

See Also

summary.dbr, predict.dbr

Examples

library("DBR")
data("pain")
est <- dbr(
  interference ~ severity + age
  , pain
  , control = dbr.control(
    nsmp = 50
    , nburnin = 25
  )
)

DBR documentation built on March 7, 2023, 7:47 p.m.