# dbbtest: dbbtest In bbreg: Bessel and Beta Regressions via Expectation-Maximization Algorithm for Continuous Bounded Data

## Description

Function to run the discrimination test between beta and bessel regressions (DBB).

## Usage

 `1` ```dbbtest(formula, data, epsilon = 10^(-5), link.mean, link.precision) ```

## Arguments

 `formula` symbolic description of the model (set: z ~ x or z ~ x | v); see details below. `data` arguments considered in the formula description. This is usually a data frame composed by: (i) the response with bounded continuous observations (0 < z_i < 1), (ii) covariates for the mean submodel (columns of matrix x) and (iii) covariates for the precision submodel (columns of matrix v). `epsilon` tolerance value to control the convergence criterion in the Expectation-Maximization algorithm (default = 10^(-5)). `link.mean` a string containing the link function for the mean. The possible link functions for the mean are "logit","probit", "cauchit", "cloglog". `link.precision` a string containing the link function the precision parameter. The possible link functions for the precision parameter are "identity", "log", "sqrt", "inverse".

## Value

Object of class dbbtest, which is a list containing two elements. The 1st one is a table of terms considered in the decision rule of the test; they are sum(z2/n) = sum_i=1^n(z_i^2)/n, sum(quasi_mu) = sum_i=1^n(tildemu_i^2 + tildemu_i(1-tildemu_i)/2) |D_bessel| and |D_beta| as indicated in the main reference. The 2nd term of the list is the name of the selected model (bessel or beta).

`simdata_bes`, `dbessel`, `simdata_bet`
 ```1 2 3``` ```# Illustration using the Weather task data set available in the bbreg package. dbbtest(agreement ~ priming + eliciting, data = WT, link.mean = "logit", link.precision = "identity") ```