dbbtest: dbbtest

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

dbbtest

Description

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

Usage

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).

See Also

simdata_bes, dbessel, simdata_bet

Examples

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

bbreg documentation built on March 18, 2022, 6:21 p.m.