beta.regs: Many simple beta regressions.

In MXM: Feature Selection (Including Multiple Solutions) and Bayesian Networks

Many simple beta regressions.

Description

Many simple beta regressions.

Usage

beta.regs(target, dataset, wei = NULL, check = FALSE, logged = FALSE, ncores = 1) 

perm.betaregs(target, dataset, wei = NULL, check = FALSE, logged = FALSE, 
threshold = 0.05, R = 999, ncores = 1)

wald.betaregs(target, dataset, wei = NULL, check = FALSE, logged = FALSE, ncores = 1) 

Arguments

target	The target (dependent) variable. It must be a numerical vector with integers.
dataset	The indendent variable(s). It can be a vector, a matrix or a dataframe with continuous only variables, a data frame with mixed or only categorical variables.
wei	A vector of weights to be used for weighted regression. The default value is NULL. An example where weights are used is surveys when stratified sampling has occured.
check	A boolean variable indicating whether to check for variables with identical values. The default is FALSE.
logged	A boolean variable; it will return the logarithm of the p-value if set to TRUE.
threshold	Threshold (suitable values in (0, 1)) for assessing p-values significance. Default value is 0.05.
R	The number of permutations, set to 999 by default. There is a trick to avoind doing all permutations. As soon as the number of times the permuted test statistic is more than the observed test statistic is more than 50 (in this example case), the p-value has exceeded the signifiance level (threshold value) and hence the predictor variable is not significant. There is no need to continue do the extra permutations, as a decision has already been made.
ncores	The number of cores to use. The default value is 1.

Details

Many simple beta regressions are fitted.

Value

A matrix with the test statistic values, their relevant (logged) p-values and the BIC values.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris [e-mail]

References

Ferrari S.L.P. and Cribari-Neto F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7): 799-815.

See Also

beta.mod, testIndBeta, reg.fit, ridge.reg

Examples

y <- rbeta(80, 5, 3)
x <- matrix( rnorm(80 * 7), ncol = 7)
a1 <- beta.regs(y, x)
a2 <- perm.betaregs(y, x[, 1:4], R = 299)

MXM documentation built on Aug. 25, 2022, 9:05 a.m.