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Beta regression.
beta.reg(y, x, xnew = NULL)The response variable. It must be a numerical vector with proportions excluding 0 and 1.
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.
If you have new values for the predictor variables (dataset) whose response values you want to predict insert them here.
A list including:
The estimated precision parameter.
A matrix with the estimated regression parameters, their standard errors, Wald statistics and associated p-values.
The log-likelihood of the regression model.
The estimated values if xnew is not NULL.
Beta regression is fitted.
Ferrari S.L.P. and Cribari-Neto F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7): 799-815.
# NOT RUN {
y <- rbeta(300, 3, 5)
x <- matrix( rnorm(300 * 2), ncol = 2)
beta.reg(y, x)
# }
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