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Last data update: 2014.03.03

R: Odds ratios for a beta regression.

betaor

R Documentation

Odds ratios for a beta regression.

Description

This function estimates a beta regression model and calculates the corresponding odds ratios.

Usage

betaor(formula, data, robust = FALSE, clustervar1 = NULL, clustervar2 = NULL, 
       control = betareg.control(), link.phi = NULL, type = "ML")

Arguments

`formula`	an object of class “formula” (or one that can be coerced to that class).
`data`	the data frame containing these data. This argument must be used.
`robust`	if `TRUE` the function reports White/robust standard errors.
`clustervar1`	a character value naming the first cluster on which to adjust the standard errors.
`clustervar2`	a character value naming the second cluster on which to adjust the standard errors for two-way clustering.
`control`	a list of control arguments specified via `betareg.control`.
`link.phi`	as in the `betareg` function.
`type`	as in the `betareg` function.

Details

The underlying link function in the mean model (mu) is "logit". If both robust=TRUE and !is.null(clustervar1) the function overrides the robust command and computes clustered standard errors.

Value

`oddsratio`	a coefficient matrix with columns containing the estimates, associated standard errors, test statistics and p-values.
`fit`	the fitted `betareg` object.
`call`	the matched call.

References

Francisco Cribari-Neto, Achim Zeileis (2010). Beta Regression in R. Journal of Statistical Software 34(2), 1-24.

Bettina Gruen, Ioannis Kosmidis, Achim Zeileis (2012). Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned. Journal of Statistical Software, 48(11), 1-25.

Examples

# simulate some data
set.seed(12345)
n = 1000
x = rnorm(n)

# beta outcome
y = rbeta(n, shape1 = plogis(1 + 0.5 * x), shape2 = (abs(0.2*x)))
# use Smithson and Verkuilen correction
y = (y*(n-1)+0.5)/n

data = data.frame(y,x)
betaor(y~x|x, data=data)