betabin {sensR}R Documentation

Beta-Binomial model to overdispersed data

Description

Fits the beta binomial model to data.

Usage

betabin(data, start = c(.5,.5), method = c("mu-gamma", "alpha-beta"),
        vcov = TRUE, ...)

## S3 method for class 'betabin':
summary(object, alpha=.05, ...)

Arguments

object an object of class "betabin", ie. the result of betabin().
alpha the allowed type I error for confidence intervals
data matrix or data.frame with two columns; first column contains the number of success and the second the total numnber of cases. The number of rows should correspond to the number of observations.
start starting values to be used in the optimization
vcov logical, should the variance-covariance matrix of the parameters be computed?
method The desired representation. Note that while the "mu-gamma" is often the most natural and easiest to interpret, it can cause convergence problems when overdispersion is close to non-existence.
... additional arguments passed to optim in betabin. Not used in summary.betabin.

Details

The following additional methods are implemented objects of class betabin: print, vcov, logLik and coef.

Value

An object of class betabin with elements

coef named vector of coefficients
vcov variance-covariance matrix of the parameter estimates
data a named vector with the data supplied to the function
call the matched call
logLik the value of the log-likelihood at the MLEs
method the method used for the fit
convergence 0 indicates convergence. For other error messages, see ?optim.
message possible error messsage - see ?optim for details
counts the number of iterations used in the optimization - see ?optim for details

Author(s)

Rune Haubo B Christensen

References

Brockhoff, P.B. (2003). The statistical power of replications in difference tests.

See Also

triangle, twoAFC, threeAFC, duotrio, discrimPwr, discrimSim, discrimSS, samediff, AnotA, findcr

Examples

## Create data:
x <- c(3,2,6,8,3,4,6,0,9,9,0,2,1,2,8,9,5,7)
n <- c(10,9,8,9,8,6,9,10,10,10,9,9,10,10,10,10,9,10)
dat <- data.frame(x, n)

(bb <- betabin(dat, method = "mu-gamma"))
summary(bb)
vcov(bb)
logLik(bb)
AIC(bb)
coef(bb)


[Package sensR version 1.0.0 Index]