R: Evaluating Log-Linear Regression Model Fits

predict.ssllrm {gss}

R Documentation

Evaluating Log-Linear Regression Model Fits

Description

Evaluate conditional density in a log-linear regression model fit at arbitrary x, or contrast of log conditional density possibly with standard errors for constructing Bayesian confidence intervals.

Usage

## S3 method for class 'ssllrm'
predict(object, x, y=object$qd.pt, odds=NULL, se.odds=FALSE, ...)

Arguments

`object`	Object of class `"ssllrm"`.
`x`	Data frame of x values.
`y`	Data frame of y values; y-variables must be factors.
`odds`	Optional coefficients of contrast.
`se.odds`	Flag indicating if standard errors are required. Ignored when `odds=NULL`.
`...`	Ignored.

Value

For odds=NULL, predict.ssanova returns a vector/matrix of the estimated f(y|x).

When odds is given, it should match y in length and the coefficients must add to zero; predict.ssanova then returns a vector of estimated "odds ratios" if se.odds=FALSE or a list consisting of the following components if se.odds=TRUE.

`fit`	Vector of evaluated fit.
`se.fit`	Vector of standard errors.

Author(s)