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 elements if `se.odds=TRUE`.

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

### See Also

Fitting function `ssllrm`.

[Package gss version 2.2-2 Index]