R: Fitting verification models

psglm {bcROCsurface} | R Documentation

## Fitting verification models

### Description

`psglm` is used to fit generalized linear models to the verification process. This function requires a symbolic formula of the linear predictor, and a specified regression model.

### Usage

    psglm(formula, data, model = "logit", test = FALSE, trace = TRUE, ...)

### Arguments

`formula` | an object of class "formula": a symbolic description of the model to be fitted.
`data` | an optional data frame containing the variables in the model.
`model` | a specified model to be used in the fitting. The suggestion regression models are logit, probit and threshold. If `model` is ignored, then `psglm` use a default model as logit.
`test` | a logical value indicating whether p-values of the regression coefficients should be returned.
`trace` | switch for tracing estimation process. Default `TRUE`.
`...` | optional arguments to be passed to `glm`.

### Details

`psglm` estimates the verification probabilities of the patients. The suggestion model is designed as a list containing: logit, probit and threshold.

### Value

`psglm` returns a list containing the following components:

`coeff` | a vector of estimated coefficients.
`values` | fitted values of the model.
`Hess` | the Hessian of the measure of fit at the estimated coefficients.
`X` | a design model matrix.
`formula` | the formula supplied.
`model` | the model object used.

### See Also

`glm`

### Examples

    data(EOC)
    out <- psglm(V ~ CA125 + CA153 + Age, data = EOC, test = TRUE)

[Package bcROCsurface version 1.0-2 Index]