psglm: 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.

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.

a design model matrix.

formula

the formula supplied.

model

the model object used.

Details

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

Examples

Run this code

# NOT RUN {
data(EOC)
out <- psglm(V ~ CA125 + CA153 + Age, data = EOC, test = TRUE)


# }