rhoMLogit: Fitting disease models via multinomial logistic models

Description

rhoMLogit is used to fit multinomial logistic models to the disease process in the verified subjects.

Usage

rhoMLogit(formula, data, test = FALSE, maxit = 500, trace = FALSE)

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.

test

a logical value indicating whether p-values of the regression coefficients should be returned. Default FALSE.

maxit

maximum number of iterations. Default 500.

trace

switch for tracing estimation process. Default FALSE.

Value

rhoMLogit 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.

the disease status vector used.

a design model matrix.

formula

the fomular supplied.

Details

In the formula, the response must be a result of preDATA, a factor with three levels, say 1, 2, 3. These levels correspond to three classes of disease status, e.g., non-dieseased, intermediate, diseased. The last class (class 3) is considered as the reference level in multinomal logistic model. In presence of verification bias, the missing (NA) values correspond to non verified subjects.

References

To Duc, K., Chiogna, M. and Adimari, G. (2016) Bias-corrected methods for estimating the receiver operating characteristic surface of continuous diagnostic tests. Electronic Journal of Statistics, 10, 3063-3113.

Examples

Run this code

# NOT RUN {
data(EOC)
Dna <- preDATA(EOC$D, EOC$CA125)
Dfact.na <- Dna$D
out <- rhoMLogit(Dfact.na ~ CA125 + CA153 + Age, data = EOC, test = TRUE,
                 trace = TRUE)

# }

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