logistic4p (version 1.6)

logistic4p.fp: Logistic Regression with FP Misclassification Correction

Description

logistic4p.fp is used to fit logistic regression models with correction of the false positive misclassification in the binary dependent variable.

Usage

logistic4p.fp(x, y, initial, max.iter = 1000, epsilon = 1e-06, detail = FALSE)

Value

estimates

a named matrix of estimates including parameter estimates, standard errors, z-scores, and p-values.

n.iter

an integer giving the number of iteration used

d

the actual max absolute difference of the parameters of the last two iterations.

loglike

loglikelihood evaluated at the parameter estimates.

AIC

Akaike Information Criterion.

BIC

Bayesian Information Criterion.

converged

logical indicating whether the current procedure converged or not.

Arguments

x, y

x is a data frame or data matrix containing the predictor variables and y is the vector of outcomes. The number of rows in x must be the same as the length of y.

initial

starting values for the parameters in the model(FP,FN misclassification parameters and those in the linear predictor); if not specified, the default initials are 0 for the misclassification parameters and estimates obtained from the logistic regression for the parameters in the linear predictor.

max.iter

a positive integer giving the maximal number of iterations; if it is reached, the algorithm will stop.

epsilon

a positive convergence tolerance epsilon.

detail

logical indicating if output should be printed for each iteration.

Author

Haiyan Liu and Zhiyong Zhang

Examples

if (FALSE) {
data(nlsy)
y=nlsy[,1]
x=nlsy[, -1]

mod.fp=logistic4p.fp(x, y, max.iter = 1000, epsilon = 1e-06, detail = FALSE)
}