Logistic regressions with constrained FP and FN misclassifications

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Description

Fit logistic regressions with misclassification correction. The FP and FN parameters are constrained to be equal.

Usage

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

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(the misclassification parameter 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; the iterations converge when max(|par-par_old|)<epsilon.

detail

logical indicating if the itermediate output should be printed after each iteration.

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, d=max(|par.final-par_old|).

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.

Author(s)

Haiyan Liu and Zhiyong Zhang

Examples

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data(nlsy)
y=nlsy[,1]
x=nlsy[, -1]
## Not run: 
mod=logistic4p.e(x, y, max.iter = 1000, epsilon = 1e-06, detail = FALSE)

## End(Not run)