logistic4p.e: Logistic regressions with constrained FP and FN... In logistic4p: Logistic Regression with Misclassification in Dependent Variables

Description

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

Usage

 `1` ```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|)

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

 ```1 2 3 4 5 6 7``` ```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) ```

logistic4p documentation built on May 31, 2017, 4:24 a.m.