logistic: Logistic Regression

In logistic4p: Logistic Regression with Misclassification in Dependent Variables

Description

Fit a logistic regression model.

Usage

logistic(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	a vector of starting values for the parameters in the linear predictor; if not specified, the default initials are 0 for all parameters.
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 output should be printed for 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

data(nlsy)
y=nlsy[,1]
x=nlsy[, -1]
## Not run: 
mod=logistic(x,y)

## End(Not run)

logistic4p documentation built on May 2, 2019, 5:57 p.m.