glm_function: Fitting Logit Models

In CatherineAmmann/regression: Linear Regression and Logistic Regression

Description

The function "glm_function" is used to fit logit models, specified by giving a symbolic description of the linear predictor and a description of the error distribution.

Usage

  glm_function(Y, X, data, stval)

Arguments

Y	the dependent variable is a dichotomous dummy, taking the values of 0 and 1.
X	the independent variable must include the X-values (X = cbind(X1, X2, ..., Xk)).
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
stval	a named list of starting values for the parameters in the model (stval = c()).

Details

The "logit model" is a regression model where the dependent variable is categorical. This function covers the case of a binary dependent variable - that is, where the output can take only two values, 0 and 1, which represent outcomes such as pass/fail or win/lose. How can we interpret the output? The coefficients are on the linked scale - so a direct interpretation is not possible. The only thing we can interpret is the direction. A positive coefficient means an increase of the probability, a negative means a decrease.

Author(s)

Catherine Ammann: ([e-mail]) and Sergio Roethlisberger: ([e-mail])

Examples

## model = glm_function(my.data$Y, cbind(my.data$X1, my.data$X2), my.data, c(z0, z1, ..., zk))
## model

CatherineAmmann/regression documentation built on May 28, 2019, 12:17 p.m.