discrete.margin: Calculate marginal effects for binary variables.

In oglmx: Estimation of Ordered Generalized Linear Models

Description

Calculate marginal effects for binary variables. Functions calculate for variables that are only in the mean equation, only in the variance equation, and variables in both.

Usage

discrete.margin_meanonly(beta, X, whichVars, etas, link, std.dev)

discrete.margin_varonly(delta, Z, whichVars, sdmodel, etas, link, std.dev)

discrete.margin_both(beta, X, delta, Z, BothEqLocs, sdmodel, etas, link, std.dev)

Arguments

beta	Coefficients for the mean equation.
X	Variable values for the mean equation.
whichVars	Numeric vector stating indexes of variables that are binary and marginal effects are desired.
etas	Inputs to link functions.
link	specifies the link function for the estimated model.
std.dev	The calculated standard deviation of the error terms.
delta	Coefficients for the variance equation.
Z	Variable values for the variance equation.
sdmodel	Expression used to calculate standard deviation.
BothEqLocs	Dataframe describing locations of binary variables that are in both the mean and variance equations.

Value

Numeric vector of marginal effects. Has as attributes calculated components that are used to calculate derivatives of marginal effects.

Author(s)

Nathan Carroll, [e-mail]

See Also

margins.oglmx

oglmx documentation built on May 2, 2019, 5:14 a.m.