# margins.oglmx: Calculate marginal effects for 'oglmx' objects. In oglmx: Estimation of Ordered Generalized Linear Models

## Description

This function constructs marginal effects and calculates standard errors for all models estimated by the `oglmx` function. Standard errors are obtained using the delta method.

## Usage

 ```1 2 3 4 5``` ```margins.oglmx(object, atmeans = TRUE, AME = FALSE, location = NULL, outcomes = "All", ascontinuous = FALSE, Vars = NULL) ## S3 method for class 'margins.oglmx' print(x, ... ) ```

## Arguments

 `object` object of class "`oglmx`". `Vars` vector specifying variables for which marginal effects are desired. `outcomes` either character string "`All`", the default option, or a numeric vector indicating the outcomes for which the marginal effect is desired. `atmeans` logical. If `TRUE` then the marginal effects are calculated at the means of the variables in the equations for the mean and variance of the latent variable. `AME` logical. If `TRUE` the marginal effects are averaged across observations. `ascontinuous` logical. If `TRUE` binary variables are treated as if continuous to calculate marginal effects.
 `location` `NULL`, a numeric vector, or a list containing two numeric vectors. Allows the user to specify the values of the explanatory variables at which the marginal effect is to be calculated. For a homoskedastic model the input should be a numeric vector of length equal to the number of variables in the model matrix. For a heterskedastic model the input should be a list, the first element should be a vector of length equal to the number of variables in the mean equation and the second is a vector of length equal to the number of variables in the variance equation. `...` additional arguments to `print` method. Currently ignored. `x` object of class `margins.oglmx`.

## Value

an object of class `margins.oglmx`. The object consists of a list containing data matrices, each matrix corresponding to an outcome for which the marginal effect was desired. Columns of each matrix correspond to the estimated marginal effect, its standard error, t-statistics and two sided p-value.

## Author(s)

Nathan Carroll, [email protected]

oglmx documentation built on May 5, 2018, 5:04 p.m.