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

Description Usage Arguments Value Author(s)

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.

margins.oglmx(object, atmeans = TRUE, AME = FALSE, location = NULL, outcomes = "All",
              ascontinuous = FALSE, Vars = NULL)

## S3 method for class 'margins.oglmx'
print(x, ... )

`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`.

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.

Nathan Carroll, nathan.carroll@ur.de

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