negbinmfx: Marginal effects for a negative binomial regression.
In mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs

Description Usage Arguments Details Value See Also Examples

View source: R/negbinmfx.R

This function estimates a negative binomial regression model and calculates the corresponding marginal effects.

1 2	negbinmfx(formula, data, atmean = TRUE, robust = FALSE, clustervar1 = NULL, clustervar2 = NULL, start = NULL, control = glm.control())

`formula`	an object of class “formula” (or one that can be coerced to that class).
`data`	the data frame containing these data. This argument must be used.
`atmean`	default marginal effects represent the partial effects for the average observation. If `atmean = FALSE` the function calculates average partial effects.
`robust`	if `TRUE` the function reports White/robust standard errors.
`clustervar1`	a character value naming the first cluster on which to adjust the standard errors.
`clustervar2`	a character value naming the second cluster on which to adjust the standard errors for two-way clustering.
`start`	starting values for the parameters in the `glm.nb` model.
`control`	see `glm.control`.

If both robust=TRUE and !is.null(clustervar1) the function overrides the robust command and computes clustered standard errors.

`mfxest`	a coefficient matrix with columns containing the estimates, associated standard errors, test statistics and p-values.
`fit`	the fitted `glm.nb` object.
`dcvar`	a character vector containing the variable names where the marginal effect refers to the impact of a discrete change on the outcome. For example, a factor variable.
`call`	the matched call.

negbinirr, glm.nb

# simulate some data
set.seed(12345)
n = 1000
x = rnorm(n)
y = rnegbin(n, mu = exp(1 + 0.5 * x), theta = 0.5)

data = data.frame(y,x)

negbinmfx(formula=y~x,data=data)

Loading required package: sandwich
Loading required package: lmtest
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: MASS
Loading required package: betareg
Call:
negbinmfx(formula = y ~ x, data = data)

Marginal Effects:
    dF/dx Std. Err.      z     P>|z|    
x 1.46147   0.15567 9.3882 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1