poissonirr: Incidence rate ratios for a Poisson regression.

In mfx: Marginal Effects, Odds Ratios and Incidence Rate Ratios for GLMs

Description

This function estimates a negative binomial regression model and calculates the corresponding incidence rate ratios.

Usage

poissonirr(formula, data, robust = FALSE, clustervar1 = NULL, 
           clustervar2 = NULL, start = NULL, control = list())

Arguments

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.
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 model.
control	see glm.control.

Details

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

Value

irr	a coefficient matrix with columns containing the estimates, associated standard errors, test statistics and p-values.
fit	the fitted glm object.
call	the matched call.

See Also

poissonmfx, glm

Examples

# 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)

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

Example output

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:
poissonirr(formula = y ~ x, data = data)

Incidence-Rate Ratio:
      IRR Std. Err.      z     P>|z|    
x 1.72454   0.03105 30.268 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mfx documentation built on May 2, 2019, 12:46 p.m.