gmm (version 1.8)

marginal: Marginal effects Summary

Description

It produces the summary table of marginal effects for GLM estimation with GEL. Only implemented for ATEgel.

Usage

# S3 method for ategel
marginal(object, ...)

Value

It returns a matrix with the marginal effects, the standard errors based on the Delta method when the link is nonlinear, the t-ratios, and the pvalues.

Arguments

object

An object of class ategel returned by the function ATEgel

...

Other arguments for other methods

References

Owen, A.B. (2001), Empirical Likelihood. Monographs on Statistics and Applied Probability 92, Chapman and Hall/CRC

Examples

## We create some artificial data with unbalanced groups and binary outcome
genDat <- function(n)
    {
        eta=c(-1, .5, -.25, -.1)
        Z <- matrix(rnorm(n*4),ncol=4)
        b <- c(27.4, 13.7, 13.7, 13.7)
        bZ <- c(Z%*%b)
        Y1 <- as.numeric(rnorm(n, mean=210+bZ)>220)
        Y0 <- as.numeric(rnorm(n, mean=200-.5*bZ)>220)
        etaZ <- c(Z%*%eta)
        pZ <- exp(etaZ)/(1+exp(etaZ))
        T <- rbinom(n, 1, pZ)
        Y <- T*Y1+(1-T)*Y0
        X1 <- exp(Z[,1]/2)
        X2 <- Z[,2]/(1+exp(Z[,1]))
        X3 <- (Z[,1]*Z[,3]/25+0.6)^3
        X4 <- (Z[,2]+Z[,4]+20)^2
        data.frame(Y=Y, cbind(X1,X2,X3,X4), T=T)
    }

dat <- genDat(200)
res <- ATEgel(Y~T, ~X1+X2+X3+X4, data=dat, type="ET", family="logit")
summary(res)

marginal(res)