Marginal effects Summary
It produces the summary table of marginal effects for GLM estimation with GEL. Only implemented for ATEgel.
## S3 method for class 'ategel' marginal(object, ...)
object |
An object of class |
... |
Other arguments for other methods |
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.
Owen, A.B. (2001), Empirical Likelihood. Monographs on Statistics and Applied Probability 92, Chapman and Hall/CRC
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.