iWeigReg (version 1.1)

loglik: The non-calibrated objective function ("log-likelihood")

Description

This function computes the objective function, its gradient and its Hessian matrix for the non-calibrated likelihood estimator in Tan (2006), JASA.

Usage

loglik(lam, tr, h)

Arguments

lam

A vector of parameters ("lambda").

tr

A vector of non-missing or treatment indicators.

h

A constraint matrix.

Value

value

The value of the objective function.

gradient

The gradient of the objective function.

hessian

The Hessian matrix of objective function.

References

Tan, Z. (2006) "A distributional approach for causal inference using propensity scores," Journal of the American Statistical Association, 101, 1619-1637.

Tan, Z. (2010) "Bounded, efficient and doubly robust estimation with inverse weighting," Biometrika, 97, 661-682.

Examples

# NOT RUN {
data(KS.data)
attach(KS.data)
z=cbind(z1,z2,z3,z4)
x=cbind(x1,x2,x3,x4)

#logistic propensity score model, correct
ppi.glm <- glm(tr~z, family=binomial(link=logit))
p <- ppi.glm$fitted

#outcome regression model, misspecified
y.fam <- gaussian(link=identity)

eta1.glm <- glm(y ~ x, subset=tr==1, 
               family=y.fam, control=glm.control(maxit=1000))
eta1.hat <- predict.glm(eta1.glm, 
               newdata=data.frame(x=x), type="response")

#
g1 <- cbind(1,eta1.hat)
h <- cbind(p, (1-p)*g1)

loglik(lam=rep(0,dim(h)[2]-1), tr=tr, h=h)
# }