Stack estimating equations
Stack estimating equations (two-stage estimator)
## S3 method for class 'estimate' stack( x, model2, D1u, inv.D2u, propensity, dpropensity, U, keep1 = FALSE, propensity.arg, estimate.arg, na.action = na.pass, ... )
x |
Model 1 |
model2 |
Model 2 |
D1u |
Derivative of score of model 2 w.r.t. parameter vector of model 1 |
inv.D2u |
Inverse of deri |
propensity |
propensity score (vector or function) |
dpropensity |
derivative of propensity score wrt parameters of model 1 |
U |
Optional score function (model 2) as function of all parameters |
keep1 |
If FALSE only parameters of model 2 is returned |
propensity.arg |
Arguments to propensity function |
estimate.arg |
Arguments to 'estimate' |
na.action |
Method for dealing with missing data in propensity score |
... |
Additional arguments to lower level functions |
m <- lvm(z0~x) Missing(m, z ~ z0) <- r~x distribution(m,~x) <- binomial.lvm() p <- c(r=-1,'r~x'=0.5,'z0~x'=2) beta <- p[3]/2 d <- sim(m,500,p=p) m1 <- estimate(r~x,data=d,family=binomial) d$w <- d$r/predict(m1,type="response") m2 <- estimate(z~1, weights=w, data=d) (e <- stack(m1,m2,propensity=TRUE))
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