Stack estimating equations (two-stage estimator)

# S3 method for estimate
stack(
  x,
  model2,
  D1u,
  inv.D2u,
  propensity,
  dpropensity,
  U,
  keep1 = FALSE,
  propensity.arg,
  estimate.arg,
  na.action = na.pass,
  ...
)

Arguments

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

Examples

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))
#> Estimate Std.Err 2.5% 97.5% P-value #> (Intercept) 0.9515 0.0987 0.7581 1.145 5.4e-22