stack.estimate: Stack estimating equations

In lava: Latent Variable Models

Stack estimating equations

Description

Stack estimating equations (two-stage estimator)

Usage

## S3 method for class '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,seed=1)
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))

lava documentation built on May 29, 2024, 3:10 a.m.