# stack.estimate: Stack estimating equations In kkholst/lava: Latent Variable Models

 stack.estimate R Documentation

## 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/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))
```

kkholst/lava documentation built on Aug. 4, 2022, 11:15 p.m.