CBPS (version 0.23)

vcov_outcome: Calculate Variance-Covariance Matrix for Outcome Model

Description

vcov_outcome Returns the variance-covariance matrix of the main parameters of a fitted CBPS object.

This adjusts the standard errors of the weighted regression of Y on Z for uncertainty in the weights.

### @aliases vcov_outcome vcov_outcome.CBPSContinuous

Usage

vcov_outcome(object, Y, Z, delta, tol = 10^(-5), lambda = 0.01)

Arguments

object

A fitted CBPS object.

Y

The outcome.

Z

The covariates (including the treatment and an intercept term) that predict the outcome.

delta

The coefficients from regressing Y on Z, weighting by the cbpsfit$weights.

tol

Tolerance for choosing whether to improve conditioning of the "M" matrix prior to conversion. Equal to 1/(condition number), i.e. the smallest eigenvalue divided by the largest.

lambda

The amount to be added to the diagonal of M if the condition of the matrix is worse than tol.

Value

A matrix of the estimated covariances between the parameter estimates in the weighted outcome regression, adjusted for uncertainty in the weights.

References

Lunceford and Davididian 2004.

Examples

# NOT RUN {
###
### Example: Variance-Covariance Matrix
###

##Load the LaLonde data
data(LaLonde)
## Estimate CBPS via logistic regression
fit <- CBPS(treat ~ age + educ + re75 + re74 + I(re75==0) + I(re74==0), 
		    data = LaLonde, ATT = TRUE)
## Get the variance-covariance matrix.
vcov(fit)

# }