# vcov_outcome: Calculate Variance-Covariance Matrix for Outcome Model In CBPS: Covariate Balancing Propensity Score

## 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

 `1` ```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.

## Author(s)

Christian Fong, Chad Hazlett, and Kosuke Imai.

## References

Lunceford and Davididian 2004.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```### ### 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) ```

CBPS documentation built on Jan. 19, 2022, 1:07 a.m.