Calculate Variance-Covariance Matrix for Outcome Model
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
vcov_outcome(object, Y, Z, delta, tol = 10^(-5), lambda = 0.01)
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. |
A matrix of the estimated covariances between the parameter estimates in the weighted outcome regression, adjusted for uncertainty in the weights.
Christian Fong, Chad Hazlett, and Kosuke Imai.
Lunceford and Davididian 2004.
### ### 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)
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