sandImpACE: Calculates standard error of Average causal effect
In ui: Uncertainty Intervals and Sensitivity Analysis for Missing Data
Description
This is a support function for ui.causal and calculates standard error of Average causal effect for the regression imputation estimator.
Usage
1
sandImpACE(X, y, z, BetaOLSy0, BetaOLSy1, NaivEst, N, p)
Arguments
X
Covariate matrix.
y
Outcome vector.
z
missingness indicator.
BetaOLSy0
Coefficients from non-treated regression.
BetaOLSy1
Coefficients from treated regression.
NaivEst
Naiv estimates.
N
Total number.
p
Number of covariates outcome regression.
ui documentation built on Nov. 11, 2019, 5:07 p.m.
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Description
This is a support function for ui.causal and calculates standard error of Average causal effect for the regression imputation estimator.
Usage
1 | sandImpACE(X, y, z, BetaOLSy0, BetaOLSy1, NaivEst, N, p)
|
Arguments
X |
Covariate matrix. |
y |
Outcome vector. |
z |
missingness indicator. |
BetaOLSy0 |
Coefficients from non-treated regression. |
BetaOLSy1 |
Coefficients from treated regression. |
NaivEst |
Naiv estimates. |
N |
Total number. |
p |
Number of covariates outcome regression. |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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