SKS.pool.t: SKS.pool.t
In hettx: Fisherian and Neymanian Methods for Detecting and Measuring Treatment Effect Variation
View source: R/helper_stat_calc.R
SKS.pool.t R Documentation
SKS.pool.t
Description
Subtract off group level treatment effect estimates and then look
at KS statistic on residuals.
Usage
SKS.pool.t(Y, Z, W)
Arguments
Y
Observed outcome vector
Z
Treatment assigment vector
W
A a factor or categorical covariate.
Details
Distinct from the interacted lm in that the control units are not
shifted and centered with respect to eachother.
Examples
df <- make.randomized.dat( 1000, gamma.vec=c(1,1,1,2), beta.vec=c(-1,-1,1,0) )
df$W <- sample(c("A", "B", "C"), nrow(df), replace = TRUE)
SKS.pool.t(df$Yobs, df$Z, df$W)
hettx documentation built on Aug. 20, 2023, 1:06 a.m.
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View source: R/helper_stat_calc.R
| SKS.pool.t | R Documentation |
SKS.pool.t
Description
Subtract off group level treatment effect estimates and then look at KS statistic on residuals.
Usage
SKS.pool.t(Y, Z, W)
Arguments
Y |
Observed outcome vector |
Z |
Treatment assigment vector |
W |
A a factor or categorical covariate. |
Details
Distinct from the interacted lm in that the control units are not shifted and centered with respect to eachother.
Examples
df <- make.randomized.dat( 1000, gamma.vec=c(1,1,1,2), beta.vec=c(-1,-1,1,0) )
df$W <- sample(c("A", "B", "C"), nrow(df), replace = TRUE)
SKS.pool.t(df$Yobs, df$Z, df$W)
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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