covarTest_mean {rddtools} | R Documentation |
Testing for balanced covariates: equality of means with t-test
Description
Tests equality of means by a t-test for each covariate, between the two full groups or around the discontinuity threshold
Usage
covarTest_mean(
object,
bw = NULL,
paired = FALSE,
var.equal = FALSE,
p.adjust = c("none", "holm", "BH", "BY", "hochberg", "hommel", "bonferroni")
)
## S3 method for class 'rdd_data'
covarTest_mean(
object,
bw = NULL,
paired = FALSE,
var.equal = FALSE,
p.adjust = c("none", "holm", "BH", "BY", "hochberg", "hommel", "bonferroni")
)
## S3 method for class 'rdd_reg'
covarTest_mean(
object,
bw = NULL,
paired = FALSE,
var.equal = FALSE,
p.adjust = c("none", "holm", "BH", "BY", "hochberg", "hommel", "bonferroni")
)
Arguments
object |
object of class rdd_data |
bw |
a bandwidth |
paired |
Argument of the |
var.equal |
Argument of the |
p.adjust |
Whether to adjust the p-values for multiple testing. Uses the |
Value
A data frame with, for each covariate, the mean on each size, the difference, t-stat and ts p-value.
Author(s)
Matthieu Stigler <Matthieu.Stigler@gmail.com>
See Also
covarTest_dis
for the Kolmogorov-Smirnov test of equality of distribution
Examples
data(house)
## Add randomly generated covariates
set.seed(123)
n_Lee <- nrow(house)
Z <- data.frame(z1 = rnorm(n_Lee, sd=2),
z2 = rnorm(n_Lee, mean = ifelse(house<0, 5, 8)),
z3 = sample(letters, size = n_Lee, replace = TRUE))
house_rdd_Z <- rdd_data(y = house$y, x = house$x, covar = Z, cutpoint = 0)
## test for equality of means around cutoff:
covarTest_mean(house_rdd_Z, bw=0.3)
## Can also use function covarTest_dis() for Kolmogorov-Smirnov test:
covarTest_dis(house_rdd_Z, bw=0.3)
## covarTest_mean works also on regression outputs (bw will be taken from the model)
reg_nonpara <- rdd_reg_np(rdd_object=house_rdd_Z)
covarTest_mean(reg_nonpara)
[Package rddtools version 1.6.0 Index]