Description Usage Arguments Details Value Examples
PER_CI
returns the two-sided level-alpha confidence
interval of the pooled effect ratio in a cluster-randomized
encouragement experiment.
1 2 3 4 5 6 7 8 9 10 11 |
R_t |
A length-K vector where K is equal to the number of clusters and the kth entry equal to the sum of unit-level outcomes in the encouraged cluster of the kth matched pair of two clusters. |
R_c |
A length-K vector where K is equal to the number of clusters and the kth entry equal to the sum of unit-level outcomes in the control cluster of the kth matched pair of two clusters. |
d_t |
A length-K vector where K is equal to the number of clusters and the kth entry equal to the sum of unit-level treatment received in the encouraged cluster of the kth matched pair of two clusters. |
d_c |
A length-K vector where K is equal to the number of clusters and the kth entry equal to the sum of unit-level treatment received in the control cluster of the kth matched pair of two clusters. |
lower, upper |
The lower and upper endpoints of the interval to be searched. |
Q |
A K times p design matrix containing the covariate information.
See Details of the function |
meshsize |
The meshsize of the grid search. |
alpha |
The level of the confidence interval. |
PER_CI
constructs a two-sided level-alpha confidence interval
by interting the corresponding hypothesis test for the pooled effect
ratio. See PER
for details on the hypothesis tesing.
PER_CI
conducts a grid search with user-specified endpoints
and meshsize in order to construct the confidence interval.
A length-2 vector of two endpoints of the confidence interval.
1 2 3 4 5 6 7 8 9 10 | R_t = encouraged_clusters$aggregated_outcome
R_c = control_clusters$aggregated_outcome
d_t = encouraged_clusters$aggregated_treatment
d_c = control_clusters$aggregated_treatment
# Construct 95% CI for the pooled effect ratio estimand
# using the default sample variance estimator, i.e.,
# setting Q = NULL.
CI = PER_CI(R_t, R_c, d_t, d_c, lower = -0.1, upper = 0.1,
alpha = 0.05)
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