tau_min {RCT} | R Documentation |
tau_min() computes the minimum detectable difference between control group and each treatment
Description
tau_min() computes the minimum detectable difference between control group and each treatment
Usage
tau_min(
outcome_var,
N,
power = 0.8,
significance = 0.05,
share_control,
n_groups = 2
)
Arguments
outcome_var |
the variable for which you wish to test the impact of treatment |
N |
number of observations in the RCT, usually nrow(data) |
power |
The level of power of the test (1 - Pr(Reject H_0 | H_0 True) ). Default is 0.8 |
significance |
The level of significance of the test Pr(Reject H_0 | H_0 False). Default is 0.05 |
share_control |
The share of observations in N assigned to control. This argument allows for sequences (i.e. seq(0,1,0.1)) |
n_groups |
Number of groups (control + # treatment groups) |
Details
This function calculates the minimum difference that could show significant E[Y(1)-Y(0)] = tau, between any two given groups (e.g. control vs each treatment), given the population size (N), the outcome variable, power and significance
Value
A tibble with the share_control and N observations in control group (N_control), the share and N of each treatment c(share_ti, N_ti), total share of treatment rows and N treated (share_treat, N_treat), N, the minimum detectable difference between control and all treatments together (tau_min_global), the minimum detectable difference between control and each treatment (tau_min_each_treat)
Examples
data <- data.frame(y_1 = rbinom(n = 100, size = 1, prob = 0.3),
y_2 = rnorm(n = 100, mean = 8, sd = 2))
tau_min(data$y_1, N = nrow(data), share_control = seq(0,1,0.1), n_groups = 3)