View source: R/TSDT_scoring_functions.R
treatment_effect | R Documentation |
Compute treatment effect as mean( treatment response ) - mean( control response )
treatment_effect(data, scoring_function_parameters = NULL)
data |
data.frame containing response data |
scoring_function_parameters |
named list of scoring function control parameters |
This function will compute the treatment for the response. The treatment effect is computed as the difference in means between the non-control treatment arm and the control treatment arm. The user must provide the treatment variable as well as the control value.
The difference in mean response across treatment arms.
TSDT, mean_response
N <- 100 df <- data.frame( continuous_response = numeric(N), trt = integer(N) ) df$continuous_response <- runif( min = 0, max = 20, n = N ) df$trt <- sample( c(0,1), size = N, prob = c(0.4,0.6), replace = TRUE ) # Compute the treatment effect treatment_effect( df, list( y_var = 'continuous_response', trt_control = 0 ) ) # Function return value should match this value mean( df$continuous_response[df$trt == 1] ) - mean( df$continuous_response[df$trt == 0] )
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