contour_ate: Visualize ATE Estimates by Contour Plot

In JiajingZ/TukeySens: Sensitivity Analysis Based on Tukey's Factorization

Description

Visualize the same level of Average Treatment Effect (ATE) for different choices of sensitivity parameters with contour lines.

Usage

contour_ate(x_trt, y_trt, x_ctrl, y_ctrl, 
            largest_effect, gamma_length = 11, ...)

Arguments

x_trt	a tibble or data frame with observed pre-treatment variables for the treatment group
y_trt	a vector with outcomes for the treatment group
x_ctrl	a tibble or data frame with observed pre-treatment variables for the control group
y_ctrl	a vector with outcomes for the control group
largest_effect	the largest magnitude of sensitivity parameter to be considered, chosen from caliplot
gamma_length	chosen length of sensitivity parameter sequence, which needs to be an odd integer
joint	logical. If TURE, the mean surface and residual variance will be estimated jointly for both treatment groups; if FALSE (default), the mean surface and residual variance will be estimated independently for each treatment group.
...	arguments passed to the function BART::wbart

Details

For analysis details, please see heatmap_ate

Examples

# Observed data in treatment group 
NHANES_trt <- NHANES %>% dplyr::filter(trt_dbp == 1)
x_trt <- NHANES_trt %>% select(-one_of("trt_dbp", "ave_dbp"))
y_trt <- NHANES_trt %>% select(ave_dbp)

# Observed data in control group
NHANES_ctrl <- NHANES %>% dplyr::filter(trt_dbp == 0)
x_ctrl <- NHANES_ctrl %>% select(-one_of("trt_dbp", "ave_dbp"))
y_ctrl <- NHANES_ctrl %>% select(ave_dbp)

# ATE Contour Plot 
contour_ate(x_trt, y_trt, x_ctrl, y_ctrl, largest_effect = 0.05)
contour_ate(x_trt, y_trt, x_ctrl, y_ctrl, largest_effect = 0.05, joint = TRUE)

JiajingZ/TukeySens documentation built on Jan. 23, 2020, 3:44 a.m.