bcalibrate | R Documentation |
Calibrates the naive estimates to account for unobserved confounding when outcome variables are binary. The calibration can be done with user-specific sensitivity parameter or with our pre-provided calibration methods, the worst-case calibration for a single contrast or multivariate calibration for multiple contrasts.
bcalibrate( y, tr, t, gamma, R2 = NULL, mu_y_t = NULL, mu_u_tr = NULL, mu_u_t = NULL, cov_u_t = NULL, nU = NULL, nsim = 4000, ... )
y |
|
tr |
|
t |
|
gamma |
a vector specifying the direction of sensitivity parameters. |
R2 |
an optional scalar or vector specifying the proportion of residual variance in outcome given the treatment that can be explained by confounders, which determines the magnitude of sensitivity parameters. |
mu_y_t |
an optional scalar or vector that contains naive estimates of treatment effects ignoring confounding. |
mu_u_tr |
an optional matrix of conditional confounder means for all observed treatments with latent variables in columns. |
mu_u_t |
an optional matrix of conditional confounder means for treatments of interest with latent variables in columns. |
cov_u_t |
an optional covariance matrix of confounders conditional on treatments. |
nU |
Number of latent confounders to consider. |
nsim |
an optional scalar specifying the number of sample draws. |
... |
further arguments passed to |
A data.frame
with naive and calibrated estimates of population average outcome receiving
treatment t
.
# load the example data # y <- GaussianT_BinaryY$y tr <- subset(GaussianT_BinaryY, select = -c(y)) t1 <- tr[1:5,] t2 <- rep(0, times = ncol(tr)) # calibration # est_b <- bcalibrate(y = y, tr = tr, t = rbind(t1, t2), nU = 3, gamma = c(1.27, -0.28, 0), R2 = c(0.2, 0.7)) est_b_rr <- list(est_df = est_b$est_df[1:5,] / as.numeric(est_b$est_df[6,]), R2 = c(0.2, 0.7)) plot_estimates(est_b_rr)
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