Bias-Corrected Confidence Intervals
bcci(thetahatstar, thetahat, theta = NULL, alpha = c(0.001, 0.01, 0.05))
| thetahatstar | Numeric vector. Sampling distribution of thetahat. |
|---|---|
| thetahat | Numeric. Parameter estimate. |
| theta | Numeric. Parameter. Optional argument. |
| alpha | Numeric vector.
Alpha level.
By default |
Other confidence intervals functions:
bcaci(),
evalci(),
len(),
pcci(),
shape(),
theta_hit(),
zero_hit()
Ivan Jacob Agaloos Pesigan
B <- 5000 data <- jeksterslabRdatarepo::thirst thetahat <- fit.ols(data, minimal = TRUE) n <- nrow(data) muthetahat <- colMeans(data) Sigmathetahat <- cov(data) thetahatstar <- pb.mvn( muthetahat = muthetahat, Sigmathetahat = Sigmathetahat, n = n, B = 5000, par = FALSE ) bcci( thetahatstar = thetahatstar, thetahat = thetahat, theta = 0.15 # assuming that the true indirect effect is 0.15 )#> est se reps ci_0.05 ci_0.5 #> 1.527185e-01 7.684378e-02 5.000000e+03 -3.160872e-02 5.218388e-03 #> ci_2.5 ci_97.5 ci_99.5 ci_99.95 zero_hit_99.9 #> 3.728281e-02 3.552757e-01 4.221709e-01 4.652529e-01 1.000000e+00 #> zero_hit_99 zero_hit_95 len_99.9 len_99 len_95 #> 0.000000e+00 0.000000e+00 4.968616e-01 4.169525e-01 3.179929e-01 #> shape_99.9 shape_99 shape_95 theta_hit_99.9 theta_hit_99 #> 1.695541e+00 1.826794e+00 1.754719e+00 1.000000e+00 1.000000e+00 #> theta_hit_95 theta_miss_99.9 theta_miss_99 theta_miss_95 theta #> 1.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 1.500000e-01