| pred_int {Replicate} | R Documentation |
Compute prediction interval for replication study given original
Description
Given point estimates and their variances for one or multiple original studies and one or more replication studies, returns a vector stating whether each replication estimate is in its corresponding prediction interval. Assumes no heterogeneity.
Usage
pred_int(yio, vio, yir = NULL, vir, level = 0.95)
Arguments
yio |
Effect estimate in the original study. Can be a vector for multiple original studies. |
vio |
Estimated variance of effect estimate in the original study (i.e., its squared standard error). Can be a vector for multiple original studies. |
yir |
Effect estimate in the replication study. Can be a vector for multiple replication studies. Can be omitted, in which case function returns only the prediction interval. |
vir |
Estimated variance of effect estimate in the replication study (i.e., its squared standard error). Can be a vector for multiple replication studies. |
level |
Coverage level of prediction interval. Typically 0.95. |
Examples
# calculate prediction interval for a single replication study
pred_int( yio = 1, vio = .5, yir = 0.6,
vir = .2 )
# calculate prediction intervals for a one-to-one design
pred_int( yio = c(1, 1.3), vio = c(.01, .6),
yir = c(.6, .7), vir = c(.01,.3) )
# no need to pass yir if you only want the intervals
pred_int( yio = c(1, 1.3), vio = c(.01, .6),
vir = c(.01,.3) )
# calculate prediction intervals for a many-to-one design
pred_int( yio = c(1), vio = c(.01), yir = c(.6, .7), vir = c(.01,.3) )