FAT-PET test using cluster robust wild bootstrap {crwbmetareg} | R Documentation |
FAT-PET test using cluster robust wild bootstrap
Description
FAT-PET test using cluster robust wild bootstrap.
Usage
fatpet(target, se, cluster, weights, boot.reps = 1000, prog.bar = FALSE, seed = NULL)
Arguments
target |
A vector with the effect sizes. |
se |
A vector with the standard errors, or the variances, of the effect sizes. |
cluster |
A vector indicating the clusters. |
weights |
A vector with the inverse of the the variances of the effect sizes. |
boot.reps |
The number of bootstrap re-samples to generate. |
prog.bar |
If you want the progress bar to appear set this equal to TRUE. |
seed |
IF you want the results to be rerpoducible set this equal to TRUE. |
Details
It implements the FAT-PET test using cluster robust wild bootstrap to compute the p-values. See references for this.
The function uses a modification of the function "cluster.wild.glm()" of the package "clusterSEs".
Value
A vector with two p-values. One for the constant and one for the cofficient of the "vse".
Author(s)
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
Oczkowski, E. and Doucouliagos, H. (2015). Wine prices and quality ratings: a meta-regression analysis. American Journal of Agricultural Economics, 97(1): 103–121.
Cameron, A. C., Gelbach, J. B. and Miller, D. L. (2008). Bootstrap-based improvements for inference with clustered errors. The Review of Economics and Statistics, 90(3): 414–427.
See Also
Examples
y <- rnorm(50)
se <- rexp(50, 3)
cluster <- sample(1:20, 50, replace = TRUE)
fatpet(y, se, cluster, weights = se^2, boot.reps = 500)