Date of Award:
12-2022
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Mathematics and Statistics
Committee Chair(s)
John R. Stevens
Committee
John R. Stevens
Committee
Yan Sun
Committee
Brennan Bean
Abstract
When designing an experiment, researchers often want to know how likely they are to detect statistically significant effects in the resulting data, i.e., they want to estimate their statistical power. The probability distribution method is a flexible way to do this, and it is currently implemented in the statistical software package SAS. This method requires a hypothetical data set (showing the magnitude of hypothesized effects) and constant values of variance components, which are critical elements of the statistical models used. The statistical software package R is increasingly popular, but the probability distribution method has not yet been implemented in R, and the statistical modeling approaches in R do not automatically allow constant values of the variance components. We present here an R implementation for power approximation that will allow variance components to be essentially held constant by assuming they follow a certain steep statistical distribution. We demonstrate this approach using normally-distributed data and explore issues in implementing it for non-normal data.
Checksum
1bfa8d6b7c559f3a1de263d705c2b16f
Recommended Citation
Geisler, Sydney, "Power Approximations for Generalized Linear Mixed Models in R Using Steep Priors on Variance Components" (2022). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 8617.
https://digitalcommons.usu.edu/etd/8617
Included in
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .