pmsesampling-package: pmsesampling: Sample Size Determination for Accurate...

In pmsesampling: Sample Size Determination for Accurate Predictive Linear Regression

pmsesampling: Sample Size Determination for Accurate Predictive Linear Regression

Description

Tools to estimate the minimum sample size required to achieve a target Prediction Mean-Squared Error (PMSE) or a specified proportional PMSE reduction (pPMSE_r). Functions implement the analytic and simulation-based criteria described in Ma (2023) and include helpers for covariance-matrix handling, root-finding and diagnostic plotting.

Core functions

pmse_samplesize()

Determines sample size from PMSE equation in basic and full models and the efficient sample size

Typical workflow

1. Obtain \sigma_k^2 and \sigma_p^2

2. Or import or build a predictor covariance matrix.
   

3. Or obtain Cohen's f^2 and \R^2

4. Call pmse_samplesize with available inputs to get sample size.
   

Author(s)

Maintainer: Louis Chen [e-mail]

Authors:

• Zheyang Wu [e-mail] [thesis advisor]

References

Ma Y. (2023) Predictive Power and Efficient Sample Size in Linear Regression Models. Worchester Polytechnic Institute

See Also

Useful links:

• https://github.com/Chenaters/pmsesampling

• Report bugs at https://github.com/Chenaters/pmsesampling/issues

pmsesampling documentation built on Sept. 9, 2025, 5:47 p.m.