# MultiRR: Bias, Precision, and Power for Multi-Level Random Regressions

Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.

- Author
- Yimen G. Araya-Ajoy
- Date of publication
- 2015-10-21 11:36:43
- Maintainer
- Yimen G. Araya-Ajoy <yimencr@gmail.com>
- License
- GPL-2
- Version
- 1.1

## Man pages

- Anal.MultiRR
- Fits a multilevel random regression to n simulated data...
- Bias
- Estimates bias for n number of multi-level random regression...
- Imprecision
- Calculates imprecision for n multi-level random regressions...
- lmerAll
- lmerAll is not a user level function
- lower2
- lower2 is not a user level function
- mean2
- mean2 is not a user level function
- median2
- median2 is not a user level function
- MultiRR-package
- Simulation Package for Multi-level random regressions
- Plot.Sim
- Density plots for each variance component.
- Power
- Estimates power to detect significant among-individual...
- sd2
- sd2 is not a user level function
- Sim.MultiRR
- Simulate data setes to be analyzed by a multi-level random...
- Summary
- Summary of the results of the multi-level random regressions...
- upper2
- Upper2 is not a user level function