Bias, Precision, and Power for Multi-Level Random Regressions
Fits a multilevel random regression to n simulated data frames.
Estimates bias for n number of multi-level random regression models pe...
Calculates imprecision for n multi-level random regressions perfromed ...
lmerAll is not a user level function
lower2 is not a user level function
mean2 is not a user level function
median2 is not a user level function
Simulation Package for Multi-level random regressions
Density plots for each variance component.
Estimates power to detect significant among-individual variation in in...
sd2 is not a user level function
Simulate data setes to be analyzed by a multi-level random regression.
Summary of the results of the multi-level random regressions performed...
Upper2 is not a user level function
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.