R: Practical Multilevel Modeling

# Practical Multilevel Modeling

## Documentation for package ‘MLMusingR’ version 0.4.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

cdata.ex | Clustered dataset for centering example
engage | Student engagement dataset (complete data).
engage.miss | Student engagement dataset (with missing data).
glance.CR2 | Glance at goodness-of-fit statistics
group_center | Group-mean center a variable
group_mean | Computes the group mean of a variable
hdp | Hospital, doctor, patient (hdp) dataset
Htest | Test for homoskedasticity at level one
lrtPV | Likelihood Ratio Test with Model Results Using Plausible Values
MatSqrtInverse | Compute the inverse square root of a matrix
mixPV | Fit Weighted Multilevel Models Using Plausible Values
nmiss | Amount of missing data per variable
pisa2012 | USA data from PISA 2012
pool_pv | Pool plausible values using Rubin's rules
ri_test1 | Sample dataset 1 for testing the likelihood ratio test
ri_test2 | Sample dataset 2 for testing the likelihood ratio test (LRT)
robust_mixed | Cluster robust standard errors with degrees of freedom adjustments for lmerMod/lme objects
satdf | Compute Satterthwaite degrees of freedom
sch29 | Data from 29 schools (based on the NELS dataset) used for regression diagnostics
summary.mixPV | Create summary output from the mixPV function
summary_all | Use the summary function on a saved list of mixPV results
suspend | Suspension data from Virginia
thai | Thai data from PISA
thai.sm | Thai data from PISA (reduced)
tidy.CR2 | Tidy a CR2 object
tidy.mixPV | Helper function to allow use with modelsummary
wide | Wide dataset to be used for growth modeling
wscale | Scale of Sampling Weights