README.md

In premodelling/mixed: Modelling Multilevel & Longitudinal Problems

MATH553: Modelling Multilevel & Longitudinal Problems

Notes

• The Statistical Sleuth, Section 20.4
• Methodologies, Lecture 5, Assumptions & Diagnostics

# ACF
# initial <- lme(.)
# plot(nlme::ACF(object = initial, resType = 'normalized'), bty = 'n', frame.plot = FALSE)
# acf(initial$residuals, type = 'correlation', frame.plot = FALSE)

References

• Mixed Models with R

• Random Effects

• Pattern Recognition and Machine Learning, Christopher Bishop | Published by Springer | January 2006 | url

• The Elements of Statistical Learning Second Edition, Hastie, Tibshirani, & Friedman | Published by Springer-Verlag | 2009 | url

• An Introduction to Statistical Learning Second Edition, James, Witten, Hastie, & Tibshirani | Published by Springer | 2021 | url

• Linear Mixed Models

Development Environment

• Edit the help file skeletons in 'man', possibly combining help files for multiple functions.
• Edit the exports in 'NAMESPACE', and add necessary imports.
• Put any C/C++/Fortran code in 'src'.
• If you have compiled code, add a useDynLib() directive to 'NAMESPACE'.
• Run R CMD build to build the package tarball.
• Run R CMD check to check the package tarball.

Read "Writing R Extensions" for more information.

premodelling/mixed documentation built on April 25, 2022, 6:27 a.m.