R packages

simstudy

https://cran.r-project.org/web/packages/simstudy/vignettes/simstudy.html

psych::sim.multilevel

http://personality-project.org/r/html/sim.multilevel.html

Re-creating data sets

https://stats.stackexchange.com/questions/30303/how-to-simulate-data-that-satisfy-specific-constraints-such-as-having-specific-m

A complex but thorough simulation example from idre.ucla

http://stats.idre.ucla.edu/r/codefragments/mesimulation/

Mark-recapture designs for wildlife studies

Multilevel Simulation with specified ICC

https://psychometroscar.wordpress.com/simulate-a-2-level-multilevelhlmlinear-mixed-model/

Other

http://clayford.github.io/dwir/dwr_12_generating_data.html

https://web.stanford.edu/class/bios221/labs/simulation/Lab_3_simulation.html

http://stackoverflow.com/questions/23256694/how-can-i-efficiently-generate-a-dataframe-of-simulated-values

http://www.quantumforest.com/2011/10/simulating-data-following-a-given-covariance-structure/

Bolkers GLMM FAQ: Model summaries (goodness-of-fit, decomposition of variance, etc.) http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#how-do-i-compute-a-coefficient-of-determination-r2-or-an-analogue-for-glmms

### Cross Validated

A problem that interests me: small marging $R^2$ values for otherwise interesting models:  Is it worth reporting small fixed-effect R2 (marginal R2), large model R2 (conditional R2)?

Partitioning explained variance to fixed effects by comparing r squared (R2) between linear mixed models

Is R2 useful or dangerous? (general issues with R2)

Misc. references on R2 in GLMMs at Proportion of explained variance in a mixed-effects model

Good explanation of Nakagawa and Schielzeth (2013) at: R2 for mixed models with multiple fixed and random effects

An open question: R2 for negative binomial GLMM: R2 from a generalized linear mixed-effects models (GLMM) using a negative binomial distribution

In response to the question Calculating R2 in mixed models using Nakagawa & Schielzeth’s (2013) R2glmm method, some re-posts a response from Douglas Bates where he voices his extreme skepticism about R2 for mixed models.

A related topics: Does the variance of a sum equal the sum of the variances?

## R Packages for R2

muMIn::r.squaredGLMM

piecewiseSEM::rsquared

sjstats::r2

sjstats:cod “coefficient of discrimination” for logistic regression.  See Tjur T (2009) Coefficients of determination in logistic regression models – a new proposal: The coefficient of discrimination. The American Statistician, 63(4): 366-372

sjstats:rsme “root mean square error”

Documentation for sjstats discusses how ICC can be used to investigate amount of variance due to clustering

## References

Jaeger et al 2017.  An R2 statistic for fixed effects in the generalized linear mixed model.  http://www.tandfonline.com/doi/abs/10.1080/02664763.2016.1193725?journalCode=cjas20

LaHuis et al.  2014.  Explained Variance Measures for Multilevel Models.  http://journals.sagepub.com/doi/abs/10.1177/1094428114541701

Tjur T (2009) Coefficients of determination in logistic regression models – a new proposal: The coefficient of discrimination. The American Statistician, 63(4): 366-372

http://www.tandfonline.com/doi/abs/10.1198/tast.2009.08210

Assessing the Fit of Regression Models

http://www.theanalysisfactor.com/assessing-the-fit-of-regression-models/

merTools

https://cran.r-project.org/web/packages/merTools/vignettes/merToolsIntro.html

predictmeans: “This package provides functions to diagnose and make inferences from various linear models, such as … ‘lme’, and ‘lmer’. Inferences include predicted means and standard errors, contrasts, multiple comparisons, permutation tests and graphs.”

https://cran.r-project.org/web/packages/predictmeans/index.html

iccbeta: “This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.”

https://cran.r-project.org/web/packages/iccbeta/iccbeta.pdf

influence.ME

https://cran.r-project.org/web/packages/influence.ME/index.html

lmerTest

https://cran.r-project.org/web/packages/lmerTest/index.html

longpower: sample size calculation for longitudinal data

https://cran.r-project.org/web/packages/longpower/index.html

mlmRev: example of lmer

https://cran.r-project.org/web/packages/mlmRev/index.html

### 0) enthought Python distribution

https://www.enthought.com/products/epd/

This seems like a good approach but I couldn’t get it to install

### How to Set Up a Python Development Environment on Windows

https://www.davidbaumgold.com/tutorials/set-up-python-windows/

### 2) A good IDE: Sublime text:

https://www.sublimetext.com/

For installing curl after an initial cygwin installation

### How do I install cURL on cygwin?

https://stackoverflow.com/questions/3647569/how-do-i-install-curl-on-cygwin

Scroll down for how to do this from the windows command line and within cygwin; note that the name of the cygwin installer might vary from what is listed in teh answer.

4)Add python to you path:  Go to Control Panel, System, Advanced system settings, click on Environment Variables, click on Path, ad something like “;C:Python27”.  See Haddock and Dunn Practical computing for biologists  pg 456 for more details.

4)Install ez_setup.py; uses wget

https://serverfault.com/questions/7282/how-to-run-easy-install-in-cygwin

On getting scipy

### Installing SciPy, NumPy and matplotlib Under Cygwin

https://www.codefull.org/2015/12/installing-scipy-numpy-and-matplotlib-under-cygwin/

extracting tarballs

https://www.interserver.net/tips/kb/extract-tar-gz-files-using-linux-command-line/

Information on pip, which is installed for me automatically

https://stackoverflow.com/questions/30863501/installing-new-versions-of-python-on-cygwin-does-not-install-pip

On installing setup tools

https://packaging.python.org/tutorials/installing-packages/

On wheel files .whl

https://pip.pypa.io/en/latest/user_guide/#installing-from-wheels

This looks useful:

# Getting to Know the Command Line

https://www.davidbaumgold.com/tutorials/command-line/

This looks useful

https://datanitro.com/blog/python_on_windows

# GitHub For Beginners: Don’t Get Scared, Get Started

General introduction, starting with downloading git and getting it working via the command line tool that comes with git.

# GitHub For Beginners: Commit, Push And Go

Follow up to “GitHub for Beginners: Don’t get scared, Get Started”

# Git for Scientists: A Tutorial

http://nyuccl.org/pages/GitTutorial/

# rstudio.com: Version Control with Git and SVN

https://support.rstudio.com/hc/en-us/articles/200532077-Version-Control-with-Git-and-SVN

The official RStudio outline of the technical details.  Not much on getting git to work, mostly on how to use it via RStudio.

# An introduction to Git and how to use it with RStudio

http://r-bio.github.io/intro-git-rstudio/

General introduction to version control

# The Basic Workflow of Version Control

https://www.git-tower.com/blog/workflow-of-version-control

Comprehensive infographic on version control workflows with git