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umx

Road map, and Tutorials (let me know what you’d like, or perhaps a book?)

umx is a package designed to make structural equation modeling easier, from building, to modifying and reporting.

citation("umx")

You should cite: Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R. Twin Research and Human Genetics, 22, 27-41. DOI:10.1017/thg.2019.2

umx includes high-level functions for complex models such as multi-group twin models, as well as graphical model output.

Install it from CRAN:

install.packages("umx")
library(umx)
?umx

Most functions have extensive and practical examples (even figures for the twin models): so USE THE HELP :-).

See what is on offer with ‘?umx’. There are online tutorials at tbates.github.io.

umx stands for “user” OpenMx functions. It provides over 100 functions, but most importantly:

1. umxRAM that makes path-based SEM in R straightforward, with umxSummary and plot for table and graphical display of your models. It can also interpret basic lavaan if you get a script in that language.
2. A suite of twin modelling functions, such as umxACE.

These are supported by many low-level functions automating activities such as parameter labels, start values etc., as well as helping with data-wrangling, journal-ready presentation (try umxAPA() among other tasks.

Some highlights include:

1. Building Path Models
   • umxRAM() # Take umxPaths + data data = run and return a model, along with a plot and umxSummary
   • umxPath() # write paths with human-readable language like var = , mean = cov =, fixedAt=. Quickly define a variance and mean (‘v.m. =’) and more.
2. Reporting output
   • umxSummary(model) # Nice summary table, in markdown or browser. Designed for journal reporting (Χ², p, CFI, TLI, & RMSEA). Optionally show path loadings
   • plot(model, std=TRUE, digits = 3, ...) # Graphical model in your browser! or edit in programs like OmniGraffle
   • parameters(m1, "below", .1, pattern="_to_")) # A powerful assistant to get labels and values from a model (e.g. all ‘to’ params, below .1 in value)
   • residuals(m1, supp=.1) # Show residual covariances filtered for magnitude
3. Modify models
   • umxModify(model, update = ) # Modify and re-run a model. You can add objects, drop or add paths, including by wildcard label matching), re-name the model, and even return the comparison. All in 1 line
4. Twin modeling!
   • umxACE # Twin ACE modeling with aplomb paths are labeled! Works with plot() and umxSummary!
   • umxCP, umxIP, umxGxE, umxCP, umxGxEbiv, umxSexLim
   • 
5. Easy-to-remember options
   • umx_set_cores()
   • umx_set_optimizer()
6. Many more miscellaneous helpers e.g.
   • umx_time(model1, model2) reports and compares run times in a compact programmable format (also “start” and “stop” a timer)
   • umxHetcor(data, use = "pairwise.complete.obs") # Compute appropriate pair-wise correlations for mixed data types.
   • Dozens more: Check out the “family links” in ?umx and in any help file!

Code and requests welcome via Github. Tell your friends! Publish good science :-)

For thrill-seekers and collaborators only: the bleeding-edge development version is here:

install.packages("devtools")
library("devtools")
install_github("tbates/umx")
library("umx")
?umx

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.