Power analysis for longitudinal multilevel models: powerlmm 0.3.0 is now out on CRAN
[This article was first published on R Psychologist - R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
My R package powerlmm 0.3.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub https://github.com/rpsychologist/powerlmm.
New features
This version adds support for raw effect sizes, and new standardized effect sizes using the function cohend(...). Here’s an example that use the different types.
p <- study_parameters(n1 = 11,
n2 = 25,
icc_pre_subject = 0.5,
var_ratio = 0.03,
effect_size = c(10, # raw
cohend(0.5, standardizer = "pretest_SD"),
cohend(0.5, standardizer = "posttest_SD"),
cohend(0.5, standardizer = "slope_SD"))
)
Other changes
- Support for lmerTest 3.0.
To leave a comment for the author, please follow the link and comment on their blog: R Psychologist - R.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.