pecanr: Partial Eta-Squared for Crossed, Nested, and Mixed Linear Mixed
Models
Computes partial eta-squared effect sizes for fixed effects in
linear mixed models fitted with the 'lme4' package. Supports crossed,
nested, and mixed (crossed-and-nested) random effects structures with any
number of grouping factors. Mixed designs handle cases where grouping
factors are simultaneously crossed with some variables and nested within
others (e.g., photos nested within models, but both crossed with
participants). Factor predictors are supported directly, and a single
factor-level (omnibus) effect size can be obtained for a multi-level factor
or multi-df interaction. Random slope variances are translated to the
outcome scale using a variance decomposition approach, correctly accounting
for predictor scaling and interaction terms. Both general and operative
effect sizes are provided, with optional parametric bootstrap confidence
intervals. For correlated predictors, per-predictor effect sizes use unique
(semipartial) variance by default. Methods are based on Correll, Mellinger, McClelland, and Judd
(2020) <doi:10.1016/j.tics.2019.12.009>, Correll, Mellinger, and Pedersen
(2022) <doi:10.3758/s13428-021-01687-2>, and Rights and Sterba (2019)
<doi:10.1037/met0000184>.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=pecanr
to link to this page.
[aut, cre],
Joshua Correll