The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

pecanr

pecanr computes partial eta-squared (eta2p) effect sizes for fixed effects in linear mixed models fitted with lme4. It correctly handles crossed, nested, and mixed (crossed-and-nested) random effects structures – including random slopes – using a variance decomposition approach that translates slope variances to the outcome scale.

Why pecanr?

pecanr accounts for:

• Crossed designs – any number of grouping factors (subjects, items, raters, etc.)
• Nested designs – hierarchical structures with automatic level detection
• Mixed designs – grouping factors that are simultaneously nested within some variables and crossed with others (e.g., photos nested within models, but both crossed with participants)
• Random slopes – translated to the outcome scale via sigma^2_slope x sigma^2_X
• Operative effect sizes – excluding variance components that don’t contribute to the standard error of the tested effect

Installation

You can install the development version of pecanr from GitHub:

# install.packages("pak")
pak::pak("bcohen0901/pecanr")

Once on CRAN:

install.packages("pecanr")

Usage

Crossed design (subjects x items)

library(lme4)
library(pecanr)

model <- lmer(y ~ condition + (1 | subject) + (1 | item), data = my_data)
eta2p(model, effect = "condition", data = my_data,
      design     = "crossed",
      cross_vars = c("subject", "item"))

Three crossed factors

model3 <- lmer(y ~ condition + (1 | subject) + (1 | item) + (1 | rater),
               data = my_data)
eta2p(model3, effect = "condition", data = my_data,
      design     = "crossed",
      cross_vars = c("subject", "item", "rater"))

Nested design

model_nested <- lmer(y ~ treatment + (1 | school/class), data = my_data)
eta2p(model_nested, effect = "treatment", data = my_data,
      design    = "nested",
      nest_vars = c("class", "school"))

Mixed design (nested-and-crossed)

Use design = "mixed" when some grouping factors are nested within others but all levels are crossed with additional factors. A common example is participants viewing multiple photos of each model: photos are nested within models, but both levels are crossed with participants.

model_mixed <- lmer(y ~ x + (1 | participant) + (1 | model) + (1 | photo:model),
                    data = my_data)
eta2p(model_mixed, effect = "x", data = my_data,
      design     = "mixed",
      cross_vars = "participant",
      nest_vars  = c("photo", "model"))

Batch over all effects

batch_eta2p(model, data = my_data,
            design     = "crossed",
            cross_vars = c("subject", "item"))

Operative effect sizes

eta2p(model, effect = "condition", data = my_data,
      design     = "crossed",
      cross_vars = c("subject", "item"),
      operative  = TRUE)

References

Correll, J., Mellinger, C., McClelland, G. H., & Judd, C. M. (2020). Avoid Cohen’s “Small”, “Medium”, and “Large” for Power Analysis. Trends in Cognitive Sciences, 24(3), 200-207.

Correll, J., Mellinger, C., & Pedersen, E. J. (2022). Flexible approaches for estimating partial eta squared in mixed-effects models with crossed random factors. Behavior Research Methods, 54, 1626-1642.

Rights, J. D., & Sterba, S. K. (2019). Quantifying explained variance in multilevel models: An integrative framework for defining R-squared measures. Psychological Methods, 24(3), 309-338.

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.