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The mlstats package provides tools for multilevel
descriptive statistics and data preparation. It computes within-group
and between-group correlations (via variance decomposition or two-level
structural equation modeling), intraclass correlation coefficients
(ICCs), and descriptive statistics for nested data (e.g., repeated
measurements per person), supporting both frequentist (via
lme4 or lavaan) and Bayesian (via
brms) estimation. Results are formatted according to APA
standards and can be exported as tables using gt or
tinytable. The package also includes functions for
decomposing variables into within-group and between-group components for
use in Random Effects Within-Between (REWB) models.
You can install mlstats from CRAN:
install.packages("mlstats")You can also install the development version from GitHub:
# install.packages("pak")
pak::pak("felixdidi/mlstats")mlstats is built around two main tasks:
mldesc()
computes means, standard deviations, ranges, ICCs, and a combined
within-/between-group correlation matrix in a single call.
within_between_correlations() computes only the correlation
matrix. Both support multiple print methods for publication-ready
output.decompose_within_between() splits time-varying predictors
into within-group deviations (situational fluctuations) and
between-group means (stable differences), ready for use in REWB
models.This example uses the simulated media_diary dataset
included with mlstats. It mimics a study in which 100
participants completed brief daily surveys for 14 consecutive days
(N = 100 persons, T = 1,400 daily observations).
library(mlstats)
data("media_diary")
media_diary |>
mldesc(
group = "person",
vars = c("self_control", "wellbeing", "screen_time", "stress")
)## # Multilevel Descriptive Statistics
## ============ ===== ====== ===== ===== ===== ===== ===== ===== =====
## variable n_obs m sd range `1` `2` `3` `4` icc
## ------------ ----- ------ ----- ----- ----- ----- ----- ----- -----
## 1 Self control 1,400 4.03 0.83 2–6 – NA NA NA 1.00
## 2 Wellbeing 1,400 4.45 0.87 2–7 .61* – .42* -.43* .46
## 3 Screen time 1,400 128.66 42.29 0–272 -.67* -.34* – .29* .45
## 4 Stress 1,400 3.81 0.91 1–7 -.53* -.38* .38* – .33
## ============ ===== ====== ===== ===== ===== ===== ===== ===== =====
## # ℹ Within-person correlations above, between-person correlations below the
## # diagonal.
## # ℹ All correlations marked with a star are significant at p < .05.
## # ℹ Correlations estimated via variance decomposition.
## # ℹ Group-weighted multilevel descriptive statistics computed with mlstats.mlstats comes with documentation vignettes to help you get started:
vignette("mlstats").vignette("multilevel-descriptives")
and how to transform them into publication-ready multilevel
descriptive tables in vignette("tables").vignette("rewb-models").vignette("correlation-methods").Bell, A., Fairbrother, M., & Jones, K. (2019). Fixed and random effects models: Making an informed choice. Quality & Quantity, 53(2), 1051–1074. https://doi.org/10.1007/s11135-018-0802-x
Enders, C. K., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12(2), 121–138. https://doi.org/10.1037/1082-989X.12.2.121
Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction (3rd ed.). Harcourt Brace.
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.