Package {mlstats}

mlstats: Multilevel Descriptive Statistics and Data Preparation

Description

The mlstats package provides tools for multilevel descriptive statistics and data preparation (e.g., repeated measurements per person, or students nested within schools). It supports:

• Computing within-group and between-group correlations (within_between_correlations())

• Creating publication-ready descriptive statistics tables with ICCs and within-/between-group correlations (mldesc())

• Decomposing variables into within-group and between-group components for Random Effects Within-Between (REWB) models (decompose_within_between())

• Three estimation methods, selectable via the method argument of mldesc() and within_between_correlations(): variance decomposition (default), two-level structural equation modeling (via lavaan), and Bayesian multilevel modeling (via brms)

• Exporting result tables as 'gt' or 'tinytable' objects via print(result, format = "gt") or print(result, format = "tt")

See vignette("correlation-methods") for the statistical background, and media_diary for an example dataset.

Author(s)

Maintainer: Felix Dietrich mail@felix-dietrich.de [copyright holder]

See Also

Useful links:

• https://felixdidi.github.io/mlstats/

• https://github.com/felixdidi/mlstats

• Report bugs at https://github.com/felixdidi/mlstats/issues

Decompose Variables into Within-Group and Between-Group Components

Description

This function performs a multilevel decomposition of variables by computing:

• Grand mean centered scores (deviations from overall mean)

• Between-group scores (group means)

• Within-group scores (deviations from group means)

Usage

decompose_within_between(
  data,
  group,
  vars,
  components = c("gmc", "between", "within"),
  gmc_pattern = "{col}_grand_mean_centered",
  between_pattern = "{col}_between_{group}",
  within_pattern = "{col}_within_{group}"
)

Arguments

Details

This decomposition is commonly used in multilevel modeling to separate within-group and between-group variance components (Enders & Tofighi, 2007). The decomposed variables are particularly useful for Random Effects Within-Between (REWB) models (Bell et al., 2019), which allow the estimation of distinct within-group and between-group effects.

The function performs three centering operations:

1. Grand mean centering: Each value is expressed as a deviation from the overall sample mean. This centers the entire distribution at zero.

2. Between-group component: For each observation, this equals the mean of their group. These values are constant within groups and vary between groups. In REWB models, this represents the between-group effect of the predictor.

3. Within-group component: Each value is expressed as a deviation from their group mean. This removes all between-group variance and represents the within-group effect of the predictor in REWB models.

Value

A data frame containing:

• All original variables from data

• Grand mean centered versions (named by gmc_pattern), if "gmc" in components

• Between-group means (named by between_pattern), if "between" in components

• Within-group deviations (named by within_pattern), if "within" in components

References

Bell, A., Fairbrother, M., & Jones, K. (2019). Fixed and random effects models: making an informed choice. Quality & Quantity, 53(2), 1051-1074.

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.

See Also

within_between_correlations, which uses this function internally to perform the within/between decomposition.

Examples

data("media_diary")

# Decompose all three components (default)
result <- decompose_within_between(
  data = media_diary,
  group = "person",
  vars = c("stress", "screen_time")
)

# Only between and within (no grand mean centering)
result_wb <- decompose_within_between(
  data = media_diary,
  group = "person",
  vars = c("stress", "screen_time"),
  components = c("between", "within")
)

# Custom column naming: flat suffixes without the group name
result_flat <- decompose_within_between(
  data = media_diary,
  group = "person",
  vars = c("stress", "screen_time"),
  components = c("between", "within"),
  between_pattern = "{col}_between",
  within_pattern = "{col}_within"
)

Simulated daily diary study: entertainment media use and wellbeing

Description

A simulated daily diary dataset for illustrating multilevel descriptive statistics with mlstats. The data mimics a study in which 100 participants completed brief daily surveys for 14 consecutive days, reporting their wellbeing, perceived stress, entertainment media use, and enjoyment on that media. Trait self-control was measured once at the beginning of the study.

The dataset is designed to illustrate the difference between within-person and between-person correlations, including a case where the two go in opposite directions (screen_time × wellbeing):

• Within persons: on days when someone watches more entertainment media than usual, they report slightly better wellbeing — consistent with short-term escapism or mood repair through media use.

• Between persons: people who watch more entertainment media on average tend to report lower average wellbeing — chronic heavy media use is associated with lower wellbeing, partly because it reflects lower trait self-control.

The pooled (naive) correlation between screen_time and wellbeing is near zero, masking both of these real effects.

Usage

media_diary

Format

A data frame with 1,400 rows and 6 columns:

person

Integer person identifier (1–100).

self_control

Trait self-control, measured once at study entry (1–7 scale, higher = more self-control). Constant within persons; ICC approximately 1.

wellbeing

Daily positive wellbeing (1–7 scale, higher = better).

screen_time

Minutes of entertainment media consumed that day (e.g., television, streaming services; non-negative integer).

stress

Daily perceived stress (1–7 scale, higher = more stressed).

enjoyment

How much the person enjoyed the media they watched that day (1–7 scale, higher = more enjoyment).

Source

Simulated data. Generated by data-raw/media_diary.R using a fixed random seed (set.seed(42)) for reproducibility. See that script for full simulation details including the intended within- and between-person correlation structure.

Examples

data("media_diary")

# Quick look at the structure
str(media_diary)

# Number of persons and observations
length(unique(media_diary$person))  # 100 persons
nrow(media_diary)                   # 1,400 diary entries

Compute Multilevel Descriptive Statistics

Description

Creates a publication-ready descriptive statistics table for multilevel data (e.g., repeated measurements per person, or students nested within schools). For each variable, the table reports basic descriptives, the proportion of variance that lies between groups (the intraclass correlation, ICC), and how each pair of variables relates both within and between groups (see within_between_correlations and vignette("correlation-methods") for the statistical background on the latter).

Usage

mldesc(
  data,
  group,
  vars,
  method = c("decomposition", "sem", "bayes"),
  weight = TRUE,
  flip = FALSE,
  significance = c("basic", "detailed"),
  ci = 0.9,
  folder = NULL,
  remove_leading_zero = TRUE
)

Arguments

Details

The function combines three types of information:

Descriptive statistics: Basic summary statistics for each variable. When weight = TRUE (default), statistics are calculated across all observations. When weight = FALSE, the mean is the mean of group means, and the SD is the standard deviation of group means, representing between-group variability.

Correlations: Within-group correlations (upper triangle) and between-group correlations (lower triangle), computed using within_between_correlations. See that function's documentation and the package vignette for how each method estimates these correlations and tests them for significance.

ICC: The intraclass correlation coefficient, computed from an unconditional (intercept-only) multilevel model using lme4::lmer (or brms::brm when method = "bayes"). The ICC represents the proportion of variance in each variable that lies between groups, with values close to 1 indicating a variable that barely varies within groups (e.g., a stable trait), and values close to 0 indicating a variable that barely varies between groups (e.g., a fast-changing state).

The ICC is always computed from a linear (Gaussian) model, regardless of a variable's measurement scale. For binary, ordinal, or count variables this yields a linear-probability-style ICC rather than a latent-scale ICC from a generalized linear mixed model. A warning is emitted if any vars look binary, ordinal, or count-like (few, whole-number values).

With method = "bayes", the function fits one brms model per variable for the ICCs, plus all the models described in within_between_correlations for the correlations — for p variables, p ICC fits in addition to the within/between-group correlation fits. This can take a long time for larger numbers of variables; see vignette("correlation-methods") for details.

Value

A tibble of class mlstats_desc_tibble containing:

• variable: Variable name

• n_obs: Number of observations

• m: Mean

• sd: Standard deviation

• range: Range from minimum to maximum

• One column per variable in vars containing correlations

• icc: Intraclass correlation coefficient

The tibble can be returned as a gt object using print(result, format = "gt") and as a tinytable object using print(result, format = "tt").

References

Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28. doi:10.18637/jss.v080.i01

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. Harcourt Brace.

Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Sage Publishers.

See Also

within_between_correlations for details on how within-group and between-group correlations are estimated and tested.

Examples

data("media_diary")
vars <- c("self_control", "wellbeing", "screen_time", "stress")

# Compute multilevel descriptives (default: decomposition method)
result <- mldesc(
  data = media_diary,
  group = "person",
  vars = vars
)

result

# Compute with unweighted between-group correlations
result_unweighted <- mldesc(
  data = media_diary,
  group = "person",
  vars = vars,
  weight = FALSE
)

# Use SEM-based estimation for correlations (on similarly-scaled variables;
# SEM is sensitive to large scale differences, unlike "decomposition")

result_sem <- mldesc(
  data = media_diary,
  group = "person",
  vars = c("self_control", "wellbeing", "stress"),
  method = "sem"
)


# Use detailed significance marking
result_detailed <- mldesc(
  data = media_diary,
  group = "person",
  vars = vars,
  significance = "detailed"
)

# Use Bayesian estimation for correlations and the ICC (requires brms)

result_bayes <- mldesc(
  data = media_diary,
  group = "person",
  vars = c("self_control", "wellbeing", "screen_time"),
  method = "bayes",
  folder = tempdir()
)

Compute Within-Group and Between-Group Correlations

Description

In data with a grouping structure (e.g., repeated measurements per person, or students nested within schools), a single correlation between two variables can be misleading, because it mixes two different relationships: how the variables relate within each group (e.g., do a person's good days also tend to be their productive days?), and how they relate between groups (e.g., do people who are generally happier also tend to be generally more productive?). This function estimates both relationships separately, using one of three methods (see Details and vignette("correlation-methods") for the full statistical background).

Usage

within_between_correlations(
  data,
  group,
  vars,
  method = c("decomposition", "sem", "bayes"),
  weight = TRUE,
  flip = FALSE,
  significance = c("basic", "detailed"),
  ci = 0.9,
  folder = NULL
)

Arguments

Details

Method "decomposition" (the default) computes the within-group correlation by first subtracting each group's mean from every observation, then correlating the resulting deviation scores. It computes the between-group correlation by correlating the group means with one another (optionally weighted by group size; see weight). This approach follows Pedhazur (1997, ch. 16), and the significance tests account for the fact that subtracting group means uses up degrees of freedom, following the general testing principle in Snijders and Bosker (2012, sec. 6.1). This method is fast and easy to interpret, and works well for most data sets, but is less suited to data with very unequal group sizes.

Method "sem" fits a two-level structural equation model (via lavaan::sem()) that estimates the within-group and between-group covariance matrices simultaneously using maximum likelihood. Significance is based on the resulting z-tests. Because groups are weighted implicitly through maximum likelihood estimation rather than through the weight argument, this method is the more principled choice for data with very unequal group sizes or a moderate amount of missing data. It is slower than "decomposition" and can occasionally fail to converge for small or collinear data sets.

For method = "sem", variables that never vary within a group (e.g., time-invariant traits) are modeled only at the between-group level, and variables with almost no between-group variance (intraclass correlation near zero) are modeled only at the within-group level; the corresponding cells of the unused level are reported as NA.

Method "bayes" mirrors "decomposition", but estimates both correlations via Bayesian multivariate models fit with brms::brm() (requires the brms package) instead of closed-form formulas, reporting posterior medians and credible intervals (via ci) in place of point estimates and p-values. It requires a folder argument to cache fitted models, can take considerably longer than the other two methods, and is most useful when the number of groups is small or when communicating uncertainty via credible intervals is a priority. See vignette("correlation-methods") for details on the number of models fit and caching behavior.

Value

A tibble containing a correlation matrix where:

• The upper triangle contains within-group correlations

• The lower triangle contains between-group correlations

• Diagonal elements are marked with "–"

• Significant correlations are marked with asterisks (see significance parameter, or ci when method = "bayes")

The tibble can be returned as a gt object using print(result, format = "gt") and as a tinytable object using print(result, format = "tt").

References

Bürkner, P.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28. doi:10.18637/jss.v080.i01

Hox, J., Moerbeek, M., & van de Schoot, R. (2018). Multilevel analysis: Techniques and applications (3rd ed.). Routledge.

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. Harcourt Brace.

Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Sage Publishers.

See Also

mldesc, which combines this function's output with descriptive statistics and ICCs in a single table. See vignette("correlation-methods") for a detailed statistical description of all three methods.

Examples

data("media_diary")

# Compute weighted between-group correlations (default, decomposition method)
result_weighted <- within_between_correlations(
  data = media_diary,
  group = "person",
  vars = c("wellbeing", "screen_time")
)

# Compute unweighted between-group correlations
result_unweighted <- within_between_correlations(
  data = media_diary,
  group = "person",
  vars = c("wellbeing", "screen_time"),
  weight = FALSE
)

# Use SEM-based estimation (on similarly-scaled variables; SEM is
# sensitive to large scale differences, unlike "decomposition")

result_sem <- within_between_correlations(
  data = media_diary,
  group = "person",
  vars = c("wellbeing", "stress"),
  method = "sem"
)


# Use detailed significance marking
result_detailed <- within_between_correlations(
  data = media_diary,
  group = "person",
  vars = c("wellbeing", "screen_time"),
  significance = "detailed"
)

# Use Bayesian estimation (requires the brms package)

result_bayes <- within_between_correlations(
  data = media_diary,
  group = "person",
  vars = c("wellbeing", "screen_time"),
  method = "bayes",
  folder = tempdir()
)