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Violin Plots to Compare Within Group (Repeated Measures)

Source: R/jjwithinstats.b.R
jjwithinstatsClass.Rd

Creates violin plots for within-subjects (repeated measures) analysis using ggstatsplot::ggwithinstats. Compares 2-4 measurements from the same subjects with statistical testing and pairwise comparisons. Ideal for biomarker tracking, treatment response monitoring, and longitudinal clinical studies.

Value

A jamovi analysis object with violin plots, statistical tests, and clinical interpretation

Details

Data Requirements:

  • Wide format required (one row per subject)

  • Each column represents a different time point or condition

  • Complete data required for paired analysis (listwise deletion)

  • Minimum 3 subjects with complete data across all measurements

Statistical Tests:

  • Parametric: Repeated measures ANOVA (assumes normality)

  • Nonparametric: Friedman test (no distribution assumptions)

  • Robust: Uses trimmed means (resistant to outliers)

  • Bayesian: Provides evidence strength via Bayes Factors

Clinical Presets:

  • Biomarker: Optimized for laboratory biomarker tracking (nonparametric)

  • Treatment: Optimized for treatment response monitoring (parametric with pairwise)

  • Laboratory: Optimized for clinical lab values (robust)

Performance Optimization

The function implements sophisticated caching:

  • Data preparation cached based on variable selection and data content

  • Options cached separately to minimize reprocessing

  • Plot state management prevents unnecessary regeneration

  • Checkpoint calls before expensive operations for responsiveness

Clinical Validation

The function performs comprehensive data quality checks:

  • Validates paired design requirements (complete cases)

  • Detects small sample sizes (< 10 subjects)

  • Identifies potential outliers (> 10% outliers)

  • Warns about skewed data for parametric tests

  • Alerts to high missing data rates (> 50%)

References

Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach. Journal of Open Source Software, 6(61), 3167. doi:10.21105/joss.03167

See also

ggwithinstats for the underlying plotting function

Super classes

jmvcore::Analysis -> ClinicoPath::jjwithinstatsBase -> jjwithinstatsClass

Methods

Public methods

Inherited methods

Method asSource()

Generate R source code for Within-Groups Statistics analysis

Usage

jjwithinstatsClass$asSource()

Returns

Character string with R syntax for reproducible analysis

Method clone()

The objects of this class are cloneable with this method.

Usage

jjwithinstatsClass$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
# Basic within-subjects analysis
data(iris)
iris_wide <- data.frame(
    Subject = 1:50,
    Baseline = iris$Sepal.Length[1:50],
    Month3 = iris$Sepal.Width[1:50] * 2.5,
    Month6 = iris$Petal.Length[1:50] * 1.8
)

jjwithinstats(
    data = iris_wide,
    dep1 = "Baseline",
    dep2 = "Month3",
    dep3 = "Month6",
    typestatistics = "parametric",
    pairwisecomparisons = TRUE,
    centralityplotting = TRUE,
    pointpath = TRUE
)

# Clinical biomarker tracking (nonparametric)
jjwithinstats(
    data = clinical_data,
    dep1 = "CRP_baseline",
    dep2 = "CRP_week4",
    dep3 = "CRP_week12",
    clinicalpreset = "biomarker",
    typestatistics = "nonparametric",
    pairwisecomparisons = TRUE,
    mytitle = "C-Reactive Protein Levels During Treatment"
)

# Robust analysis with publication plot
jjwithinstats(
    data = lab_data,
    dep1 = "ALT_pre",
    dep2 = "ALT_post",
    typestatistics = "robust",
    centralityplotting = TRUE,
    centralitytype = "robust",
    addGGPubrPlot = TRUE,
    ggpubrPlotType = "paired",
    ggpubrAddStats = TRUE
)
} # }