Plot Categorical Interaction Effects Matrix

This function calculates and visualizes the interaction effects between categorical variables.

Usage

PlotCatInteractionEffectsMatrix(
  data,
  predictor_vars,
  outcome_vars = NULL,
  xVarLabels = NULL,
  yVarLabels = NULL,
  interVar,
  Data = lifecycle::deprecated(),
  xVars = lifecycle::deprecated(),
  fdr_scope = c("matrix", "per_outcome", "per_predictor"),
  yVars = lifecycle::deprecated()
)

Arguments

data

The dataset containing the variables.

predictor_vars

A character vector of the names of the x-axis categorical variables.

outcome_vars

A character vector of the names of the y-axis categorical variables. Defaults to NULL, in which case it takes the same values as xVars.

xVarLabels

A character vector of labels for the x-axis variables. Defaults to NULL, in which case it takes the same values as xVars.

yVarLabels

A character vector of labels for the y-axis variables. Defaults to NULL, in which case it takes the same values as yVars.

interVar

The name of the interaction variable.

Data

Deprecated (since 19.15.0). Use data instead.

xVars

Deprecated (since 19.15.0). Use predictor_vars instead.

fdr_scope

Either "matrix" (default) or "per_outcome", passed to ApplyFDRCorrection(). "matrix" corrects across all interaction p-values at once (historical behavior). "per_outcome" corrects separately within each y-axis variable (outcome_vars).

yVars

Deprecated (since 19.15.0). Use outcome_vars instead.

Value

A list containing matrices of interaction coefficients, p-values, ggplot objects for visualizations, and tables of FDR-corrected p-values.

Examples

data(SampleData)
data(SampleVariableTypes)

# Attach labels and factor levels for readable axes
Labelled <- RevalueData(SampleData, SampleVariableTypes)$RevaluedData

result <- PlotCatInteractionEffectsMatrix(
  Labelled,
  predictor_vars = c("Alpha_2_Macroglobulin", "Angiopoietin_2_ANG_2",
                     "Apolipoprotein_A_IV", "Apolipoprotein_A1",
                     "Apolipoprotein_A2", "Apolipoprotein_B",
                     "Apolipoprotein_CI", "Apolipoprotein_CIII",
                     "Apolipoprotein_D", "Apolipoprotein_E"),
  outcome_vars = c("age", "ACE_CD143_Angiotensin_Converti",
                   "ACTH_Adrenocorticotropic_Hormon", "AXL", "Adiponectin",
                   "Alpha_1_Antichymotrypsin", "Alpha_1_Antitrypsin",
                   "Alpha_1_Microglobulin"),
  interVar = "Diagnosis"
)
#> Joining with `by = join_by(X, Y)`
#> Joining with `by = join_by(X, Y)`

# Raw p-value interaction matrix
result$p


# FDR-adjusted interaction matrix
result$p_FDR