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Plot a diagnostic likelihood-ratio heatmap

Source: R/PlotDiagnosticLRHeatmap.R
PlotDiagnosticLRHeatmap.Rd

Visualizes results created by DiagnosticLikelihoodRatioTable() without recomputing statistics. Tile fill is log2(LR), so reciprocal evidence is equally distant from LR 1. Multi-outcome input can return a compact screening overview alongside outcome-specific diagnostic panels.

Usage

PlotDiagnosticLRHeatmap(
  x,
  result = c("all", "positive", "negative"),
  predictor_order = c("original", "alphabetical", "strength", "cluster"),
  outcome_order = c("original", "alphabetical", "strength", "cluster"),
  orientation = c("auto", "predictors_rows", "outcomes_rows"),
  show_values = "auto",
  show_ci_marker = TRUE,
  facet_strata = TRUE,
  cap = NULL,
  na_color = "grey90",
  multi_outcome = c("auto", "combined", "split")
)

Arguments

x

An object returned by DiagnosticLikelihoodRatioTable(), or a tidy data frame compatible with its Results element.

result

Diagnostic result levels to display: "all", "positive", or "negative". Positive and negative selections require the result object.

predictor_order

Predictor ordering: "original", "alphabetical", "strength", or "cluster".

outcome_order

Outcome ordering with the same choices.

orientation

Tile orientation: "auto", "predictors_rows", or "outcomes_rows".

show_values

"auto", TRUE, or FALSE; auto labels at most 150 tiles.

show_ci_marker

Logical; append * when the unadjusted LR CI excludes 1.

facet_strata

Logical; facet separate diagnostic strata when present.

cap

Optional positive maximum absolute log2(LR) for color scaling.

na_color

Fill color for unavailable LR values.

multi_outcome

Multi-outcome display: "auto" returns a split overview/panel list for multiple outcomes, "combined" returns one all-results matrix, and "split" always returns the linked plot list.

Value

A named list. Single-outcome and combined displays contain DiagnosticLR, DiagnosticMatrices, DiagnosticLRData, and DiagnosticMatrixData. Split multi-outcome displays contain Overview, ByOutcome, DiagnosticMatrices, and their corresponding tidy data.

See also

DiagnosticLikelihoodRatioTable() to calculate displayed LRs.

Examples

data(SampleData)
data(SampleVariableTypes)
df_Labelled <- RevalueData(SampleData, SampleVariableTypes)$RevaluedData
df_Labelled$DiagnosisBinary <- factor(df_Labelled$Diagnosis,
  levels = c("Control", "Impaired"))
lr <- DiagnosticLikelihoodRatioTable(df_Labelled, "DiagnosisBinary",
  c("sex", "Genotype"))
#> DiagnosisBinary: 'Impaired' treated as outcome-positive.
plots <- PlotDiagnosticLRHeatmap(lr)
plots$DiagnosticLR

plots$DiagnosticMatrices