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Create a reusable MCA object and visualizations

Source: R/CreateMCATable.R
CreateMCAObject.Rd

This function performs Multiple Correspondence Analysis (MCA) on a set of categorical variables, imputes missing data if needed, and generates a set of visualizations and tables to interpret the results.

CreateMCATable() has been superseded by CreateMCAObject(). It remains available as a backwards-compatible alias and returns the same reusable MCA object.

Usage

CreateMCAObject(
  data,
  VarsToReduce,
  VariableCategories = NULL,
  minThresh = 75,
  scale = TRUE,
  center = TRUE,
  Relabel = TRUE,
  Ordinal = FALSE,
  numComponents = NULL,
  ImputeMissing = FALSE,
  Data = lifecycle::deprecated()
)

CreateMCATable(...)

Arguments

data

A dataframe containing the data to be analyzed.

VarsToReduce

A vector of column names in Data to be included in the MCA.

VariableCategories

An optional vector to assign specific categories to the variables in VarsToReduce. These will be used to color the loadings plot.

minThresh

A numeric value representing the minimum cumulative variance threshold to determine the number of components. Default is 75%.

scale

Logical, indicating whether to scale the variables. Default is TRUE.

center

Logical, indicating whether to center the variables. Default is TRUE.

Relabel

Logical, if TRUE, the function will replace missing labels in the data using an external helper function ReplaceMissingLabels. Default is TRUE.

Ordinal

Logical, if TRUE, the function will treat variables as ordinal for MCA. Default is FALSE.

numComponents

An optional integer specifying the number of components to retain. If NULL, the number of components will be determined based on minThresh.

ImputeMissing

Logical, if TRUE, missing values will be imputed using missRanger. Default is FALSE.

Data

Deprecated (since 19.15.0). Use data instead.

...

Arguments passed to CreateMCAObject().

Value

A list with the following elements:

p_scree

A ggplot object representing the scree plot, showing the cumulative and percentage of variance explained by each component.

pcaresults

The MCA results object, which includes component scores and contributions.

LoadingTable

A data frame with the variable loadings for each component.

Scores

A data frame with the MCA scores for each individual in the data.

CombinedData

The original data combined with the MCA scores.

Lollipop

A ggplot object showing a lollipop plot of variable loadings across components.

Details

Optional dependencies used by this workflow include missRanger for imputation and FactoMineR for MCA calculation. These packages are listed in DESCRIPTION and should be installed for MCA workflows.

Examples

data(SampleData)
data(SampleVariableTypes)

Labelled <- RevalueData(SampleData, SampleVariableTypes)$RevaluedData

mca <- CreateMCAObject(
  Labelled,
  VarsToReduce = c("Diagnosis", "Genotype")
)

# Variance explained by MCA dimensions
mca$p_scree


# Variable loadings across MCA dimensions
mca$Lollipop