plotCorrelations: plotCorrelations

In miceRanger: Multiple Imputation by Chained Equations with Random Forests

Description

Plot the correlation of imputed values between every combination of datasets for each variable.

Usage

plotCorrelations(
  miceObj,
  vars = names(miceObj$callParams$vars),
  factCorrMetric = "CramerV",
  numbCorrMetric = "pearson",
  ...
)

Arguments

miceObj	an object of class miceDefs, created by the miceRanger function.
vars	the variables you want to plot. Default is to plot all variables. Can be a vector of variable names, or one of 'allNumeric' or 'allCategorical'
factCorrMetric	The correlation metric for categorical variables. Can be one of: "CramerV" Cramer's V correlation metric. "Chisq" Chi Square test statistic. "TschuprowT" Tschuprow's T correlation metric. "Phi" (Binary Variables Only) Phi coefficient. "YuleY" (Binary Variables Only) Yule's Y, also known as coefficient of colligation "YuleQ" (Binary Variables Only) Yule's Q, related to Yule's Y by Q=2Y/(1+Y^2)
numbCorrMetric	The correlation metric for numeric variables. Can be one of: "pearson" Pearson's Correlation Coefficient "spearman" Spearman's Rank Correlation Coefficient "kendall" Kendall's Rank Correlation Coefficient "Rsquared" R-squared
...	Other arguments to pass to ggarrange()

Value

an object of class ggarrange.

Examples

data("sampleMiceDefs")
plotCorrelations(sampleMiceDefs)

Example output

miceRanger documentation built on Sept. 6, 2021, 5:07 p.m.