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docs: clarify how variables are filtered out in step_corr() #1518

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Jun 24, 2025
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docs: clarify how variables are filtered out in step_corr() #1518

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a8558a8
docs: clarify how variables are filtered out in step_corr()
erictleung Jun 23, 2025
fe1af1f
refactor: clarify variable and documentation
erictleung Jun 24, 2025
77b22e4
run devtools::document()
EmilHvitfeldt Jun 24, 2025
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10 changes: 8 additions & 2 deletions R/corr.R
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,10 @@
#' This step attempts to remove variables to keep the largest absolute
#' correlation between the variables less than `threshold`.
#'
#' The filter tries to prioritize predictors for removal based on the global
#' affect on the overall correlation structure. If you have two identical
#' predictors, the variable ordered first will be removed.
#'
#' When a column has a single unique value, that column will be excluded from
#' the correlation analysis. Also, if the data set has sporadic missing values
#' (and an inappropriate value of `use` is chosen), some columns will also be
Expand Down Expand Up @@ -246,14 +250,16 @@ corr_filter <-
diag(x) <- 1
}
averageCorr <- colMeans(abs(x))
averageCorr <- as.numeric(as.factor(averageCorr))
avgCorrVarsRank <- as.numeric(as.factor(averageCorr))
x[lower.tri(x, diag = TRUE)] <- NA
combsAboveCutoff <- which(abs(x) > cutoff)

colsToCheck <- ceiling(combsAboveCutoff / nrow(x))
rowsToCheck <- combsAboveCutoff %% nrow(x)

colsToDiscard <- averageCorr[colsToCheck] > averageCorr[rowsToCheck]
# Discard column variable in the correlation pair with the higher average
# correlation across all pairwise correlations
colsToDiscard <- avgCorrVarsRank[colsToCheck] > avgCorrVarsRank[rowsToCheck]
rowsToDiscard <- !colsToDiscard

deletecol <- c(colsToCheck[colsToDiscard], rowsToCheck[rowsToDiscard])
Expand Down
4 changes: 4 additions & 0 deletions man/step_corr.Rd
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