This provides a vector of cue_validities and potentially other useful information, particularly if reverse_cues=TRUE. For example, education is negatively associated with number of felonies. If reverse_cues=FALSE, education will get validity < 0.5. If reverse_cues=TRUE, then LESS education will be used as a predictor, resulting in: 1) cue_validity > 0.5 2) cue_direction == -1 To use the cue for prediction, be sure to multiply it by the cue_direction. For ranking, based heuristics, cue_ranks gives the rank order of cues where highest validity = rank 1 (after reversing, if any).
cueValidityComplete(
data,
criterion_col,
cols_to_fit,
replaceNanWith = 0.5,
reverse_cues = FALSE,
ties.method = "random"
)The matrix or data.frame whose columns are treated as cues.
The index of the column used as criterion.
A vector of indexes of the columns to calculate cue validity for.
The value to return as cue validity in case it cannot be calculated.
Optional parameter to reverse cues as needed. By default, the model will reverse the cue values for cues with cue validity < 0.5, so a cue with validity 0 becomes a cue with validity 1. Set this to FALSE if you do not want that, i.e. the cue stays validity 0.
An optional parameter passed to rank: A character string sepcifying how ties (in cue validity) are treated.
A list where $cue_validities has a vector of validities for each of the columns in cols_to_fit.
Wikipedia's entry on https://en.wikipedia.org/wiki/Cue_validity