STIC.RFimpute: A wrapper for missForest for random forest STIC imputation

In streamDAG: Analytical Methods for Stream DAGs

A wrapper for missForest for random forest STIC imputation

Description

A simple wrapper for the missForest random forest imputation algorithm. STIC.RFimpute first converts STIC (Stream Temperature, Intermittency, and Conductivity) presence/absence data to categorical outcomes to avoid regression fitting. One should consult missForest for specifics on the underlying algorithm.

Usage

STIC.RFimpute(p.a, ...)

Arguments

p.a	Optimally, a dataframe containing presence absence data at sites (columns) over time (rows).
...	Additional arguments from missForest

Value

Provides the conventional unaltered missForest output.

Author(s)

Daniel J. Stekhoven, <[e-mail]>

References

Stekhoven, D.J. and Buehlmann, P. (2012), 'MissForest - nonparametric missing value imputation for mixed-type data', Bioinformatics, 28(1) 2012, 112-118, doi: 10.1093/bioinformatics/btr597

Examples

arc.pa <- data.frame(matrix(ncol = 3, data = c(1,1,1, 0,1,1, 1,1,1, 0,NA,1), byrow = TRUE))
names(arc.pa) <- c("n1 --> n2", "n2 --> n3", "n3 --> n4")

STIC.RFimpute(arc.pa)

streamDAG documentation built on April 4, 2025, 12:28 a.m.