DataCheck5: Data cleaning for InterVA-5 algorithm

In InterVA5: Replicate and Analyse 'InterVA5'

Description

This function implements the data cleaning steps in the InterVA5 software.

Usage

DataCheck5(Input, id, probbaseV5, InSilico_check = FALSE, write)

Arguments

Input	original data vector for one observation coded by 0 (absence), 1 (presence), and NA (missing).
id	id for this observation
probbaseV5	matrix of probbaseV5
InSilico_check	logical indicator for if the check uses InSilicoVA rule. InSilicoVA rule sets all symptoms that should not be asked to missing. In contrast, the default InterVA5 rule sets these symptoms to missing only when they take the substantive value.
write	logical indicator of writing to file

Value

Output	new data vector
firstPass	message for the first pass check
secondPass	message for the second pass check

Author(s)

Jason Thomas, Zehang Li, Tyler McCormick, Sam Clark

References

http://www.interva.net/

See Also

InterVA5.plot

Examples

data(RandomVA5)
data(probbaseV5)
probbaseV5 <- as.matrix(probbaseV5)
RandomVA5 <- as.matrix(RandomVA5)
input <- as.character(RandomVA5[1, ])
input[which(toupper(input) == "N")] <- "0" 
input[which(toupper(input) == "Y")] <- "1" 
input[which(input != "1" & input != "0")] <- NA
input <- as.numeric(input)
output <- DataCheck5(Input=input, id="d1", probbaseV5=probbaseV5, write=TRUE)

InterVA5 documentation built on July 21, 2021, 5:06 p.m.