aberrantscores {triplesmatch}

Convert outcome to aberrant ranks

Description

Replaces non-aberrant responses by 0 and ranks the aberrant responses by severity. The more aberrant responses have the highest ranks.

Usage

aberrantscores(ymat, cutoff, cutoff_dir = "less", tau = 0, treated1 = NULL)

Arguments

ymat

A matrix of outcomes. Rows correspond to matched triples and the three columns correspond to the three units in the match. The first unit is the one treated unit if 'treated1 == TRUE' or the one control unit if 'treated1 == FALSE'. The other two columns contain the remaining two units in the match. These are control units if 'treated1 == TRUE' or treated units if 'treated1 == FALSE'. This can easily be created from the triples match using the [formattrip()] function with 'type == "wide"'

cutoff

The cutoff for whether an outcome is aberrant. Any outcome more extreme then this cutoff will be considered aberrant

cutoff_dir

Either 'less' or 'greater', indicating whether outcomes should be aberrant if they are less than the 'cutoff' or greater than the 'cutoff', respectively

tau

The null treatment effect to be subtracted from all treated units before aberrant ranking commences. If 'tau != 0', then 'treated1' is required

treated1

A logical vector with length equal to the number of triples. 'TRUE' if there is only one treated unit in the matched triple; 'FALSE' if there are two treated units and only one control unit. This can easily be created from the triples match using the [formattrip()] function with 'type == "wide"'

Details

This can be useful for creating 'scores' in [sentrip()] for an aberrant rank test.

Value

A matrix similar to 'ymat' in all regards other than the outcomes being converted to aberrant ranks

See Also

aberrantscoreslong for the same function with inputs given in the long format as opposed to the wide format

formattrip for formatting the triples match into long or wide format

Examples

# Generate some data
set.seed(246)
n <- 30
x <- rnorm(n, 0, 3)
nt <- floor(n * 0.5)
nc <- n - nt
z <- c(rep(1, nt), rep(0, nc))
# Create a distance matrix, everything in one stratum
dist <- dist_mahal(data.frame(x = x), z, rep(1, n))[[1]]
# Create the triples match
triplesm <- triples_st(cost = dist, z = z, solver = "rlemon")
# Create an outcome
y <- 1:40
# Give the outcome some random unit names
names(y) <- sample(1:40)
# Reformat the triples match
ywide <- formattrip(m = triplesm, y = y, type = "wide")
# Turn the outcome into scores, in this case aberrant ranks
ab <- aberrantscores(ywide$ymat, 15, cutoff_dir = "less", tau = 0, treated1 = NULL)
# Conduct a one-sided hypothesis test with a bias of gamma = 1.25
sentrip(scores = ab, treated1 = ywide$treated1, gamma = 1.25, alternative = "greater")