MTD: Multivariate Test of deficit

In singcar: Comparing Single Cases to Small Samples

Multivariate Test of deficit

Description

Testing for abnormality in the distance between a a vector of observations for a single case and a vector of population means. Please see vignette for further details.

Usage

MTD(
  case,
  controls,
  conf_level = 0.95,
  method = c("pd", "pchi", "pf", "pmd"),
  mahalanobis_dist = NULL,
  k = NULL,
  n = NULL
)

Arguments

case	Vector of case scores
controls	Matrix or data frame with scores from the control sample, each column representing a variable
conf_level	Level of confidence for the confidence intervals
method	One out of "pd", "pchi", "pf" and "pmd". Use "pmd" if the Mahalanobi's distance seems suspiciously small
mahalanobis_dist	Mahalanobi's distance of the case if summary statistics are used
k	The number of dimensions, if summary statistics are used
n	The size of the control sample

Value

A list with class "htest" containing the following components:

statistic	Hotelling's T^2 statistic for the case's Mahalanobi's distance
p.value	The p value associated with the Hotelling statistic
estimate	Estimates of the case Mahalanobis distance and index as well as abnormality
interval	List of interval measure for the estimates
sample.size	number of controls.
method	a character string indicating what type of test was performed and which abnormality measure used

Examples

caseA <- size_weight_illusion[1, "V_SWI"]
contA <- size_weight_illusion[-1, "V_SWI"]
caseB <- size_weight_illusion[1, "K_SWI"]
contB <- size_weight_illusion[-1, "K_SWI"]

MTD(case = c(caseA, caseB), controls = cbind(contA, contB),
  conf_level = 0.95, method = c("pd", "pchi", "pf", "pmd"),
  mahalanobis_dist = NULL, k = NULL, n = NULL)

singcar documentation built on March 31, 2023, 9:25 p.m.