R: Nonparametric Multivariate Hyperrectangular Tolerance Regions

npmvtol.region {tolerance}

R Documentation

Nonparametric Multivariate Hyperrectangular Tolerance Regions

Description

Provides depth-based multivariate central or semi-space nonparametric tolerance regions. These can be calculated for any continuous multivariate data set. Either (P, 1-alpha) tolerance regions or beta-expectation tolerance regions can be specified.

Usage

npmvtol.region(x, alpha = NULL, P = NULL, Beta = NULL, depth.fn, 
               adjust = c("no", "floor", "ceiling"), 
               type = c("central", "semispace"), 
               semi.order = list(lower = NULL, center = NULL, upper = NULL), 
               L = -Inf, U = Inf, ...)

Arguments

`x`	An `n`x`p` matrix of data assumed to be drawn from a `p`-dimensional multivariate distribution. `n` pertains to the sample size.
`alpha`	The level chosen such that `1-alpha` is the confidence level. Note that if a (P, 1-alpha) tolerance region is required, then both `alpha` and `P` must be specified, but `Beta` must be set to `NULL`.
`P`	The proportion of the population to be covered by this tolerance interval. Note that if a (P, 1-alpha) tolerance region is required, then both `alpha` and `P` must be specified, but `Beta` must be set to `NULL`.
`Beta`	The confidence level for a beta-expectation tolerance region. Note that if a beta-expectation tolerance region is required, then `Beta` must be specified, but both `alpha` and `P` must be set to `NULL`.
`depth.fn`	The data depth function used to perform the ordering of the multivariate data. Thus function must be coded in such a way that the first argument is multivariate data for which to calculate the depth values and the second argument is the original multivariate sample, `x`. For the purpose of this tolerance region calculation, these two arguments should both be the original multivariate sample.
`adjust`	Whether an adjustment should be made during an intermediate calculation for determining the number of points that need to be included in the multivariate region. If `adjust = "no"`, the default, then no adjustment is made during the intermediate calculation. If `adjust = "floor"`, then the intermediate calculation is rounded down to the next nearest integer. If `adjust = "ceiling"`, then the intermediate calculation is rounded up to the next nearest integer.
`type`	The type of multivariate hyperrectangular region to calculate. If `type = "central"`, then two-sided intervals are reported for each dimension of the data `x`. If `type = "semispace"`, then a combination of one-sided intervals and two-sided intervals are reported for the dimensions of `x`. Which interval is calculated for each dimension in this latter setting is dictated by the `semi.order` argument.
`semi.order`	If `type = "semispace"`, then this argument must be specified. This argument is a list of length 3, such that each element gives the indices of the dimensions of `x` for which the type of interval should be calculated. Indices specified for the element of `lower` will return one-sided lower limits for those dimensions, indices specified for the element of `center` will return two-sided intervals for those dimensions, and indices specified for the element of `upper` will return one-sided upper limits for those dimensions.
`L`	If `type = "semispace"`, these are the lower limits for any dimensions for which one requests one-sided upper limits.
`U`	If `type = "semispace"`, these are the upper limits for any dimensions for which one requests one-sided lower limits.
`...`	Additional arguments passed to the `depth.fn` function.

Value

npmvtol.region returns a px2 matrix where the columns give the lower and upper limits, respectively, of the multivariate hyperrectangular tolerance region.

References

Young, D. S. and Mathew, T. (2020+), Nonparametric Hyperrectangular Tolerance and Prediction Regions for Setting Multivariate Reference Regions in Laboratory Medicine, Submitted.

Examples

 
## 90%/95% semi-space tolerance region for a sample
## of size 20 generated from a multivariate normal
## distribution. The mdepth function below is not 
## a true depth function, but used only for
## illustrative purposes.

mdepth <- function(pts, x){
          mahalanobis(pts, center = rep(0, 3),
                      cov = diag(1, 3))
          }

set.seed(100)
x <- cbind(rnorm(100), rnorm(100), rnorm(100))
out <-npmvtol.region(x = x, alpha = 0.10, P = 0.95, depth.fn = mdepth,
                     type = "semispace", semi.order = list(lower = 2, 
                     center = 3, upper = 1))
out

[Package tolerance version 2.0.0 Index]