plotly_npmvtol: plotting Nonparametric Multivaraite Hyperrectangular...

In tolerance: Statistical Tolerance Intervals and Regions

plotting Nonparametric Multivaraite Hyperrectangular Tolerance Region

Description

plotly_npmvtol is plotting function for nonparametric multivaraite hyperrectangular tolerance region. The function takes the outcome of npmvtol.region as an input and provides visualzation for hypperrectangular tolerance regions between two variables.

Usage

plotly_npmvtol(tol.out, 
               x, 
               var.names = NULL, 
               title = NULL,
               x.col = "#4298B5",
               x.cex = 6,
               x.shape = "dot",
               outlier.col = "#A6192E",
               outlier.cex = 8,
               outlier.shape = "triangle-up",
               tol.col = "#D1DDE6",
               tol.opacity = 0.4,
               x.lab.size = 12,
               x.tick.size = 12,
               y.lab.size = 12,
               y.tick.size = 12,
               title.position.x = 0.5,
               title.position.y = 0.98,
               title.size = 12,
               show.bound = TRUE, 
               bound.type = c("dash", "dot", "solid", "longdash", 
                              "dashdot", "longdashdot"), 
               bound.col = "#000000",
               bound.lwd = 1
               )

Arguments

tol.out	Output from npmvtol.region for multivariate data.
x	Data frame for different variables. Columns of x represent for different variables.
var.names	Labels of variable names. The dimension of var.names needs to be consistent with column dimension of x.
title	The main title on top of the plot. The length of title can be either 1 or multiple. If only 1 title is specified, all plots share the same title. If multiple titles are specified, number of titles needs to be consistent with the number of combinations of variables. For example, if the data has 4 variables, either 1 or 6 (choose 2 out of 4) titles need to be specified.
x.col	Color of original data points, excluding outliers.
x.cex	Size of original data points, excluding outliers.
x.shape	Shape of original data points, excluding outliers.
outlier.col	Color of outliers.
outlier.cex	Size of outliers.
outlier.shape	Shape of outliers.
tol.col	Color of tolerance region.
tol.opacity	Opacity of tolerance region.
x.lab.size	Size of label of the x-axis.
x.tick.size	Size of tick marks on the x-axis.
y.lab.size	Size of label of the y-axis.
y.tick.size	Size of tick marks on the y-axis.
title.position.x	Horizontal position of the title.
title.position.y	Vertical position of the title.
title.size	Size of the title.
show.bound	Logical indicating to show rectanglular boundaries. Default is TRUE.
bound.type	Line type of the rectangle boundaries.
bound.col	Color of the rectangle boundaries.
bound.lwd	Width of the rectangle boundaries.

Value

plotly_npmvtol returns figures of hypperectangular tolerance regions between two random variable generated by npmvtol.region.

References

Young, D. S., & Mathew, T. (2020), Nonparametric Hyperrectangular Tolerance and Prediction Regions for Setting Multivariate Reference Regions in Laboratory Medicine. Statistical Methods in Medical Research, 29, 3569–3585.

See Also

npmvtol.region

Examples

 
library(plotly)

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

set.seed(100)
x <- cbind(X1=rnorm(300), X2=rnorm(300), X3=rnorm(300))
out <-npmvtol.region(x = x, alpha = 0.10, P = 0.90, depth.fn = mdepth,
                     type = "semispace", semi.order = list(lower = 2, 
                                                           center = 3, upper = 1))

gg.out <- plotly_npmvtol(tol.out = out , x = x)

tolerance documentation built on May 29, 2024, 7:38 a.m.