depth.mdata: Provides the depth measure for multivariate data In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

 depth.mdata R Documentation

Provides the depth measure for multivariate data

Description

Compute measure of centrality of the multivariate data. Type of depth function: simplicial depth (SD), Mahalanobis depth (MhD), Random Half–Space depth (HS), random projection depth (RP) and Likelihood Depth (LD).

Usage

```mdepth.LD(x, xx = x, metric = metric.dist, h = NULL, scale = FALSE, ...)

mdepth.HS(x, xx = x, proj = 50, scale = FALSE, xeps = 1e-15, random = FALSE)

mdepth.RP(x, xx = x, proj = 50, scale = FALSE)

mdepth.MhD(x, xx = x, scale = FALSE)

mdepth.KFSD(x, xx = x, trim = 0.25, h = NULL, scale = FALSE, draw = FALSE)

mdepth.FSD(x, xx = x, trim = 0.25, scale = FALSE, draw = FALSE)

mdepth.FM(x, xx = x, scale = FALSE, dfunc = "TD1")

mdepth.TD(x, xx = x, xeps = 1e-15, scale = FALSE)

mdepth.SD(x, xx = NULL, scale = FALSE)
```

Arguments

 `x` is a set of points, a d-column matrix. `xx` is a d-dimension multivariate reference sample (a d-column matrix) where `x` points are evaluated. `metric` Metric function, by default `metric.dist`. Distance matrix between `x` and `xx` is computed. `h` Bandwidth, `h>0`. Default argument values are provided as the 15%–quantile of the distance between `x` and `xx`. `scale` =TRUE, scale the depth, see scale. `...` Further arguments passed to or from other methods. `proj` are the directions for random projections, by default 500 random projections generated from a scaled `runif(500,-1,1)`. `xeps` Accuracy. The left limit of the empirical distribution function. `random` =TRUE for random projections. =FALSE for deterministic projections. `trim` The alpha of the trimming. `draw` =TRUE, draw the curves, the sample median and trimmed mean. `dfunc` type of univariate depth function used inside depth function: "FM1" refers to the original Fraiman and Muniz univariate depth (default), "TD1" Tukey (Halfspace),"Liu1" for simplical depth, "LD1" for Likelihood depth and "MhD1" for Mahalanobis 1D depth. Also, any user function fulfilling the following pattern `FUN.USER(x,xx,...)` and returning a `dep` component can be included.

Details

Type of depth measures:

• The `mdepth.SD` calculates the simplicial depth (HD) of the points in `x` w.r.t. `xx` (for bivariate data).

• The `mdepth.HS` function calculates the random half–space depth (HS) of the points in `x` w.r.t. `xx` based on random projections `proj`.

• The `mdepth.MhD` function calculates the Mahalanobis depth (MhD) of the points in `x` w.r.t. `xx`.

• The `mdepth.RP` calculates the random' projection depth (RP) of the points in `x` w.r.t. `xx` based on random projections `proj`.

• The `mdepth.LD` calculates the Likelihood depth (LD) of the points in `x` w.r.t. `xx`.

• The `mdepth.TD` function provides the Tukey depth measure for multivariate data.

Value

• lmed Index of deepest element `median` of `xx`.

• ltrim Index of set of points `x` with trimmed mean `mtrim`.

• dep Depth of each point `x` w.r.t. `xx`.

• proj The projection value of each point on set of points.

• xis a set of points to be evaluated.

• xx a reference sample

• name Name of depth method

Author(s)

`mdepth.RP`, `mdepth.MhD` and `mdepth.HS` are versions created by Manuel Febrero Bande and Manuel Oviedo de la Fuente of the original version created by Jun Li, Juan A. Cuesta Albertos and Regina Y. Liu for polynomial classifier.

References

Liu, R. Y., Parelius, J. M., and Singh, K. (1999). Multivariate analysis by data depth: descriptive statistics, graphics and inference,(with discussion and a rejoinder by Liu and Singh). The Annals of Statistics, 27(3), 783-858.

Functional depth functions: `depth.FM`, `depth.mode`, `depth.RP`, `depth.RPD` and `depth.RT`.

Examples

```## Not run:
data(iris)
group<-iris[,5]
x<-iris[,1:2]

MhD<-mdepth.MhD(x)
PD<-mdepth.RP(x)
HD<-mdepth.HS(x)
SD<-mdepth.SD(x)

x.setosa<-x[group=="setosa",]
x.versicolor<-x[group=="versicolor",]
x.virginica<-x[group=="virginica",]
d1<-mdepth.SD(x,x.setosa)\$dep
d2<-mdepth.SD(x,x.versicolor)\$dep
d3<-mdepth.SD(x,x.virginica)\$dep

## End(Not run)
```

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.