# plotmixt: Plot for 1- to 3-dimensional normal and t-mixture density... In ks: Kernel Smoothing

## Description

Plot for 1- to 3-dimensional normal and t-mixture density functions.

## Usage

 ```1 2``` ```plotmixt(mus, sigmas, Sigmas, props, dfs, dist="normal", draw=TRUE, deriv.order=0, which.deriv.ind=1, binned=TRUE, ...) ```

## Arguments

 `mus` (stacked) matrix of mean vectors `sigmas` vector of standard deviations (1-d) `Sigmas` (stacked) matrix of variance matrices (2-d, 3-d) `props` vector of mixing proportions `dfs` vector of degrees of freedom `dist` "normal" - normal mixture, "t" - t-mixture `draw` flag to draw plot. Default is TRUE. `deriv.order` derivative order `which.deriv.ind` index of which partial derivative to plot `binned` flag for binned estimation of contour levels. Default is TRUE. `...` other graphics parameters, see `plot.kde`

## Value

If `draw=TRUE`, the 1-d, 2-d plot is sent to graphics window, 3-d plot to RGL window. If `draw=FALSE`, then a `kdde`-like object is returned.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## bivariate mus <- rbind(c(0,0), c(-1,1)) Sigma <- matrix(c(1, 0.7, 0.7, 1), nr=2, nc=2) Sigmas <- rbind(Sigma, Sigma) props <- c(1/2, 1/2) plotmixt(mus=mus, Sigmas=Sigmas, props=props, display="filled.contour") ## trivariate mus <- rbind(c(0,0,0), c(-1,0.5,1.5)) Sigma <- matrix(c(1, 0.7, 0.7, 0.7, 1, 0.7, 0.7, 0.7, 1), nr=3, nc=3) Sigmas <- rbind(Sigma, Sigma) props <- c(1/2, 1/2) plotmixt(mus=mus, Sigmas=Sigmas, props=props, dfs=c(11,8), dist="t") ```

ks documentation built on July 26, 2018, 9:01 a.m.