lol.sims.rtrunk: Random Trunk In neurodata/lol: Linear Optimal Low-Rank Projection

Description

A simulation for the random trunk experiment, in which the maximal covariant dimensions are the reverse of the maximal mean differences.

Usage

 ```1 2``` ```lol.sims.rtrunk(n, d, rotate = FALSE, priors = NULL, b = 4, K = 2, maxvar = 100) ```

Arguments

 `n` the number of samples of the simulated data. `d` the dimensionality of the simulated data. `rotate` whether to apply a random rotation to the mean and covariance. With random rotataion matrix `Q`, `mu = Q*mu`, and `S = Q*S*Q`. Defaults to `FALSE`. `priors` the priors for each class. If `NULL`, class priors are all equal. If not null, should be `|priors| = K`, a length `K` vector for `K` classes. Defaults to `NULL`. `b` scalar for mu scaling. Default to `4`. `K` number of classes, should be <4. Defaults to `2`. `maxvar` the maximum covariance between the two classes. Defaults to `100`. `maxvar.outlier` the maximum covariance for the outlier points. Defaults to `maxvar*5`.

Value

A list of class `simulation` with the following:

 `X` `[n, d]` the `n` data points in `d` dimensions as a matrix. `Y` `[n]` the `n` labels as an array. `mus` `[d, K]` the `K` class means in `d` dimensions. `Sigmas` `[d, d, K]` the `K` class covariance matrices in `d` dimensions. `priors` `[K]` the priors for each of the `K` classes. `simtype` The name of the simulation. `params` Any extraneous parameters the simulation was created with. `robust` If robust is not false, a list containing `inlier` a boolean array indicating which points are inliers, `s.outlier` the covariance structure of outliers, and `mu.outlier` the means of the outliers.

Details

For more details see the help vignette: `vignette("sims", package = "lolR")`

Eric Bridgeford

Examples

 ```1 2 3``` ```library(lolR) data <- lol.sims.rtrunk(n=200, d=30) # 200 examples of 30 dimensions X <- data\$X; Y <- data\$Y ```

neurodata/lol documentation built on May 6, 2019, 8:51 a.m.