lol.project.pca: Principal Component Analysis (PCA)
In lolR: Linear Optimal Low-Rank Projection

Description Usage Arguments Value Details Author(s) Examples

A function that performs PCA on data.

1	lol.project.pca(X, r, xfm = FALSE, xfm.opts = list(), robust = FALSE, ...)

`X`	`[n, d]` the data with `n` samples in `d` dimensions.
`r`	the rank of the projection.
`xfm`	whether to transform the variables before taking the SVD. FALSEapply no transform to the variables. 'unit'unit transform the variables, defaulting to centering and scaling to mean 0, variance 1. See `scale` for details and optional arguments to be passed with `xfm.opts`. 'log'log-transform the variables, for use-cases such as having high variance in larger values. Defaults to natural logarithm. See `log` for details and optional arguments to be passed with `xfm.opts`. 'rank'rank-transform the variables. Defalts to breaking ties with the average rank of the tied values. See `rank` for details and optional arguments to be passed with `xfm.opts`. c(opt1, opt2, etc.)apply the transform specified in opt1, followed by opt2, etc.
`xfm.opts`	optional arguments to pass to the `xfm` option specified. Should be a numbered list of lists, where `xfm.opts[[i]]` corresponds to the optional arguments for `xfm[i]`. Defaults to the default options for each transform scheme.
`robust`	whether to perform PCA on a robust estimate of the covariance matrix or not. Defaults to `FALSE`.
`...`	trailing args.

A list containing the following:

`A`	`[d, r]` the projection matrix from `d` to `r` dimensions.
`d`	the eigen values associated with the eigendecomposition.
`Xr`	`[n, r]` the `n` data points in reduced dimensionality `r`.

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

Eric Bridgeford

library(lolR)
data <- lol.sims.rtrunk(n=200, d=30)  # 200 examples of 30 dimensions
X <- data$X; Y <- data$Y
model <- lol.project.pca(X=X, r=2)  # use pca to project into 2 dimensions