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, ...)
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X |
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r |
the rank of the projection. |
xfm |
whether to transform the variables before taking the SVD.
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xfm.opts |
optional arguments to pass to the |
robust |
whether to perform PCA on a robust estimate of the covariance matrix or not. Defaults to |
... |
trailing args. |
A list containing the following:
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the eigen values associated with the eigendecomposition. |
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For more details see the help vignette:
vignette("pca", package = "lolR")
Eric Bridgeford
1 2 3 4 | 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
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