| pca {sdm} | R Documentation |
Principle Component Analysis
Description
pca performs a principal components analysis (using princomp function from stats package) on the given numeric data matrix and returns the results as an object of class princomp.
Usage
## S4 method for signature 'sdmdata'
pca(x,scale,filename,...)
## S4 method for signature 'data.frame'
pca(x,scale,filename,...)
## S4 method for signature 'RasterStackBrick'
pca(x,scale,filename,...)
## S4 method for signature 'SpatRaster'
pca(x,scale,filename,...)
Arguments
x |
sdmdata object, or a data.frame, or a Raster (either RasterStackBrick or SpatRaster) object |
scale |
logical; specifies whether the input data should be scaled (by subtracting the variable's mean, then dividing it by its standard deviation) |
filename |
optional character; specifies a filename that should be either a CSV file when |
... |
additional arguments pass to |
Details
pca analysis can be considered as a way to deal with multicollinearity problem and/or reduction of the data dimention. It returns two items in a list including data, and pca. The data contains the transoformed data into priciple components (the number of components is the same as the number of variables in the input data). You can check the pca item to see how many components (e.g., first 3) should be selected (e.g., by checking loadings). For more information on the calculation, see the princomp function.
Value
a list including data (a data.frame or a RasterStack depending on the type of x), and pca results (output of the princomp function)
Author(s)
Babak Naimi naimi.b@gmail.com
https://www.biogeoinformatics.org/
Examples
filename <- system.file('external/predictors.tif',package='sdm')
r <- rast(filename)
p <- pca(r) # p is a .pcaObject
p
plot(p@pcaObject) # or biplot(p@pcaObject)
plot(p@data)