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Data Transformation: Principal Components of Zoo Time Series

Source: R/transformations.R
f_pca.Rd

Uses stats::prcomp() to compute the Principal Component Analysis of a time series and return the principal components instead of the original columns. Output columns are named "PC1", "PC2" and so on.

Usage

f_pca(x = NULL, ...)

Arguments

x

(required, zoo object) Zoo time series object to transform.

...

(optional, additional arguments) Ignored in this function.

Value

zoo object

See also

Other tsl_transformation: f_binary(), f_chi_squared(), f_clr(), f_detrend_difference(), f_detrend_linear(), f_hellinger(), f_list(), f_log(), f_percentage(), f_proportion(), f_proportion_sqrt(), f_rescale_global(), f_rescale_local(), f_scale_global(), f_scale_local(), f_slope(), f_trend_linear()

Examples

x <- zoo_simulate(cols = 2)

y <- f_pca(
  x = x
)

if(interactive()){
  zoo_plot(x)
  zoo_plot(y)
}