Estimate the number of components in Principal Component Analysis
Estimate the number of components in PCA .
estim_ncp(X, ncp.min=0, ncp.max=NULL, scale=TRUE, method="GCV")
X |
a data frame with continuous variables |
ncp.min |
minimum number of dimensions to interpret, by default 0 |
ncp.max |
maximum number of dimensions to interpret, by default NULL which corresponds to the number of columns minus 2 |
scale |
a boolean, if TRUE (value set by default) then data are scaled to unit variance |
method |
method used to estimate the number of components, "GCV" for the generalized cross-validation approximation or "Smooth" for the smoothing method (by default "GCV") |
Returns ncp the best number of dimensions to use (find the minimum or the first local minimum) and the mean error for each dimension tested
Francois Husson Francois.Husson@agrocampus-ouest.fr, Julie JosseJulie.Josse@agrocampus-ouest.fr
Josse, J. and Husson, F. (2012). Selecting the number of components in PCA using cross-validation approximations. Computational Statistics and Data Analysis, 56, 1869-1879.
data(decathlon) nb.dim <- estim_ncp(decathlon[,1:10],scale=TRUE)
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