# quanti sup pca

## Summary

Principal component analysis (PCA) is a method used to summarize and visualize data containing multiple inter-correlated quantitative variables. 1 It allows us to reduce the number of variables by creating a smaller set of new variables, called principal components, which are linear combinations of the original variables. 1 PCA is used to identify patterns in data and to reduce the dimensionality of the data set. 1

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Summary Principal component analysis ( PCA ) allows us to summarize and to visualize the information in a data set containing individuals/observations described by multiple inter-correlated quantitative variables. Each variable could be considered as a different dimension.
PCA - Principal Component Analysis Essentials - Articles - STHDA sthda.com

Performs Principal Component Analysis ( PCA ) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables. Missing values are replaced by the column mean. Usage PCA (X, scale.unit = TRUE, ncp =…
FactoMineR: PCA – R documentation – Quantargo quantargo.com

Allowed values are “none” or the combination of c(“ind”, “ind. sup ”, “quali”, “var”, “ quanti . sup ”). “ind” can be used to label only active individuals. “ind. sup ” is for supplementary individuals. “quali” is for…
fviz_pca: Quick Principal Component Analysis data visualization - R ... sthda.com

There are 4 eigenvalues greater than 1, and the components account for 74.7% of the total variation, with the first two accounting for about 50%. We can create a table…
Principal Components Analysis statpower.net

Principal Component Analysis (PCA ) Description Performs Principal Component Analysis (PCA ) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables. Missing values are replaced by the column mean. Usage
PCA: Principal Component Analysis (PCA) in FactoMineR: Multivariate ... rdrr.io

Principal Component Analysis ( PCA ) Description Performs Principal Component Analysis ( PCA ) with supplementary individuals, supplementary quantitative variables and supplementary categorical variables. Missing values are replaced by the column mean. Usage
R: Principal Component Analysis (PCA) r-project.org

PCA (Principal Components Analysis)即主成分分析，也称主分量分析或主成分回归分析法，是一种无监督的数据降维方法。 首先利用线性变换，将数据变换到一个新的坐标系统中；然后再利用降维的思想，使得任何数据投影的第一大方差在第一个坐标 (称为第一主成分)上，第二大方差在第二个坐标 (第二主成分)上。 这种降维的思想首先减少数据集的维数，同时还保持数据集的对方差贡献最大的特征，最终使数据直观呈现在二维坐标系。
PCA(principal component analysis,主成分分析) - 简书 jianshu.com

Principal component analysis ( PCA ) allows us to summarize and to visualize the information in a data set containing individuals/observations described by multiple inter-correlated quantitative variables. Each variable could be considered…
4 Detailed study of Principal Component Analysis - GitHub Pages f0nzie.github.io

quanti.sup. a list of matrices containing all the results for the supplementary quantitative variables (coordinates, correlation between variables and axes) quali.sup. a list of matrices containing all the results for…
PCA function - RDocumentation rdocumentation.org

PCA : Principal Component Analysis ( PCA ) plot.CA: Draw the Correspondence Analysis (CA) graphs; plot.CaGalt: Draw the Correspondence Analysis on Generalised Aggregated... plot.catdes: Plots for description of clusters (catdes) plot.DMFA: Draw the…
plot.PCA : Draw the Principal Component Analysis (PCA) graphs rdrr.io

data (decathlon) res. pca <- PCA (decathlon, quanti . sup = 11:12, quali. sup = 13) plot (res. pca , habillage = 13, cex=0.8) if (FALSE) { plot (res. pca , habillage = "cos2") plot (res. pca , habillage =…
plot.PCA function - RDocumentation rdocumentation.org