- HOW TO ADD NEW PCA COLUMN BACK TO HOW TO
- HOW TO ADD NEW PCA COLUMN BACK TO CODE
- HOW TO ADD NEW PCA COLUMN BACK TO PC
Design and analysis of experiments in context Outliers: discrepancy, leverage, and influence of the observations More than one variable: multiple linear regression (MLR)
Summary of steps to build and investigate a linear model Least squares models with a single x-variable
The industrial practice of process monitoring Statistical tables for the normal- and t-distribution The normal distribution and checking for normality General summary: revealing complex data graphically Ind.cos2 <- apply(ind.coord, 2, cos2, d2) Var_coord_func <- function(loadings, comp.sdev) The contribution of a variable to a given principal component is (in percentage) : (var.cos2 * 100) / (total cos2 of the component)
HOW TO ADD NEW PCA COLUMN BACK TO HOW TO
Here we’ll show how to calculate the PCA results for variables: coordinates, cos2 and contributions: The grouping variable should be of same length as the number of active individuals (here 23). Qualitative / categorical variables can be used to color individuals by groups. The data sets decathlon2 contain a supplementary qualitative variable at columns 13 corresponding to the type of competitions. <- t(apply(ind.scaled, 1, coord_func, pca.loadings ))
HOW TO ADD NEW PCA COLUMN BACK TO CODE
The R code below can be used : # Centering and scaling the supplementary individuals
HOW TO ADD NEW PCA COLUMN BACK TO PC
Negative correlated variables point to opposite sides of the graph.Ĭol.var = "contrib", # Color by contributions to the PC Positive correlated variables point to the same side of the plot. Individuals with a similar profile are grouped together.Ĭol.ind = "cos2", # Color by the quality of representation Show the percentage of variances explained by each principal component. Res.pca <- prcomp(decathlon2.active, scale = TRUE) In this section we’ll provide an easy-to-use R code to compute and visualize PCA in R using the prcomp() function and the factoextra package.
The coordinates of the individuals (observations) on the principal components. The variable standard deviations (the scaling applied to each variable ) The variable means (means that were substracted) The matrix of variable loadings (columns are eigenvectors) The standard deviations of the principal components The elements of the outputs returned by the functions prcomp() and princomp() includes : prcomp() name If TRUE, the coordinates on each principal component are calculated If TRUE, the data will be centered and scaled before the analysis