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. If you have mo...
These include plotting 1) Matrix; 2) Linear Model and Generalized Linear Model; 3) Time Series; 4) PCA/Clustering; 5) Survival Curve; 6) Probability distribution GGally: GGally extends ggplot2 for visualizing correlation matrix, scatterplot plot matrix, survival plot and more. ggRandomForests...
To reduce overplotting, the argumentjitteris used in the functionsfviz_pca_xx(),fviz_ca_xx()andfviz_mca_xx()available in the R packagefactoextra. The argumentjitteris a list containing the parameterswhat,widthandheight(i.e jitter = list(what, width, height)): ...
PCA reduces the dimensionality of multivariate data, to two or three that can be visualized graphically with minimal loss of information. Several functions from different packages are available in R for performing PCA : prcomp and princomp (built-in R stats package), PCA (FactoMineR package), dud...
In the situation where there multiple response variables you can test them simultaneously using a multivariate analysis of variance (MANOVA). This article describes how to compute manova in R. For example, we may conduct an experiment where we give two treatments (A and B) to two groups of...
Plotting PCA (Principal Component Analysis) Data set: iris Function: autoplot.prcomp() # Prepare the data df <- iris[, -5] # Principal component analysis pca <- prcomp(df, scale. = TRUE) # Plot autoplot(pca, loadings = TRUE, loadings.label = TRUE, data = ...
Several functions from different packages - FactoMineR, ade4, ExPosition, stats - are available in R for performing PCA, CA or MCA. However, The components of the output vary from package to package. No matter the package you decided to use, factoextra can give you a human understandab...
fviz_pca_biplot(): Biplot of individuals of variables Infos Description Draw the graph of individuals/variables from the output of Principal Component Analysis (PCA). The following functions, from factoextra package are use: fviz_pca_ind(): Graph of individuals fviz_pca_var():...
Principal component analysis (PCA) (Chapter @ref(principal-component-analysis)) when variables are quantitative, Multiple correspondence analysis (MCA) (Chapter @ref(multiple-correspondence-analysis)) when variables are qualitative. This global analysis, where multiple sets...