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 more...
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...
ggfortify: Allow ggplot2 to handle some popular R packages. 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:GGallyextends ggplot2 for visualizingcorrelation matrix,scatterplot...
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)): ...
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 = iris...
factoextra is an R package for visualizing the results of multivariate analyses such as clustering or PCA, CA and MCA
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...
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 understanda...
X an object of class PCA, CA and MCA [FactoMineR]; prcomp and princomp [stats]; dudi, pca, coa and acm [ade4]; ca and mjca [ca package]. choice a text specifying the data to be plotted. Allowed values are “variance” or “eigenvalue”. geom a text specifying the...
Using epCA() [ExPosition] library("ExPosition") res.ca <- epCA(housetasks, graph = FALSE) No matter what functions you decide to use, in the list above, the factoextra package can handle the output. fviz_eig(res.ca) # Scree plot fviz_ca_biplot(res.ca)...