WebThis function is designed to point out the variables and the categories that are the most characteristic according to each dimension obtained by a Factor Analysis. a tab containing the summary of the dataset and a boxplot and histogram for quantitative variables. a tab containing the dataset with a nice display. WebFeb 3, 2024 · In Factoshiny: Perform Factorial Analysis from 'FactoMineR' with a Shiny Application. Description Usage Arguments Value Author(s) See Also Examples. View source: R/FAMDshiny.R. Description. Performs Factor Analysis for Mixed Data (FAMD) with supplementary individuals, supplementary quantitative variables and supplementary …
Interactive plots in PCA with Factoshiny R-bloggers
WebThe package Factoshiny. A beautiful graph tells more than a lengthy speech!! It is crucial to improve the graphs obtained by any Principal Component Methods (PCA, CA, MCA, MFA, … WebFactoshiny Perform Factorial Analysis from 'FactoMineR' with a Shiny Application Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a … jo frost coach
Factoshiny - mran.microsoft.com
WebThe function Factoshiny of the package Factoshiny allows you to perform PCA in a really easy way. You can include extras information such as categorical variables, manage missing data , draw and improve the graphs interactively , have several numeric indicators as outputs, perform clustering on the PCA results, and even have an automatic ... WebOct 22, 2024 · Factoshiny: Perform Factorial Analysis from 'FactoMineR' with a Shiny Application Perform factorial analysis with a menu and draw graphs interactively thanks … Webthe Factoshiny package can also generate the code used to construct the graphs. 2 The functions of the Factoshiny package Several functions are available according to the dataset and the nature of the active variables. Nature of active variables Method Function continuous Principal Component Analysis PCAshiny intel assistance download