In this section, you’ll find R packages developed by STHDA for easy data analyses. factoextra factoextra let you extract and create ggplot2-based elegant visualizations of multivariate data analyse results, including PCA, CA, MCA, MFA, HMFA and clustering methods. Overview ____factoextra Site Link ____ survminer survminer provides functions for facilitating ... [Read more...]

Previously, we described the basic methods for analyzing survival data, as well as, the Cox proportional hazards methods to deal with the situation where several factors impact on the survival process. In the current article, we continue the series by describing methods to evaluate the validity of the Cox model ... [Read more...]

The Cox proportional-hazards model (Cox, 1972) is essentially a regression model commonly used statistical in medical research for investigating the association between the survival time of patients and one or more predictor variables. In the previous chapter (survival analysis basics), we described the basic concepts of survival analyses and methods for ... [Read more...]

Survival analysis corresponds to a set of statistical approaches used to investigate the time it takes for an event of interest to occur. Survival analysis is used in a variety of field such as: Cancer studies for patients survival time analyses, Sociology for “event-history analysis”, and in engineering for “failure-time ... [Read more...]

The chi-square test of independence is used to analyze the frequency table (i.e. contengency table) formed by two categorical variables. The chi-square test evaluates whether there is a significant association between the categories of the two variables. This article describes the basics of chi-square test and provides practical examples ... [Read more...]

1 How this article is organized 2 Required R packages 3 Data preparation 4 R function for clustering analyses4.1 Example of k-means clustering 4.2 Example of hierarchical clustering 5 Combining hierarchical clustering and k-means5.1 Why? 5.2 How ? 5.3 R codes5.3.1 Compute hierarchical clustering and cut the tree into k-clusters: 5.3.2 Compute the centers of clusters defined by hierarchical clustering: 5.3.3 ... [Read more...]

1 Required packages 2 Data preparation2.1 faithful dataset 2.2 Random uniformly distributed dataset 3 Why assessing clustering tendency? 4 Methods for assessing clustering tendency4.1 Hopkins statistic4.1.1 Algorithm 4.1.2 R function for computing Hopkins statistic 4.2 VAT: Visual Assessment of cluster Tendency4.2.1 VAT Algorithm 4.2.2 R functions for VAT 5 A single function for Hopkins statistic and VAT 6 Infos Clustering ... [Read more...]

Example data sets Install ggpubr Bar plot of group means with individual informations ggpubr is great for data visualization and very easy to use for non-“R programmer”. It makes easy to simply produce an elegant ggplot2-based graphs. Read more about ggpubr: ggpubr . Here we demonstrate how to plot ... [Read more...]