Publications by Easy Guides
Bar plot of Group Means with Individual Observations
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 easily a barplot of group means +/- standar...
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Assessing clustering tendency: A vital issue – Unsupervised Machine Learning
1 Required packages 2 Data preparation 2.1 faithful dataset 2.2 Random uniformly distributed dataset 3 Why assessing clustering tendency? 4 Methods for assessing clustering tendency 4.1 Hopkins statistic 4.1.1 Algorithm 4.1.2 R function for computing Hopkins statistic 4.2 VAT: Visual Assessment of cluster Tendency 4.2.1 VAT Algorithm 4.2.2 R fu...
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Hybrid hierarchical k-means clustering for optimizing clustering outputs – Unsupervised Machine Learning
1 How this article is organized 2 Required R packages 3 Data preparation 4 R function for clustering analyses 4.1 Example of k-means clustering 4.2 Example of hierarchical clustering 5 Combining hierarchical clustering and k-means 5.1 Why? 5.2 How ? 5.3 R codes 5.3.1 Compute hierarchical clustering and cut the tree into k-clusters: 5.3.2 Compute...
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Chi-Square Test of Independence in R
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 using R ...
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Survival Analysis Basics
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 analysis”. In...
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Cox Proportional-Hazards Model
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 met...
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Cox Model Assumptions
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 assumptions. Note that, when used i...
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R packages
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 ...
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survminer 0.2.4
I’m very pleased to announce survminer 0.2.4. It comes with many new features and minor changes. Install survminer with: install.packages("survminer") To load the package, type this: library(survminer) Contents New features Minor changes Bug fixes Summary of survival curves Plot survival curves Determine the optimal cutpoint for continuous va...
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Survival Analysis
Survival analysis corresponds to a set of statistical methods for investigating the time it takes for an event of interest to occur. In this chapter, we start by describing how to fit survival curves and how to perform logrank tests comparing the survival time of two or more groups of individuals. We continue by demonstrating how to assess simul...
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