Programming & Scientific Computing · R Programming & Statistical Analysis

R: Advanced Topics and Specific Applications

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction R language · Specific applications

Overview

Topics covered

  • Introduction
  • R in data analysis: overview and paradigms
  • 1.1 Document structure
  • Object systems: S3, S4, R6 and environments
  • 2.1 S3: attribute-based informal dispatch
  • 2.2 S4: formal classes with validation

Related concepts

Main connections

  • Survival analysis & Kaplan-Meier
  • Generalized linear models (GLM)
  • Binomial distribution
  • Poisson distribution
  • Bootstrap resampling
  • Logistic regression

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

ln(P(Y=1)1−P(Y=1))=β0+β1X1+…+βpXp.
View LaTeX source\ln\left(\frac{P(Y=1)}{1-P(Y=1)}\right) = \beta_0 + \beta_1 X_1 + \dots + \beta_p X_p.