A Practical Guide to Visualization and Statistical Analysis of Infection Data Using R

Overview

Journal Front Plant Sci

Date 2017 May 10

PMID 28484483

Citations 9

Authors

Niklas Schandry

Affiliations

Soon will be listed here.

Abstract

This paper describes and summarizes approaches for visualization and statistical analysis using data from infection experiments based on methods and concepts that are broadly applicable. Members of the species complex cause bacterial wilt disease. Bacterial wilt is a lethal plant disease and has been studied for over 100 years. During this time various methods to quantify disease and different ways to analyze the generated data have been employed. Here, I aim to provide a general background on three distinct and commonly used measures of disease: the area under the disease progression curve, longitudinal recordings of disease severity and host survival. I will discuss how one can proceed with visualization, statistical analysis, and interpretation using different datasets while revisiting the general concepts of statistical analysis. Datasets and R code to perform all analyses discussed here are included in the supplement.

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