One way analysis of variance using R

-

Comments

Post a Comment

Popular posts from this blog

Two way repeated measures analysis in R

-
Image
See Video ⮞ ☝ AGRON Info-Tech June 20, 2020 Description of data Clear R environment Importing data Fitting ANOVA model Mean comparison test SNK test Visualizing results Barplot for time variable Barplot for group variable In this tutorial you will learn how to carry out analysis for two way repeated measures anova using R studio. A two way repeated measures anova compares the mean differences between groups that have been split on two within subject factors. These factors are also known as independent variables. Two way repeated measures anova is also known as: Two factor repeated measures anova Two factor or two way anova with repeated measures Within within subjects anova A two way repeated measures anova is often used in studies where you have measured: A dependent variable over two or more time points When subjects have undergone tw
Read more

Split plot analysis in R

-
Image
See Video   ⮞  ☝ AGRON Stats June 26, 2018 Description of design Import data file Observe variables Fit analysis of variance model Mean separation test Get error df and error MS values LSD test on main effects LSD test on interaction term Plot variation Plot bargraphs Main effects Interaction effect You will learn how to carry out split plot analysis, mean separation test using LSD.test() function and plotting the main and interaction effects using base graphics functions in R studio. Description of design Split-plot design is frequently used for factorial experiments. Such design may incorporate one or more of the completely randomized, completely randomized block, and Latin square designs. The main principle is that there are whole plots or whole units, to which the levels of one or more factors are applied. Thus each whole plot becomes a bloc
Read more

Principal component analysis in R

-
Image
See Video   ⮞  ☝ AGRON Stats May 26, 2020 Introduction Import data set Standardize the data set Extraction of components (eigenvalues) Eigen values & vectors Proportion of variance Computation of principal component vectors Computation of vectors from stats package Display a screeplot Correlation of original variables with PCs Displaying correlation of original variables with PCs Plotting principle components Introduction The principal aim of the principal component analysis is dimension reduction . Sometimes the data set consists of several variables. For example, the projects related to soil horizon data contain more than a hundred variables. It is difficult to graphically inspect the main data structure of a multivariate data set. It is required to find components that express as much of the inherent variability of the complete data set as possible
Read more