The analysis of variance (ANOVA) model can be extended from making a comparison between multiple groups to take into account additional factors in an experiment. The simplest extension is from one-way to two-way ANOVA where a second factor is included in the model as well as a potential interaction between the two factors. As an example
When you use R at the command-line, the textual output is limited by the medium: one monospaced font, with no typesetting of any kind. That's great when you're doing exploratory analysis, but what about when you want to include R output in a report or publication? In other words, what if you want to convert this Analysis of Variance...
In a previous post we considered using R to fit one-way ANOVA models to data. In this post we consider a few additional ways that we can look at the analysis. In the analysis we made use of the linear model function lm and the analysis could be conducted using the aov function. The code used
Often times, a scatterplot reveals a pattern that seems not so linear. Polynomial regression can be used to explore a predictor at different levels of curvilinearity. This tutorial will demonstrate how polynomial regression can be used in a hierarchical fashion to best represent a dataset in R.
Often times, a scatterplot reveals a pattern that seems not so linear. Polynomial regression can be used to explore a predictor at different levels of curvilinearity. This tutorial will demonstrate how polynomial regression can be used in a hierarchical fashion to best represent a dataset in R.
Analysis of Variance (ANOVA) is a commonly used statistical technique for investigating data by comparing the means of subsets of the data. The base case is the one-way ANOVA which is an extension of two-sample t test for independent groups covering situations where there are more than two groups being compared. In one-way ANOVA the data
Regression models can become increasingly complex as more variables are included in an analysis. Furthermore, they can become exceedingly convoluted when things such as polynomials and interactions are explored. Thankfully, once the potential independe...
The commonly applied analysis of variance procedure, or ANOVA, is a breeze to conduct in R. This tutorial will explore how R can be used to perform ANOVA to analyze a single regression model and to compare multiple models.Tutorial FilesBefore we begin,...
The Latin square design is used where the researcher desires to control the variation in an experiment that is related to rows and columns in the field.
Remember that:
* Treatments are assigned at random within rows and columns, with each treatment once per row and once per column.
* There are equal numbers...
The Latin square design is used where the researcher desires to control the variation in an experiment that is related to rows and columns in the field.
Remember that:
* Treatments are assigned at random within rows and columns, with each treatment once per row and once per column.
* There are equal numbers...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
