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Developing and Analyzing Statistical Models with R

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Introduction to R

Basics of R Objects in R Functions in R Data Representation in R Vectors and Matrices in R Data Frames and Lists in R Summary Exercise: Create Factors and a Data Frame Solution: Create Factors and a Data Frame Exercise: Create Plots with Numerical Data Solution: Create Plots with Numerical Data Exercise: Create Plots with Categorical Data Solution: Create Plots with Categorical Data Quiz: Introduction to R

Thoughts on Proper Data Analysis

Experimental Designs Plan an Experiment Decide Between Competing Analyses Summary

Exploratory Data Analysis and Data Summarization

About the Data Data Exploration and Error Checking Fixing Mistakes in the Data Summarizing and Manipulating Data Summary Exercise: Hands-on Data Filtering/Manipulation Solution: Hands-on Data Filtering/Manipulation Exercise: Create Box and Whisker Plots Solution: Create Box and Whisker Plots Quiz: Exploratory Data Analysis

Introduction to Plotting

Introduction to Plotting Different Types of Plotting Techniques Plotting Data Using gplots Plotting Data Using ggplot2 Summary Exercise: Create Plots with Facets, Colors and Descriptive Labels Solution: Create Plots with Facets, Colors and Descriptive Labels Exercise: Create Enhanced Bar Plots Solution: Create Enhanced Bar Plots Quiz: Introduction to Plotting

Basic Statistical Analysis Using R

Basic Statistical Analysis Testing for Normality in Our Data Nonparametric Tests Introducing Linear Models Multiple Comparisons Summary Exercise: Conduct a Series of One-way ANOVAs Solution: Conduct a Series of One-way ANOVAs Exercise: Examine the Effects of Log-transformation on Results Solution: Examine the Effects of Log-transformation on Results Quiz: Basic Statistical Analysis Using R

More Linear Models in R

Introduction to Multi-way ANOVA

More Linear Models

Linear Regression

Plotting the Regression

Analysis of Covariance (ANCOVA)

The Predict() Function

Plotting with ggplot() Instead of qplot()

Exercise: Analyze Data with Multiple Predictors

Solution: Analyze Data with Multiple Predictors

Exercise: Perform Analysis of Covariance (ANCOVA) and Plot

Solution: Perform Analysis of Covariance (ANCOVA) and Plot

Exercise: Statistical Analysis with Different Interactions

Solution: Statistical Analysis with Different Interactions

Quiz: More Linear Models

Advanced Statistical Analysis Using R

Generalized Linear Models Understanding and Interpreting the GLM GLMs and ANCOVAs Summary Exercise: Explore the Data with Multiple Predictors Solution: Explore Data with Multiple Predictors Exercise: Appropriately Visualize the Significant Findings Solution: Appropriately Visualize the Significant Findings Quiz: Advanced Analytical Statistics Using R

Mixed-effects Model

Understanding Mixed-effects Models Diagnostic Plots Generalized Linear Mixed Model (GLMM)More About Model Selection Summary Exercise: Rerun the ANCOVA Models Solution: Rerun the ANCOVA Models Exercise: Compare the AIC Scores Solution: Compare the AIC Scores Quiz: Mixed Effects Models

Advanced Data Wrangling and Plotting

The tidyverse Library Basic Data Wrangling Advanced Data Wrangling Making Better Figures with ggplot2 Customizing a Figure in R Faceting in R Combining Data Wrangling and Plotting with ggplot2 Summary Exercise: Create a Bar Graph for Each Tank in the Experiment Solution: Create a Bar Graph for Each Tank in the Experiment Quiz: Advanced Wrangling and Plotting

Writing Loops and Functions in R

For Loops Functions Writing a Simple Function Writing More Complex Functions Summary Exercise: Automate the Repeated Tasks and Simulate the Data Solution: Automate the Repeated Tasks and Simulate the Data Quiz: Writing Loops and Functions in R

Appendix

Setup on the Local Machine Create Well-documented and Annotated Code

Conclusion

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Solution: Perform Analysis of Covariance (ANCOVA) and Plot

Solution: Perform Analysis of Covariance (ANCOVA) and Plot

Let's look at the solution of the previous exercise.

Solution

As we mentioned while giving the exercise, the assignment consists of two steps:

We need to pick a pair of continuous variables.
We need to use predict() to plot it.

This lesson provides one example of a possible answer we might get, and hopefully, that’ll give us an idea as to how we can make a figure like this.

We’re using the effect of resources on final mass at metamorphosis, controlling for variation in age at metamorphosis.

Perhaps the simplest thing we could do is change the response variable. Instead of looking at SVL, let’s examine mass. To make the example a little more interesting, let’s use Res ...