Below are the solutions to these exercises on “ANCOVA – Part 2.” if (!require(car)){install.packages(car, dep=T)} library(car) if (!require(ggplot2)){install.packages(ggplot2, dep=T)} library(ggplot2) ############### # # # Exercise 1 # # # ############### # Load data limpet<-read.csv(file.choose()) limpet.lm <- lm(EGGS ~ DENSITY * SEASON, data = limpet) ############### # # # Exercise 2 # # # ############### predict(limpet.lm) […]

# ANCOVA

## Modeling With ANCOVA – Part 2: Exercises

In this 2nd part of ANCOVA modeling exercises, we will focus on the extend of ANCOVA visualization using the predict function. The function will help us to plot the linear regression of ANCOVA and also predict other useful information that aids our interpretation (We’ll see later.) The previous exercise can be found here. Answers to these […]

## Modeling With ANCOVA – Part 1: Exercises

In the previous exercise on the #REcology series, we learned how to define the impact of one explanatory variable to another response variable. In a real practice, particularly in experimental or observational design, explanatory variables are often found to be more than one. Thus, it needs a new determination to analyze the data-set and generate […]

## Modeling With ANCOVA – Part 1: Solutions

Below are the solutions to these exercises on “ANCOVA.” if (!require(car)){install.packages(car, dep=T)} ## Warning: package ‘car’ was built under R version 3.3.2 library(car) ############### # # # Exercise 1 # # # ############### # Load data comp<-read.csv(file.choose()) ############### # # # Exercise 2 # # # ############### #scatterplot sct_plot<-scatterplot(Fruit ~ Root | Grazing, data=comp, xlab=”Root”, […]