Generalized Additive Models (GAM) are non-parametric models that add smoother to the data. In this exercise, we will look at GAMs using cubic spline using the mgcv package. Data-sets used can be downloaded here. The data-set is the experiment result of grassland richness over time in the Yellowstone National Park (Skkink et al. 2007). […]

# REcology

## Basic Generalized Additive Models In Ecology: Solutions

Below are the solutions to these exercises on “GAMs – Exercises.” if (!require(mgcv)){install.packages(mgcv, dep=T)} library(mgcv) if (!require(car)){install.packages(car, dep=T)} library(car) ############### # # # Exercise 1 # # # ############### Veg <- read.csv(file.choose()) str(Veg) ## ‘data.frame’: 58 obs. of 8 variables: ## $ SR : int 8 6 8 8 10 7 6 5 8 6 […]

## Modeling With ANCOVA – Part 2: Solutions

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) […]

## 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”, […]

## Groups Comparison With ANOVA: Exercises (Part 2)

On this 2nd part of groups comparison exercise, we will focus on nested ANOVA application in R, particularly the application on ecology. This is the last part of groups comparison exercise.Previous exercise can be found here Answers to the exercises are available here. If you obtained a different (correct) answer than those listed on the […]

## Groups Comparison With ANOVA: Solutions (Part 2)

Below are the solutions to these exercises on Two way ANOVA. if (!require(car)){install.packages(car, dep=T)} ## Warning: package ‘car’ was built under R version 3.3.2 library(car) if (!require(ggplot2)){install.packages(ggplot2, dep=T)} ## Warning: package ‘ggplot2’ was built under R version 3.3.3 library(ggplot2) if (!require(dplyr)){install.packages(dplyr, dep=T)} ## Warning: package ‘dplyr’ was built under R version 3.3.3 library(dplyr) if (!require(lattice)){install.packages(lattice, […]

## Groups Comparison with ANOVA: Exercises (Part 1)

As we’re aware, the growth of data science has been increased recently, and successfully applied on research for decision making or creating baseline conditions. Statistical analysis, including data visualization, exploration, and modeling are three main important elements in data science. In this exercise, we’ll learn how to analyze response and explanatory variables of data that […]

## Groups Comparison with ANOVA: Solutions (Part 1)

Below are the solutions to these exercises on “Two Way ANOVA’s.” if (!require(car)){install.packages(car, dep=T)} ## Warning: package ‘car’ was built under R version 3.3.2 library(car) if (!require(ggplot2)){install.packages(ggplot2, dep=T)} ## Warning: package ‘ggplot2’ was built under R version 3.3.3 library(ggplot2) if (!require(dplyr)){install.packages(dplyr, dep=T)} ## Warning: package ‘dplyr’ was built under R version 3.3.3 library(dplyr) if (!require(lattice)){install.packages(lattice, […]