Articles by Hanif Kusuma

Basic Generalized Linear Modeling – Part 4: Exercises

August 29, 2018 | 0 Comments

This exercise is going to be the last exercise on Basic Generalized Linear Modeling (GLM). Please click here to find the other part of the Basic GLM Exercise that you’ve missed. In this exercise, we will discuss Logistic Regression models as one of the GLM methods. The model is ...
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Basic Generalized Linear Modeling – Part 3: Exercises

August 15, 2018 | 0 Comments

In this exercise, we will continue to solve problems from the last exercise about GLM here. Therefore, the exercise number will start at 9. Please make sure you read and follow the previous exercise before you continue practicing. In the last exercise, we knew that there was over-dispersion over the model. ...
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Basic Generalized Linear Modeling – Part 2: Exercises

August 1, 2018 | 0 Comments

In this exercise, we will try to handle the model that has been over-dispersed using the quasi-Poisson model. Over-dispersion simply means that the variance is greater than the mean. It’s important because it leads to inflation in the models and increases the possibility of Type I errors. We will ...
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Basic Generalised Linear Modelling – Part 1: Exercises

July 18, 2018 | 0 Comments

A generalized linear model (GLM) is a flexible generalization of an ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function ... [Read more...]

Basic Generalised Additive Model In Ecology; Exercise

July 5, 2018 | 0 Comments

Generalised Additive Models (GAM) are non-parametric models that add smoother to the data. On this exercise, we will look at GAMs using cubic spline using the mgcv package. Dataset used can be downloaded here. The dataset is the experiment result at grassland richness over time in Yellowstone National Park (Skkink ... [Read more...]

Non-Linear Model in R Exercises

June 21, 2018 | 0 Comments

A mechanistic model for the relationship between x and y sometimes needs parameter estimation. When model linearisation does not work,we need to use non-linear modelling. There are three main differences between non-linear and linear modelling in R: 1. specify the exact nature of the equation 2. replace the lm() with nls() ... [Read more...]

Polynomial Model in R – Study Case: Exercises

June 7, 2018 | 0 Comments

It is pretty rare to find something that represents linearity in the environmental system. The Y/X response may not be a straight line, but humped, asymptotic, sigmoidal or polynomial are possibly, truly non-linear. In this exercise, we will try to take a closer look at how polynomial regression works ... [Read more...]

Simple Spatial Modelling – Part 3: Exercises

May 23, 2018 | 0 Comments

So far, we have learned how to count spatial variability in our model. Please look at these two previous exercises here and here if you haven’t tried it yet. However, it only represents 1-Dimension model. On this exercise, we will try to expand our spatial consideration into 2-Dimension model. ...
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Simple Spatial Modeling – Part 2: Exercises

May 9, 2018 | 0 Comments

In the first exercise of simple spatial modeling here, we learned to create a model that considers more spatial variability. However, it relies on an isolated system where we set the q1 and q6 as zero. In this exercise, we try to bring the model into a more realistic space ... [Read more...]

Simple Spatial Modeling – Part 1: Exercises

April 26, 2018 | 0 Comments

This exercise is an extension of the last two previous exercises about numerical modeling. They can be found here and here. Those two previous exercises are representing how the model works in a lumped system. At this time, we will try to bring a space into our model. Make sure ...
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Simple Numerical Modeling in R – Part 2: Exercises

April 6, 2018 | 0 Comments

In this exercise, we will continue to build our model from our previous exercise here, specifically to revise the errors that may be generated from the model, including rounding and truncating errors. Answers to these exercises are available here. If you obtained a different (correct) answer than those listed on ...
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