Learn how to practically implement GLMs and mixed effect models and assess these models to answer research questions.
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Now that you have a foundational understanding of generalized linear models (GLMs) and mixed effect models, a critical skill is knowing when and how to practically implement and assess these models to answer research questions. In this course, you will apply these modeling techniques to an ecological data set containing information about dragonfly habitat and behaviour. Here, you'll find that some questions can be answered using GLMs, while other questions require insight that can be gained using random intercept or random intercept and slope models. At each step, you will learn how to determine if your model is appropriate using model diagnostics, use the estimated parameters to make predictions about dragonfly abundance and behavior, visualize the relationships described by the models, and learn how to address problems that arise when the model you have built is not the right one for the job!
Let's get started! In this chapter you'll learn what data exploration steps should be taken before you even start modeling. You'll apply and visualize predictions from both Gaussian and Poisson Generalized Linear Models (GLMs) in R and run diagnostic tests to determine if the models are appropriate for answering the research question being addressed.
In this chapter, you'll move into the world of Mixed Effect Models, leaning how to practically apply a random intercept model to explore both fixed (population level) and random (within-site) effects. You'll extract key values from model output, and also learn how model fit methods influence the type of model comparisons that can be made during the model selection process.
Increase the complexity of your GLM by adding factors and offsets and examine the impact of this complexity on model diagnostics. You'll also learn how to apply Negative Binomial GLMs and the process and techniques of model selection. Being able to defend your chosen model is key!
Let's extend our Mixed Effect Model to the random intercept and slope model! You'll learn why and how these models can be used and how to interact with the model output to extract key values and generate visualizations. This course wraps up with an overview of how modeling can be used as a tool and what pieces of information are essential to include in model reporting.
Let's get started! In this chapter you'll learn what data exploration steps should be taken before you even start modeling. You'll apply and visualize predictions from both Gaussian and Poisson Generalized Linear Models (GLMs) in R and run diagnostic tests to determine if the models are appropriate for answering the research question being addressed.
Increase the complexity of your GLM by adding factors and offsets and examine the impact of this complexity on model diagnostics. You'll also learn how to apply Negative Binomial GLMs and the process and techniques of model selection. Being able to defend your chosen model is key!
In this chapter, you'll move into the world of Mixed Effect Models, leaning how to practically apply a random intercept model to explore both fixed (population level) and random (within-site) effects. You'll extract key values from model output, and also learn how model fit methods influence the type of model comparisons that can be made during the model selection process.
Let's extend our Mixed Effect Model to the random intercept and slope model! You'll learn why and how these models can be used and how to interact with the model output to extract key values and generate visualizations. This course wraps up with an overview of how modeling can be used as a tool and what pieces of information are essential to include in model reporting.
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