Book Image

Learning Bayesian Models with R

By : Hari Manassery Koduvely
Book Image

Learning Bayesian Models with R

By: Hari Manassery Koduvely

Overview of this book

Table of Contents (16 chapters)
Learning Bayesian Models with R
About the Author
About the Reviewers


For the following exercises in this chapter, we use the Auto MPG dataset from the UCI Machine Learning repository (references 5 and 6 in the References section of this chapter). The dataset can be downloaded from The dataset contains the fuel consumption of cars in the US measured during 1970-1982. Along with consumption values, there are attribute variables, such as the number of cylinders, displacement, horse power, weight, acceleration, year, origin, and the name of the car:

  • Load the dataset into R using the read.table() function.

  • Produce a box plot of mpg values for each car name.

  • Write a function that will compute the scaled value (subtract the mean and divide by standard deviation) of a column whose name is given as an argument of the function.

  • Use the lapply() function to compute scaled values for all variables.

  • Produce a scatter plot of mgp versus acceleration for each car name using coplot(). Use legends to annotate the graph.