by David Lillis, Ph.D. In our last post, we calculated Pearson and Spearman correlation coefficients in R and got a surprising result. So let’s ... Continue Reading
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Blog Posts
Generalized Linear Models in R, Part 7: Checking for Overdispersion in Count Regression
by David Lillis, Ph.D. In my last blog post we fitted a generalized linear model to count data using a Poisson error structure. We found, however, ... Continue Reading
Generalized Linear Models in R, Part 6: Poisson Regression for Count Variables
by David Lillis, Ph.D. In my last couple of articles (Part 4, Part 5), I demonstrated a logistic regression model with binomial errors on binary ... Continue Reading
Generalized Linear Models in R, Part 5: Graphs for Logistic Regression
by David Lillis, Ph.D. In my last post I used the glm() command in R to fit a logistic model with binomial errors to investigate the relationships ... Continue Reading
Linear Models in R: Plotting Regression Lines
by David Lillis, Ph.D. Today let’s re-create two variables and see how to plot them and include a regression line. We take height to be a variable ... Continue Reading
R Graphics: Plotting in Color with qplot Part 2
by David Lillis, Ph.D. In the last lesson, we saw how to use qplot to map symbol colour to a categorical variable. Now we see how to control symbol ... Continue Reading
R Graphics: Plotting in Color with qplot
by David Lillis, Ph.D. In this lesson, let’s see how to use qplot to map symbol colour to a categorical variable. Copy in the following data set ... Continue Reading
Doing Scatterplots in R
by David Lillis, Ph.D. In this lesson, we see how to use qplot to create a simple scatterplot. The qplot (quick plot) system is a subset of the ... Continue Reading
Graphing Non-Linear Mathematical Expressions in R
by David Lillis, Ph.D. In Part 20 of this series, let’s see how to create mathematical expressions for your graph in R. We'll use an example of ... Continue Reading
Generalized Linear Models in R, Part 3: Plotting Predicted Probabilities
by David Lillis, Ph.D. In our last article, we learned about model fit in Generalized Linear Models on binary data using the glm() command. We ... Continue Reading
R Is Not So Hard! A Tutorial, Part 13: Box Plots
by David Lillis, Ph.D. In Part 13, let’s see how to create box plots in R. Let’s create a simple box plot using the boxplot() command, which is easy ... Continue Reading
R is Not So Hard! A Tutorial, Part 12: Creating Histograms & Setting Bin Widths
by David Lillis, Ph.D. I'm sure you've heard that R creates beautiful graphics. It's true, and it doesn't have to be hard to do so. Let’s ... Continue Reading
R Is Not So Hard! A Tutorial, Part 11: Creating Bar Charts
by David Lillis, Ph.D. Let’s create a simple bar chart in R using the barplot() command, which is easy to use. First, we set up a vector of ... Continue Reading
R Is Not So Hard! A Tutorial, Part 7: More Plotting in R
by David Lillis, Ph.D. In Part 7, let’s look at further plotting in R. Try entering the following three commands together (the semi-colon allows you ... Continue Reading
R Is Not So Hard! A Tutorial, Part 6: Basic Plotting in R
by David Lillis, Ph.D. In Part 6, let’s look at basic plotting in R. Try entering the following three commands together (the semi-colon allows ... Continue Reading
R Is Not So Hard! A Tutorial, Part 5: Fitting an Exponential Model
by David Lillis, Ph.D. In Part 3 and Part 4 we used the lm() command to perform least squares regressions. We saw how to check for non-linearity in ... Continue Reading
R Is Not So Hard! A Tutorial, Part 4: Fitting a Quadratic Model
by David Lillis, Ph.D. In Part 3 we used the lm() command to perform least squares regressions. In Part 4 we will look at more advanced aspects of ... Continue Reading
R Is Not So Hard! A Tutorial, Part 3: Regressions and Plots
by David Lillis, Ph.D. In Part 2 of this series, we created two variables and used the lm() command to perform a least squares regression on them, ... Continue Reading
R is Not So Hard! A Tutorial, Part 2: Variable Creation
by David Lillis, Ph.D. In Part 1 we installed R and used it to create a variable and summarize it using a few simple commands. Today let’s re-create ... Continue Reading
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