Combining the length() and which() commands gives a handy method of counting elements that meet particular criteria.
b <- c(7, 2, 4, 3, -1, -2, 3, 3, 6, 8, 12, 7, 3)
b
Let’s count the 3s in the vector b. (more…)
Statistical Software
R Is Not So Hard! A Tutorial, Part 15: Counting Elements in a Data Set
August 13th, 2014 by David Lillis
Random Sample from a Uniform Distribution in R Commander
July 23rd, 2014 by Karen Grace-Martin
Why We Needed a Random Sample of 6 numbers between 1 and 10000
As you may have read in one of our recent newsletters, this month The Analysis Factor hit two milestones:
- 10,000 subscribers to our mailing list
- 6 years in business.
We’re quite happy about both, and seriously grateful to all members of our community.
So to celebrate and to say thanks, we decided to do a giveaway to 6 randomly-chosen newsletter subscribers.
I just sent emails to the 6 winners this morning.
How We Randomly Generated 6 Equally Likely Values out of 10000 Using R Commander (more…)
Generalized Linear Models in R, Part 3: Plotting Predicted Probabilities
July 2nd, 2014 by David Lillis
In our last article, we learned about model fit in Generalized Linear Models on binary data using the glm() command. We continue with the same glm on the mtcars data set (regressing the vs variable on the weight and engine displacement).
Now we want to plot our model, along with the observed data.
Although we ran a model with multiple predictors, it can help interpretation to plot the predicted probability that vs=1 against each predictor separately. So first we fit a glm for only (more…)
Generalized Linear Models in R, Part 2: Understanding Model Fit in Logistic Regression Output
June 24th, 2014 by David Lillis
In the last article, we saw how to create a simple Generalized Linear Model on binary data using the glm() command. We continue with the same glm on the mtcars data set (more…)
Generalized Linear Models in R, Part 1: Calculating Predicted Probability in Binary Logistic Regression
June 18th, 2014 by David Lillis
Ordinary Least Squares regression provides linear models of continuous variables. However, much data of interest to statisticians and researchers are not continuous and so other methods must be used to create useful predictive models.
The glm() command is designed to perform generalized linear models (regressions) on binary outcome data, count data, probability data, proportion data and many other data types.
In this blog post, we explore the use of R’s glm() command on one such data type. Let’s take a look at a simple example where we model binary data.
SPSS Procedures for Logistic Regression
May 15th, 2014 by Karen Grace-Martin
Need to run a logistic regression in SPSS? Turns out, SPSS has a number of procedures for running different types of logistic regression.
Some types of logistic regression can be run in more than one procedure. For some unknown reason, some procedures produce output others don’t. So it’s helpful to be able to use more than one.
Logistic Regression
Logistic Regression can be used only for binary dependent (more…)