Logistic regression, also known as logit regression or logit model, is a probabilistic linear model for dichotomous data. The response variable is a binary variable (nominal variable), which means the variable has two categories or two values; True vs. False, $1$ vs. $0$ or success vs. failure, with probabilities of $\pi$ and $1-\pi$, respectively. Thus, the response variable follows a binomial distribution written as

$$y \sim B(\eta,\pi)\text{,}$$

where $\eta$ is the binomial denominator, which for a binary variable is $0$ or $1$, and $\pi$ is the probability of success.

---

The following functions and R-packages are used in this section (in alphabetical order):

• ggplot2
• leaflet
• readxl

• coefficients()
• confint.default()
• download.file()
• exp()
• file.remove()
• getwd()
• glm()
• ifelse()
• paste0()
• predict()
• readxl::read.excel()
• seq()
• summary()
• str()
• table()

• abline()
• ggplot2::ggplot()
• grid()
• leaflet::leaflet()
• leaflet::addCircleMarkers()
• leaflet::addLegend()
• leaflet::addProvierTiles()
• leaflet::addTiles()
• lines()
• plot()
• polygon()

---

Citation

The E-Learning project SOGA-R was developed at the Department of Earth Sciences by Kai Hartmann, Joachim Krois and Annette Rudolph. You can reach us via mail by soga[at]zedat.fu-berlin.de.

You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: Hartmann, K., Krois, J., Rudolph, A. (2023): Statistics and Geodata Analysis using R (SOGA-R). Department of Earth Sciences, Freie Universitaet Berlin.