# Run Multiple Regression Models in for-Loop in R (Example)

In this article, I’ll show how to **estimate multiple regression models in a for-loop** in the R programming language.

Table of contents:

If you want to know more about these topics, keep reading…

## Introducing Example Data

The following data is used as basement for this R programming tutorial:

set.seed(98274) # Creating example data y <- rnorm(1000) x1 <- rnorm(1000) + 0.2 * y x2 <- rnorm(1000) + 0.2 * x1 + 0.1 * y x3 <- rnorm(1000) - 0.1 * x1 + 0.3 * x2 - 0.3 * y data <- data.frame(y, x1, x2, x3) head(data) # Head of data # y x1 x2 x3 # 1 0.5587036 -0.3779533 -0.5320515 -0.92069263 # 2 0.8422515 -1.3835572 1.2782521 0.87967960 # 3 -0.5395343 -0.9729798 -0.1515273 -0.05973894 # 4 -0.3522260 1.2977564 -0.3512013 -0.77239810 # 5 1.5848675 -1.3152806 -2.3644414 -1.14651812 # 6 0.2207957 1.8860636 0.1967851 -0.04963894 |

set.seed(98274) # Creating example data y <- rnorm(1000) x1 <- rnorm(1000) + 0.2 * y x2 <- rnorm(1000) + 0.2 * x1 + 0.1 * y x3 <- rnorm(1000) - 0.1 * x1 + 0.3 * x2 - 0.3 * y data <- data.frame(y, x1, x2, x3) head(data) # Head of data # y x1 x2 x3 # 1 0.5587036 -0.3779533 -0.5320515 -0.92069263 # 2 0.8422515 -1.3835572 1.2782521 0.87967960 # 3 -0.5395343 -0.9729798 -0.1515273 -0.05973894 # 4 -0.3522260 1.2977564 -0.3512013 -0.77239810 # 5 1.5848675 -1.3152806 -2.3644414 -1.14651812 # 6 0.2207957 1.8860636 0.1967851 -0.04963894

As you can see based on the previous RStudio console output, our example data consists of four numeric columns. The first variable is our regression outcome and the three other variables are our predictors.

## Example: Running Multiple Linear Regression Models in for-Loop

In this Example, I’ll show how to run three regression models within a for-loop in R. In each for-loop iteration, we are increasing the complexity of our model by adding another predictor variable to the model.

First, we have to create a list in which we will store the outputs of our for-loop iterations:

mod_summaries <- list() # Create empty list |

mod_summaries <- list() # Create empty list

Now, we can write a for-loop that runs multiple linear regression models as shown below:

for(i in 2:ncol(data)) { # Head of for-loop predictors_i <- colnames(data)[2:i] # Create vector of predictor names mod_summaries[[i - 1]] <- summary( # Store regression model summary in list lm(y ~ ., data[ , c("y", predictors_i)])) } |

for(i in 2:ncol(data)) { # Head of for-loop predictors_i <- colnames(data)[2:i] # Create vector of predictor names mod_summaries[[i - 1]] <- summary( # Store regression model summary in list lm(y ~ ., data[ , c("y", predictors_i)])) }

Let’s have a look at the output of our previously executed for-loop:

`mod_summaries # Return summaries of all models` |

mod_summaries # Return summaries of all models

As you can see in Figure 1, we have created a list containing three different summary statistics of three different linear regressions.

## Video, Further Resources & Summary

If you need further explanations on the content of this tutorial, I can recommend to have a look at the following video that I have published on my YouTube channel. In the video, I’m explaining the R programming syntax of this tutorial in RStudio.

*The YouTube video will be added soon.*

In addition, you may want to read the other R programming tutorials of my homepage.

- summary Function in R
- Extract Regression Coefficients of Linear Model
- for-Loop in R
- Loops in R
- The R Programming Language

Summary: At this point you should know how to **write a for-loop executing several linear regressions** in R programming. Please let me know in the comments below, in case you have further questions. Furthermore, please subscribe to my email newsletter to receive updates on new articles.

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