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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:

1) Introducing Example Data
2) Example: Running Multiple Linear Regression Models in for-Loop
3) Video, Further Resources & Summary

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

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

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)]))
 
}

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

mod_summaries                            # Return summaries of all models

 

multiple linear regressions executed in a for-loop in r

 

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 having a look at the following video that I have published on my YouTube channel.

In the video, I’m explaining how top loop and repeat the estimation of multiple regression models using 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: 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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32 Comments. Leave new

  • Fernando Ramires
    March 1, 2021 1:46 pm

    the procedure can follow the stepwise line….

    Reply
  • Farieda Hassanen
    July 24, 2021 5:22 pm

    Very good

    Reply
  • Anu
    August 7, 2021 8:07 am

    Thanks for the excellent explanation. If you can provide steps on how to use PCA (principal component analysis) on series of images, that would be great!

    Reply
  • Helen
    August 28, 2021 9:14 pm

    How can I modify this to have models for each variable by itself? For example I want the the second model to just have x2 and the third model to just have x3 so on, rather than them building onto each other. Also could I change this to do and OddsRatio with each predictor?

    Reply
  • Hussein Ali
    October 25, 2021 8:22 pm

    Hi Mr Joachim
    I need your help because till now no one answered my question and I hope to find the solution with you,

    How can find the range (minimum, maximum) values for Intercept, slope and R-square in r programming?

    Best Regards
    Hussein Ali

    Reply
  • Simon von Zahn
    October 28, 2021 7:31 pm

    Hello Joachim,
    will this example provide me knowing which of the tested models to use for a maximised adj. R squared?
    And which packages are required to run it?
    Best Regards
    Simon

    Reply
  • Alexandre
    April 14, 2022 8:42 pm

    Hi Joachim,

    I’m trying to use this method to run regressions on “n” independent variables, so up to every combination available from my columns. Is there a way I can do this with this code? Thanks for your help!

    Reply
  • Simone
    May 10, 2022 2:14 pm

    Hi Joachim,
    thank you for sharing this guide. I run several linear regression models with it. How can I extract all p-values and estimates of the list of models I created? I would like to store them in one dataframe.

    Reply
  • Foyez
    September 2, 2022 11:32 am

    How can we extract the predicted value from this linear models that stored in the list

    Reply
  • Sabrina
    September 14, 2022 2:12 pm

    Hii Joachim,

    Thanks for the nice example. How can I expand this code to extract the coeff, SD and P-value and put the in a separate list?

    Reply
  • Sal
    September 24, 2022 2:26 pm

    Hey Joachim,
    Can I modify the code and use for multiple ‘target variables’ instead of multiple ‘predictors’? If so, could you examplify the code.
    Thanks

    Reply
    • Joachim
      September 30, 2022 5:06 pm

      Hey Sal,

      Yes, this is possible. Please have a look at the modified example code below:

      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(y1 = y,
                   y2 = y + rnorm(1000),
                   y3 = y + 10 * rnorm(1000),
                   x1, x2, x3)
head(data)                               # Head of data
#           y1          y2         y3         x1         x2          x3
# 1  0.5587036  1.53788165  0.1669674 -0.3779533 -0.5320515 -0.92069263
# 2  0.8422515  0.08528937  0.6847646 -1.3835572  1.2782521  0.87967960
# 3 -0.5395343 -0.98086394 -1.2222470 -0.9729798 -0.1515273 -0.05973894
# 4 -0.3522260  0.30012623 -1.7663313  1.2977564 -0.3512013 -0.77239810
# 5  1.5848675 -0.48901240 -1.3411279 -1.3152806 -2.3644414 -1.14651812
# 6  0.2207957  0.11100091 -0.2485206  1.8860636  0.1967851 -0.04963894
 
mod_summaries <- list()                  # Create empty list
 
out_names <- c("y1", "y2", "y3")         # Names of all target variables
 
for(i in 1:length(out_names)) {          # Estimate models
 
  mod_summaries[[i]] <- summary(lm(get(out_names[i]) ~ x1 + x2 + x3, data))
}
 
mod_summaries                            # Return summaries of all models
# [[1]]
# 
# Call:
# lm(formula = get(out_names[i]) ~ x1 + x2 + x3, data = data)
# 
# Residuals:
#      Min       1Q   Median       3Q      Max 
# -2.67110 -0.65829 -0.03136  0.66088  3.15418 
# 
# Coefficients:
#              Estimate Std. Error t value Pr(>|t|)    
# (Intercept)  0.003017   0.030502   0.099    0.921    
# x1           0.131145   0.031817   4.122 4.07e-05 ***
# x2           0.173592   0.031297   5.547 3.73e-08 ***
# x3          -0.211426   0.029224  -7.235 9.31e-13 ***
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 0.9631 on 996 degrees of freedom
# Multiple R-squared:  0.09867,	Adjusted R-squared:  0.09596 
# F-statistic: 36.35 on 3 and 996 DF,  p-value: < 2.2e-16
# 
# 
# [[2]]
# 
# Call:
# lm(formula = get(out_names[i]) ~ x1 + x2 + x3, data = data)
# 
# Residuals:
#     Min      1Q  Median      3Q     Max 
# -4.3833 -0.9343  0.0150  0.8776  5.3319 
# 
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)    
# (Intercept) -0.03054    0.04400  -0.694  0.48784    
# x1           0.11453    0.04590   2.495  0.01274 *  
# x2           0.13429    0.04515   2.974  0.00301 ** 
# x3          -0.21587    0.04216  -5.121 3.66e-07 ***
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 1.389 on 996 degrees of freedom
# Multiple R-squared:  0.04237,	Adjusted R-squared:  0.03948 
# F-statistic: 14.69 on 3 and 996 DF,  p-value: 2.301e-09
# 
# 
# [[3]]
# 
# Call:
# lm(formula = get(out_names[i]) ~ x1 + x2 + x3, data = data)
# 
# Residuals:
#     Min      1Q  Median      3Q     Max 
# -34.264  -6.389  -0.109   6.519  34.821 
# 
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)   
# (Intercept) -0.44531    0.31494  -1.414  0.15769   
# x1           0.33912    0.32852   1.032  0.30220   
# x2           0.85905    0.32315   2.658  0.00798 **
# x3          -0.09715    0.30175  -0.322  0.74755   
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 9.944 on 996 degrees of freedom
# Multiple R-squared:  0.0105,	Adjusted R-squared:  0.007517 
# F-statistic: 3.522 on 3 and 996 DF,  p-value: 0.01465

      I hope this helps!

      Joachim

      Reply
  • Emily
    November 19, 2022 10:08 pm

    The last example is really useful for running multiple regressions in a for loop. What if I wanted to extract the regression coefficients for each i in out_names (Y1, Y2, Y3), and then transform those? For example, for each target variable (Y1, Y2, Y3) if I were interested in understanding the fraction of X1*x/Yi (such that X1*x/Yi +X2*x/Yi+X3*x/Yi = 1). Could I use a loop to extract the regression coefficient and store it so I can use it in a new function?

    Reply
  • Nate C
    January 16, 2023 4:29 pm

    Hi there, I adapted your code (thank you) for 1115 variables. Now I have a massive list with all of the summaries. Do you have recommendations on how I could easily sort through or extract only p-values or adjusted R squared values? Thanks!

    Reply
    • Cansu (Statistics Globe)
      January 17, 2023 9:49 am

      Hello Nate,

      Glad that we helped. To extract the adjusted Rsquares and the p-values only. You can use the following code.

      For extracting the adjusted R squares:

      mod_summaries_adj_rsqr <- list()                  # Create empty list
 
for(i in 2:ncol(data)) {                 # Head of for-loop
 
  predictors_i <- colnames(data)[2:i]    # Create vector of predictor names
  mod_summaries_adj_rsqr [[i - 1]] <- summary(     # Store regression model adj_rsqr summary in list
    lm(y ~ ., data[ , c("y", predictors_i)]))$adj.r.squared
 
}
 
mod_summaries_adj_rsqr                           # Return adj_rsqr summaries of all models
 
# [[1]]
# [1] 0.03510536
# 
# [[2]]
# [1] 0.04940452
# 
# [[3]]
# [1] 0.09595714

      For extracting the p-values:

      mod_summaries_pvalues <- list()                  # Create empty list
 
for(i in 2:ncol(data)) {                 # Head of for-loop
 
  predictors_i <- colnames(data)[2:i]    # Create vector of predictor names
  mod_summaries_pvalues[[i - 1]] <- summary(     # Store regression model p-values summary in list
    lm(y ~ ., data[ , c("y", predictors_i)]))$coefficients[,4]
 
}
 
mod_summaries_pvalues                            # Return  p-values summaries of all models
 
# [[1]]
# (Intercept)           x1 
# 8.008000e-01 1.416432e-09 
# 
# [[2]]
# (Intercept)           x1           x2 
# 7.899250e-01 1.364254e-06 6.759303e-05 
# 
# [[3]]
# (Intercept)           x1           x2           x3 
# 9.212350e-01 4.071976e-05 3.732345e-08 9.307473e-13

      Hope it helps!

      Regards,
      Cansu

      Reply
  • Fatme Ghaddar
    July 20, 2023 7:39 am

    Hello Joachim,

    Could you please help me to solve this coding problem:
    If I have 7 samples and I want to analyze the linear mixed model (using the LMER function) to get the variation of the antibodies logarithm according to 4 different runs.
    The general code should be as follows:
    Model<- lmer(log10(values)~sample + (1|Run) , data = Elisa, REML = F)

    Concerning the random effect formula (1|Run): I want to get the variances for the Run variable and also the variance of the residuals for each sample (=7), how can I do this according to your explanation above.
    Should I separate the sample variable into dummy variables? Or keep the sample variable as a single variable and use the loop function?
    I've tried adding the Sample variable to the random effects formula like this: ((Sample|Run), but it only gives me the variances of the run without the variances of the residuals for each sample.

    Thank you in advance for your help and cooperation.
    And if you can write me the adjustable code, I'd be grateful.

    Have a nice day.

    Reply

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Joachim Schork Statistician Programmer

I’m Joachim Schork. On this website, I provide statistics tutorials as well as code in Python and R programming.

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