Remove Particular Predictors from GLM in R (Example Code)

This tutorial shows how to remove particular predictors from a GLM in R programming.

Creation of Example Data

data(iris)                                                         # Load example data
head(iris)
#   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1          5.1         3.5          1.4         0.2  setosa
# 2          4.9         3.0          1.4         0.2  setosa
# 3          4.7         3.2          1.3         0.2  setosa
# 4          4.6         3.1          1.5         0.2  setosa
# 5          5.0         3.6          1.4         0.2  setosa
# 6          5.4         3.9          1.7         0.4  setosa
model_a <- lm(Sepal.Length ~ ., iris)                              # Estimate linear regression model
summary(model_a)                                                   # Display summary of model
# Call:
# lm(formula = Sepal.Length ~ ., data = iris)
# 
# Residuals:
#      Min       1Q   Median       3Q      Max 
# -0.79424 -0.21874  0.00899  0.20255  0.73103 
# 
# Coefficients:
#                   Estimate Std. Error t value Pr(>|t|)    
# (Intercept)        2.17127    0.27979   7.760 1.43e-12 ***
# Sepal.Width        0.49589    0.08607   5.761 4.87e-08 ***
# Petal.Length       0.82924    0.06853  12.101  < 2e-16 ***
# Petal.Width       -0.31516    0.15120  -2.084  0.03889 *  
# Speciesversicolor -0.72356    0.24017  -3.013  0.00306 ** 
# Speciesvirginica  -1.02350    0.33373  -3.067  0.00258 ** 
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 0.3068 on 144 degrees of freedom
# Multiple R-squared:  0.8673,	Adjusted R-squared:  0.8627 
# F-statistic: 188.3 on 5 and 144 DF,  p-value: < 2.2e-16

Example: Remove Certain Predictor Variables from Linear Regression Model

model_b <- lm(Sepal.Length ~ . - Sepal.Width - Petal.Width, iris)  # Exclude predictors from lm()
summary(model_b)                                                   # Display summary of model
# Call:
# lm(formula = Sepal.Length ~ . - Sepal.Width - Petal.Width, data = iris)
# 
# Residuals:
#      Min       1Q   Median       3Q      Max 
# -0.75310 -0.23142 -0.00081  0.23085  1.03100 
# 
# Coefficients:
#                   Estimate Std. Error t value Pr(>|t|)    
# (Intercept)        3.68353    0.10610  34.719  < 2e-16 ***
# Petal.Length       0.90456    0.06479  13.962  < 2e-16 ***
# Speciesversicolor -1.60097    0.19347  -8.275 7.37e-14 ***
# Speciesvirginica  -2.11767    0.27346  -7.744 1.48e-12 ***
# ---
# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 0.338 on 146 degrees of freedom
# Multiple R-squared:  0.8367,	Adjusted R-squared:  0.8334 
# F-statistic: 249.4 on 3 and 146 DF,  p-value: < 2.2e-16

Further Resources

In addition, you might have a look at the related tutorials on this homepage.

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