Extract Equation of Linear Regression Line in R (Example Code)

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

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

mod <- lm(Sepal.Length ~ ., iris)    # Estimate regression model using lm()
summary(mod)                         # Print descriptive statistics
# 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

mod <- lm(Sepal.Length ~ ., iris) # Estimate regression model using lm() summary(mod) # Print descriptive statistics # 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

mod_eq <- paste("Sepal.Length =",    # Specify equation of line
                round(coef(mod)[[1]], 2),
                "+",
                paste(round(as.numeric(coef(mod)[2:length(coef(mod))]), 2),
                      "*",
                      names(coef(mod)[2:length(coef(mod))]),
                      "+",
                      collapse = " "))
mod_eq <- substr(mod_eq, 1, nchar(mod_eq) - 2)
mod_eq <- gsub("\+ -", "- ", mod_eq)
mod_eq                               # Print equation of line
# [1] "Sepal.Length = 2.17 + 0.5 * Sepal.Width + 0.83 * Petal.Length - 0.32 * Petal.Width - 0.72 * Speciesversicolor - 1.02 * Speciesvirginica"

mod_eq <- paste("Sepal.Length =", # Specify equation of line round(coef(mod)[[1]], 2), "+", paste(round(as.numeric(coef(mod)[2:length(coef(mod))]), 2), "*", names(coef(mod)[2:length(coef(mod))]), "+", collapse = " ")) mod_eq <- substr(mod_eq, 1, nchar(mod_eq) - 2) mod_eq <- gsub("\+ -", "- ", mod_eq) mod_eq # Print equation of line # [1] "Sepal.Length = 2.17 + 0.5 * Sepal.Width + 0.83 * Petal.Length - 0.32 * Petal.Width - 0.72 * Speciesversicolor - 1.02 * Speciesvirginica"