How to Plot a Logistic Regression Curve in R?
In this article, we will learn how to plot a Logistic Regression Curve in the R programming Language.
Logistic regression is basically a supervised classification algorithm. That helps us in creating a differentiating curve that separates two classes of variables. To Plot the Logistic Regression curve in the R Language, we use the following methods.
Dataset used: Sample4
Method 1: Using Base R methods
To plot the logistic regression curve in base R, we first fit the variables in a logistic regression model by using the glm() function. The glm() function is used to fit generalized linear models, specified by giving a symbolic description of the linear predictor. Then we use that model to create a data frame where the y-axis variable is changed to its predicted value derived by using the predict() function with the above-created model. Then we plot a scatter plot of original points by using the plot() function and predicted values by using the lines() function.
Syntax:
logistic_model <- glm( formula, family, dataframe )
plot( original_dataframe )
lines( predicted_dataframe )
Parameter:
- formula: determines the symbolic description of the model to be fitted.
- family: determines the description of the error distribution and link function to be used in the model.
- dataframe: determines the data frame to be used for fitting purpose
Example: Plot logistic regression
R
df <- read.csv ( "Sample4.csv" )
logistic_model <- glm (var1 ~ var2, data=df, family=binomial)
Predicted_data <- data.frame (var2= seq (
min (df$var2), max (df$var2),len=500))
Predicted_data$var1 = predict (
logistic_model, Predicted_data, type= "response" )
plot (var1 ~ var2, data=df)
lines (var1 ~ var2, Predicted_data, lwd=2, col= "green" )
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Output:
Method 2: Using ggplot2 package
To plot the logistic curve using the ggplot2 package library, we use the stat_smooth() function. The argument method of function with the value “glm” plots the logistic regression curve on top of a ggplot2 plot. So, we first plot the desired scatter plot of original data points and then overlap it with a regression curve using the stat_smooth() function.
Syntax:
plot + stat_smooth( method=”glm”, se, method.args )
Parameter:
- se: determines a boolean that tells whether to display confidence interval around smooth.
- method.args: determines the method function for logistic curve.
Example: Plot logistic regression
R
library (ggplot2)
df <- read.csv ( "Sample4.csv" )
ggplot (df, aes (x=var2, y=var1)) + geom_point () +
stat_smooth (method= "glm" , color= "green" , se= FALSE ,
method.args = list (family=binomial))
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Output:
Last Updated :
27 Jan, 2022
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