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
# load dataframedf <- read.csv("Sample4.csv")# create logistic regression modellogistic_model <- glm(var1 ~ var2, data=df, family=binomial)#Data frame with hp in ascending orderPredicted_data <- data.frame(var2=seq( min(df$var2), max(df$var2),len=500))# Fill predicted values using regression modelPredicted_data$var1 = predict( logistic_model, Predicted_data, type="response")# Plot Predicted data and original data pointsplot(var1 ~ var2, data=df)lines(var1 ~ var2, Predicted_data, lwd=2, col="green") |
Output:
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