Visualize Best fit curve with data frame
Now since from the above summary, we know the linear model of fourth-degree fits the curve best with an adjusted r squared value of 0.955868. So, we will visualize the fourth-degree linear model with the scatter plot and that is the best fitting curve for the data frame.
Example:
R
# create sample data sample_data <- data.frame(x=1:10, y=c(25, 22, 13, 10, 5, 9, 12, 16, 34, 44)) # Create best linear model best_model <- lm(y~poly(x,4,raw=TRUE), data=sample_data) # create a basic scatterplot plot(sample_data$x, sample_data$y) # define x-axis values x_axis <- seq(1, 10, length=10) # plot best model lines(x_axis, predict(best_model, data.frame(x=x_axis)), col='green') |
Output:
Curve Fitting in R
In this article, we will discuss how to fit a curve to a dataframe in the R Programming language.
Curve fitting is one of the basic functions of statistical analysis. It helps us in determining the trends and data and helps us in the prediction of unknown data based on a regression model/function.
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