Table of Contents
R is a programming language commonly used for statistical analysis and data visualization. It offers a variety of functions and packages that allow users to plot confidence intervals, which are a range of values that are likely to contain the true population parameter with a certain level of confidence. To plot a confidence interval in R, one can use the built-in functions such as t.test() or confint(), or install additional packages such as ggplot2 or plotrix. These functions allow users to specify the data, confidence level, and other necessary parameters to generate a graph that visually represents the confidence interval. By utilizing R’s powerful abilities to perform statistical calculations and generate high-quality graphics, users can effectively communicate the uncertainty in their data and make informed decisions based on the results of their analysis.
Plot a Confidence Interval in R
A confidence interval is a range of values that is likely to contain a population parameter with a certain level of confidence.
This tutorial explains how to plot a confidence interval for a dataset in R.
Example: Plotting a Confidence Interval in R
Suppose we have the following dataset in R with 100 rows and 2 columns:
#make this example reproducible set.seed(0) #create dataset x <- rnorm(100) y <- x*2 + rnorm(100) df <- data.frame(x = x, y = y) #view first six rows of dataset head(df) x y 1 1.2629543 3.3077678 2 -0.3262334 -1.4292433 3 1.3297993 2.0436086 4 1.2724293 2.5914389 5 0.4146414 -0.3011029 6 -1.5399500 -2.5031813
To create a plot of the relationship between x and y, we can first fit a linear regression model:
model <- lm(y ~ x, data = df)
Next, we can create a plot of the estimated linear regression line using the abline() function and the lines() function to create the actual confidence bands:
#get predicted y values using regression equation newx <- seq(min(df$x), max(df$x), length.out=100) preds <- predict(model, newdata = data.frame(x=newx), interval = 'confidence') #create plot of x vs. y, but don't display individual points (type='n') plot(y ~ x, data = df, type = 'n') #add fitted regression line abline(model) #add dashed lines for confidence bands lines(newx, preds[ ,3], lty = 'dashed', col = 'blue') lines(newx, preds[ ,2], lty = 'dashed', col = 'blue')
The black line displays the fitted linear regression line while the two dashed blue lines display the confidence intervals.
If you’d like, you can also fill in the area between the confidence interval lines and the estimated linear regression line using the following code:
#create plot of x vs. y plot(y ~ x, data = df, type = 'n') #fill in area between regression line and confidence interval polygon(c(rev(newx), newx), c(rev(preds[ ,3]), preds[ ,2]), col = 'grey', border = NA) #add fitted regression line abline(model) #add dashed lines for confidence bands lines(newx, preds[ ,3], lty = 'dashed', col = 'blue') lines(newx, preds[ ,2], lty = 'dashed', col = 'blue')
Here’s the complete code from start to finish:
#make this example reproducible set.seed(0) #create dataset x <- rnorm(100)y <- x*2 + rnorm(100)df <- data.frame(x = x, y = y) #fit linear regression model model <- lm(y ~ x, data = df) #get predicted y values using regression equation newx <- seq(min(df$x), max(df$x), length.out=100)preds <- predict(model, newdata = data.frame(x=newx), interval = 'confidence') #create plot of x vs. y plot(y ~ x, data = df, type = 'n') #fill in area between regression line and confidence interval polygon(c(rev(newx), newx), c(rev(preds[ ,3]), preds[ ,2]), col = 'grey', border = NA) #add fitted regression line abline(model) #add dashed lines for confidence bands lines(newx, preds[ ,3], lty = 'dashed', col = 'blue') lines(newx, preds[ ,2], lty = 'dashed', col = 'blue')
Additional Resources
What are Confidence Intervals?
How to Use the abline() Function in R to Add Straight Lines to Plots