In this post, I will show how one can easily construct confidence intervals in R. Assume you have a vector of numbers and you want to construct a confidence interval around the mean of this vector. The subsequent R code shows one easy way to calculate the confidence interval around the mean of this vector. The following code loads a function that allows you to pass on the vector and returns the confidence intervals. Per default the function returns the 95% confidence interval. However, the parameter ‘conf_level’ allows you to specify the interval you want.
#start with an empty workspace rm(list=ls()) #set seed set.seed(2) # load necessary packages for demo library(RCurl) # import the function from repository url_robust <- "https://raw.githubusercontent.com/IsidoreBeautrelet/economictheoryblog/master/confidence_intervals.R" eval(parse(text = getURL(url_robust, ssl.verifypeer = FALSE)), envir=.GlobalEnv) #generate a vector of random numbers vector <- rnorm(100) #calculate 95% confidence intervals conf(vector)  -0.2580911 0.1966948 #calculate 90% confidence intervals conf(vector,conf_level = 0.90)  -0.2215323 0.1601360
Furthermore, if you do not have many observations, you may want to use Student’s t-distribution instead of the Normal distribution. The Student’s t-distribution has wider tales when the number of observations is low and gives a you more conservative estimates of your confidence interval. In case you want to use Student’s t-distribution you case set the parameter ‘distribution’, i.e. distribution=”normal”.
In case you do not know whether to use the Normal Distribution or the Student’s t-distribution , you might want to check out this post. In this post, I try illustrate the difference between using the Normal and the t-distribution.