How to Use the confint() Function in R

You can use the confint() function in R to calculate a confidence interval for one or more parameters in a fitted regression model.

This function uses the following basic syntax:

confint(object, parm, level=0.95)

where:

• object: Name of the fitted regression model
• parm: Parameters to calculate confidence interval for (default is all)
• level: Confidence level to use (default is 0.95)

The following example shows how to use this function in practice.

Example: How to Use confint() Function in R

Suppose we have the following data frame in R that shows the number of hours spent studying, number of practice exams taken, and final exam score for 10 students in some class:

#create data frame
df frame(score=c(77, 79, 84, 85, 88, 99, 95, 90, 92, 94),
                 hours=c(1, 1, 2, 3, 2, 4, 4, 2, 3, 3),
                 prac_exams=c(2, 3, 3, 2, 4, 5, 4, 3, 5, 4))

#view data frame
df

   score hours prac_exams
1     77     1          2
2     79     1          3
3     84     2          3
4     85     3          2
5     88     2          4
6     99     4          5
7     95     4          4
8     90     2          3
9     92     3          5
10    94     3          4

Now suppose we would like to fit the following multiple linear regression model in R:

Exam score = β₀ + β₁(hours) + β₂(practice exams)

We can use the lm() function to fit this model:

#fit multiple linear regression model
fit #view summary of model
summary(fit)

Call:
lm(formula = score ~ hours + prac_exams, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.4324 -1.2632 -0.8956  0.4316  5.1412 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  68.4029     2.8723  23.815 5.85e-08 ***
hours         4.1912     0.9961   4.207   0.0040 ** 
prac_exams    2.6912     0.9961   2.702   0.0306 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 2.535 on 7 degrees of freedom
Multiple R-squared:  0.9005,	Adjusted R-squared:  0.8721 
F-statistic: 31.68 on 2 and 7 DF,  p-value: 0.0003107

Notice that the model summary displays the fitted regression coefficients:

• Intercept = 68.4029
• hours = 4.1912
• prac_exams = 2.6912

To obtain a 95% confidence interval for each of these coefficients, we can use the confint() function:

#calculate 95% confidence interval for each coefficient in model
confint(fit)

                 2.5 %    97.5 %
(Intercept) 61.6111102 75.194772
hours        1.8357237  6.546629
prac_exams   0.3357237  5.046629

The 95% confidence interval for each parameter is shown:

• 95% C.I. for Intercept = [61.61, 75.19]
• 95% C.I. for hours = [1.84, 6.55]
• 95% C.I. for prac_exams = [0.34, 5.05]

To instead calculate a 99% confidence interval, simply change the value for the level argument:

#calculate 99% confidence interval for each coefficient in model
confint(fit, level=0.99)

                 0.5 %    99.5 %
(Intercept) 58.3514926 78.454390
hours        0.7052664  7.677087
prac_exams  -0.7947336  6.177087

And to only calculate a confidence interval for a specific parameter, simply specify the coefficient using the parm argument:

#calculate 99% confidence interval for hours
confint(fit, parm='hours', level=0.99)

          0.5 %   99.5 %
hours 0.7052664 7.677087

Notice that the 99% confidence interval is shown for the hours variable only.

Additional Resources

The following tutorials provide additional information about linear regression in R:

How to Interpret Regression Output in R
How to Perform Simple Linear Regression in R
How to Perform Multiple Linear Regression in R
How to Perform Logistic Regression in R