The **lm()** function in R can be used to fit linear regression models.

Once we’ve fit a model, we can then use the **predict()** function to predict the response value of a new observation.

This function uses the following syntax:

**predict(object, newdata, type=”response”)**

where:

**object:**The name of the model fit using the glm() function**newdata:**The name of the new data frame to make predictions for**type:**The type of prediction to make.

The following example shows how to use the **lm()** function to fit a linear regression model in R and then how to use the **predict()** function to predict the response value of a new observation the model hasn’t seen before.

**Example: Using the predict() Function with lm() in R**

Suppose we have the following data frame in R that contains information about various basketball players:

#create data frame df <- data.frame(minutes=c(5, 10, 13, 14, 20, 22, 26, 34, 38, 40), fouls=c(5, 5, 3, 4, 2, 1, 3, 2, 1, 1), points=c(6, 8, 8, 7, 14, 10, 22, 24, 28, 30)) #view data frame df minutes fouls points 1 5 5 6 2 10 5 8 3 13 3 8 4 14 4 7 5 20 2 14 6 22 1 10 7 26 3 22 8 34 2 24 9 38 1 28 10 40 1 30

Suppose we would like to fit the following multiple linear regression model using minutes played and total fouls to predict the number of points scored by each player:

**points = β _{0} + β_{1}(minutes) + β_{2}(fouls)**

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

#fit multiple linear regression model fit <- lm(points ~ minutes + fouls, data=df) #view summary of model summary(fit) Call: lm(formula = points ~ minutes + fouls, data = df) Residuals: Min 1Q Median 3Q Max -3.5241 -1.4782 0.5918 1.6073 2.0889 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -11.8949 4.5375 -2.621 0.0343 * minutes 0.9774 0.1086 9.000 4.26e-05 *** fouls 2.1838 0.8398 2.600 0.0354 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 2.148 on 7 degrees of freedom Multiple R-squared: 0.959, Adjusted R-squared: 0.9473 F-statistic: 81.93 on 2 and 7 DF, p-value: 1.392e-05

Using the coefficients from the model output, we can write the fitted regression equation:

**points = -11.8949 + 0.9774(minutes) + 2.1838(fouls)**

We can then use the **predict()** function to predict the number of points that a player will score who plays for 15 minutes and has 3 total fouls:

#define new observation newdata = data.frame(minutes=15, fouls=3) #use model to predict points value predict(fit, newdata) 1 9.317731

The model predicts that this player will score **9.317731** points.

Note that we can also make several predictions at once if we have a data frame that has multiple new observations.

For example, the following code shows how to use the fitted regression model to predict the points values for three players:

#define new data frame of three cars newdata = data.frame(minutes=c(15, 20, 25), fouls=c(3, 2, 1)) #view data frame newdata minutes fouls 1 15 3 2 20 2 3 25 1 #use model to predict points for all three players predict(model, newdata) 1 2 3 9.317731 12.021032 14.724334

Here’s how to interpret the output:

- The predicted points for the player with 15 minutes and 3 fouls is
**9.32**. - The predicted points for the player with 20 minutes and 2 fouls is
**12.02**. - The predicted points for the player with 25 minutes and 1 foul is
**14.72**.

**Notes on Using predict()**

The names of the columns in the new data frame should exactly match the names of the columns in the data frame that were used to build the model.

Notice that in our previous example, the data frame we used to build the model contained the following column names for our predictor variables:

**minutes****fouls**

Thus, when we created the new data frame called **newdata** we made sure to also name the columns:

**minutes****fouls**

If the names of the columns do not match, you’ll receive the following error message:

Error in eval(predvars, data, env)

Keep this in mind when using the **predict()** function.

**Additional Resources**

The following tutorials explain how to perform other common tasks in R:

How to Perform Simple Linear Regression in R

How to Perform Multiple Linear Regression in R

How to Perform Polynomial Regression in R

How to Create a Prediction Interval in R