The **predict() function in R** is used to predict the values based on the input data. All the modeling aspects in the R program will make use of the predict() function in its own way, but note that the functionality of the predict() function remains the same irrespective of the case.

## Syntax of predict() function in R

**predict():** The predict() function in R is used to predict the values based on the input data.

```
predict(object,newdata,interval)
```

**Where**:

**Object =**The class inheriting from the “lm”**newdata =**Input data to predict the values**Interval =**Type of interval calculation

## An example of the predict() function

We need data to predict the values. Hence, we are going to import the data from the inbuilt dataset in R – “Cars”.

The simple reason for this is, it is a simple dataset having only 2 attributes in it.

```
df<-datasets::cars
```

```
speed dist
1 4 2
2 4 10
3 7 4
4 7 22
5 8 16
6 9 10
7 10 18
8 10 26
9 10 34
10 11 17
```

We have two columns such as ‘speed’ and ‘dist’. We have the first 10 values in it. All we need to do is to predict future values using this data. Interesting right? Let’s roll!

First, we need to compute a linear model for this data frame. Executing the below code will fetch you the linear model results.

```
#Creates a linear model
my_linear_model <- lm(dist~speed,data = df)
#Prints the model results
my_linear_model
```

```
Call:
lm(formula = dist ~ speed, data = df)
Coefficients:
(Intercept) speed
-17.579 3.932
```

The linear model has returned the speed of the cars as per our input data behavior. Now, we got the model and everything is ready to get values predicted.

**Let’s see how it works!**

```
#Creating a data frame
variable_speed<-data.frame(speed=c(11,11,12,12,12,12,13,13,13,13))
#fiting the linear model
liner_model<-lm(dist~speed,data = df)
#predicts the future values
predict(liner_model,newdata = variable_speed)
```

```
1 2 3 4 5
25.67740 25.67740 29.60981 29.60981 29.60981
```

```
6 7 8 9 10
29.60981 33.54222 33.54222 33.54222 33.54222
```

Well, we have successfully predicted the future distance values based on the previous data and with the help of the linear model.

Now, we have to check the **“confidence”** level in our predicted values to see how accurate is our prediction.

## Confidence in the Predicted Values

The confidence interval in the predict function will help us to gauge the uncertainty in the predictions.

```
#Input data
variable_speed <-data.frame(speed=c(11,11,12,12,12,12,13,13,13,13))
#Fits the model
liner_model<-lm(dist~speed,data = df)
#Predits the values with confidence interval
predict(liner_model,newdata = variable_speed,interval = 'confidence')
```

```
fit lwr upr
1 25.67740 19.96453 31.39028
2 25.67740 19.96453 31.39028
3 29.60981 24.39514 34.82448
4 29.60981 24.39514 34.82448
5 29.60981 24.39514 34.82448
6 29.60981 24.39514 34.82448
7 33.54222 28.73134 38.35310
8 33.54222 28.73134 38.35310
9 33.54222 28.73134 38.35310
10 33.54222 28.73134 38.35310
```

You can see the confidence interval in our predicted values in the above output.

The output clearly says that the cars which are traveling at a speed of 11-13 mph have chances to travel the distance in the range of 19.9 to 31.3 miles.

Simple right? This is how the predict() function in R language works. I hope now you understand the predict() function in R.

## Wrapping Up

The predict() function is used to predict the values based on the previous data behaviors and thus by fitting that data to the model.

You can also use the confidence intervals to check the accuracy of our predictions.

That’s all for now. **Happy Predicting!**

**More read:** R documentation

“The output clearly says that the cars which are traveling at a speed of 11-13 mph have chances to travel the distance in the range of 19.9 to 31.3 miles.”

No, it really doesn’t.