In this tutorial, you will learn how to use the **predict() function in R.** The predict function in R can help you predict the value based on your data. Let’s follow this article to learn more about it with the explanation and examples below.

**Predict() function in R**

**What does predict() do in R?**

The predict function in R can help you predict the value based on your data. You can use the predict() function to predict the value of each attribute of your model, each attribute uses this function in its way, but the functionality of the predict() function maintains the same in every case.

**Syntax**:

`predict(object, newdata, interval)`

**Parameters**:

**object:**The model object that the predictions are desired.**newdata:**The name of the data frame that predicts value.**interval:**The type of interval to make.

After learning the usage and syntax of the predict() function, you will learn how to use this function in R in the next title.

**How to use the predict() function in R**

**Create a data frame**

First, you have to have the data to predict the values. The same as machine learning, the computer needs the data to learn, and then it can predict the values based on this data. For convenience, I will get the data in the ‘datasets‘ package built-in R.

Look at the example below:

# Create a data frame that is obtained from the 'datasets' package cars_data <- datasets::cars cars_data

**Output**:

```
speed dist
...
10 11 17
11 11 28
12 12 14
13 12 20
14 12 24
15 12 28
…
```

This data frame has two attributes which are the speed and distance of the cars.

**Build the linear regression**

After creating the data frame, you have to build the linear regression based on this data frame.

Look at the example below:

# Create linear regression linear_model <- lm(dist~speed, data = cars_data) linear_model

**Output**:

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

**Use the predict() function to predict values**

In this example, you predict the distance based on the speed. So then, you will create the model to use the predict() function and fit it with the previous linear expression.

Look at the example below:

# Create a data frame that has speed values to predict distance values. data_test <- data.frame(speed = c (6, 7, 47, 25, 14, 12, 16, 20)) predict(linear_model, newdata = data_test)

**Output**

```
1 2 3 4 5 6
6.015358 9.947766 167.244117 80.731124 37.474628 29.609810
7 8
45.339445 61.069080
```

**Confidence in the predicted values**

The confidence interval can help you predict the values in an uncertain range around the mean predictions.

Look at the example below:

predict(linear_model, newdata = data_test, interval = 'confidence')

**Output**

```
fit lwr upr
1 6.015358 -2.973341 15.00406
2 9.947766 1.678977 18.21656
3 167.244117 140.484322 194.00391
4 80.731124 71.596083 89.86617
5 37.474628 32.947783 42.00147
6 29.609810 24.395138 34.82448
7 45.339445 40.937676 49.74121
8 61.069080 55.247285 66.89088
```

**Prediction interval**

The confidence interval can help you predict the values in an uncertain range around a single value.

Look at the example below:

predict(linear_model, newdata = data_test, interval = 'prediction')

**Output**

```
fit lwr upr
1 6.015358 -26.187314 38.21803
2 9.947766 -22.061423 41.95696
3 167.244117 126.350328 208.13791
4 80.731124 48.487298 112.97495
5 37.474628 6.222305 68.72695
6 29.609810 -1.749529 60.96915
7 45.339445 14.104995 76.57390
8 61.069080 29.603089 92.53507
```

**Summary**

You have learned the **predict() function in R. **By this function, you can predict the value based on your data. If you have any questions about this function, feel free to leave your comments below, and we will answer your questions. Thanks!

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My name is Thomas Valen. As a software developer, I am well-versed in programming languages. Don’t worry if you’re having trouble with the C, C++, Java, Python, JavaScript, or R programming languages. I’m here to assist you!

**Name of the university:** PTIT

**Major**: IT

**Programming Languages**: C, C++, Java, Python, JavaScript, R