# What Is The lm() Function In R And How To Use It?

In this article, we will learn about the lm() function in R, what it is and how to use it for linear regression analysis. Let’s go into detail now.

## What is the lm() function in R?

The lm() function in the R language is a linear model function used for fit linear models and linear regression analysis.

Syntax:

lm(formula, data)

Parameters:

• formula: a symbolic description of the model.
• data: an optional data frame or environment containing the variables in the model.

## How to use the lm() function in R?

We will use specific examples to help you learn how to use the lm() function in R.

### Fitting a linear model

The linear model of the form: y = a + b*x + errors

The lm() function takes as its first argument a formula, which is a symbolic description of the model. For example, y ~ x is a formula stating that y depends on x. The second argument of the lm() function is data, which defines the data frame where x and y are found. The lm() function returns an object which contains information about the fitted model.

Example:

# Sample data frame
dataSample <- data.frame(x = c(1, 2, 3), y = c(4, 5, 6))

# Fit a linear model
rel <- lm(y ~ x, dataSample)

print(rel)

Output:

Call:
lm(formula = y ~ x, data = dataSample)

Coefficients:
(Intercept)            x
3            1  

### Linear regression analysis

A simple example of linear regression analysis is predicting height when knowing a person’s weight.

To predict, we need to have a relationship between a person’s weight and height. The steps are:

• Measure the height and weight value of many people.
• Use the lm() function in R to fit linear models.
• Use the summary() function to see detailed information on the model’s performance and coefficients.
• Use predict() function in R to predict a person’s height.

Example:

Here is an example of measured height and weight data.

Weight:

48, 56, 52, 42, 65, 61, 70, 58, 60, 57

Height:

159, 172, 160, 171, 166, 174, 178, 164, 169, 170

Code:

# Height
height <- c(159, 172, 160, 171, 166, 174, 178, 164, 169, 170)

# Weight
weight <- c(48, 56, 52, 42, 65, 61, 70, 58, 60, 57)

# Fit linear models
fitModel <- lm(height ~ weight)

# Detailed information on the model's performance and coefficients
summaryModel <- summary(fitModel)
print(summaryModel)

# Predict a person's height
df <- data.frame(weight <- c(50, 60))
result <- predict(fitModel, df)

print(result)

Output:

Call:
lm(formula = height ~ weight)

Residuals:
Min      1Q  Median      3Q     Max
-6.5644 -5.0492  0.6333  4.1905  7.9777

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 148.1457    13.3607  11.088 3.91e-06 ***
weight        0.3542     0.2327   1.522    0.166
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 5.656 on 8 degrees of freedom
Multiple R-squared:  0.2246,	Adjusted R-squared:  0.1277
F-statistic: 2.317 on 1 and 8 DF,  p-value: 0.1665

1       2
165.856 169.398 

Based on the available data, we predict the height of two people weighing 50 kg and 60 kg.

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

Syntax:

prediction (object, new data)

Parameter:

• object: The class inherits from the linear model.
• new data: Enter data to predict values.

## Summary

We shared the lm() function in R and how to use it for linear model tuning and linear regression analysis. We hope the information in this article has been helpful to you. Thank you for reading.

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