Remove intercept from the linear regression model

To remove the intercept from a linear model, we manually set the value of intercept zero. In this way, we may not necessarily get the best fit line but the line guaranteed passes through the origin. To set the intercept as zero we add 0 and plus sign in front of the fitting formula. This makes the intercept zero.

Syntax:

linear_model <- lm( var1 ~ 0+ formula, data )

summary( linear_model )

Parameter:

• var1: determines the variable on which data is to be fitted.
• formula: determines the formula for the linear model.
• data: determines the name of the data frame that contains the data.

Example: Here, is a linear regression model without intercept in the R Language.

Output:

Call:

lm(formula = y ~ 0 + x1 + x2, data = sample_data)

Residuals:

     1       2       3       4       5  

-1.5422 -0.3795 -1.6325  4.7831 -0.8434  

Coefficients:

  Estimate Std. Error t value Pr(>|t|)  

x1   1.2711     0.6886   1.846   0.1621  

x2   0.8554     0.3056   2.799   0.0679 .

—

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.097 on 3 degrees of freedom

Multiple R-squared:  0.9757,    Adjusted R-squared:  0.9595  

F-statistic: 60.22 on 2 and 3 DF,  p-value: 0.003789

Here, we don’t get intercept in the coefficient of summary section as the intercept is set to zero.

In this article, we will discuss how to remove intercept from the Regression model in the R Programming Language.