Extract intercept from the linear regression model
To extract intercept from the linear regression model in the R Language, we use the summary() function of the R Language. We first create the linear regression model using the lm() function. The lm() function is used to fit linear models to data frames in the R Language. It can be used to carry out regression, single stratum analysis of variance, and analysis of covariance to predict the value corresponding to data that is not in the data frame. Then we use the summary() function to retrieve the statistical summary of that model which also contains the information of intercept that the fitted model makes.
Syntax:
linear_model <- lm( formula, data )
summary( linear_model )
Parameter:
- 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 with intercept in the R Language.
R
# sample data frame sample_data <- data.frame ( x1= c (2,3,5,4,8), x2= c (0,3,5,6,23), y= c (1,6,9,15,29)) # fit linear model linear_model <- lm (y ~ x1+x2, data=sample_data) # view summary of linear model summary (linear_model) |
Output:
Call: lm(formula = y ~ x1 + x2, data = sample_data) Residuals: 1 2 3 4 5 -1.9974 -0.6673 -1.1100 4.6628 -0.8880 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.5032 7.4142 0.203 0.858 x1 0.7471 2.7159 0.275 0.809 x2 0.9743 0.6934 1.405 0.295
Here, the intercept is estimated to be 1.5032, which can be seen clearly in the coefficient section of the linear model summary.
Remove Intercept from Regression Model in R
In this article, we will discuss how to remove intercept from the Regression model in the R Programming Language.
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