The given points will follow the straight line. The very most straightforward case of a single scalar predictor variable x and a single scalar. Linear Regression - From a given set of data points, the linear regression finds an equation of straight line.

Linear regression shows the linear relationship between two variables. You actually only need a couple of points to get the line, but for simplicity, I used the following code. Linear Regression Equation Linear Regression Formula. To answer the question, one easy way to get the line is to extract the predicted values and plot the regression. The above answer concerns plotting a boundary, not the linear SVM regression line. W <- colSums(coef(svp)] * x)Ībline((b+1)/w,-w/w,lty=2, col='red')Ībline((b-1)/w,-w/w,lty=2, col='red')Īnd your second question: x = model.matrix(data=mtcars, mpg ~ scale(hp) + scale(wt) - 1) #set up data Svp = fit$finalModel #extract s4 model object X = model.matrix(data=mtcars, mpg ~ scale(hp)) #set up dataįit=train(x, y, method='svmLinear') #train I scaled the data to fit nicely on the plot of the vectors since the library scales the data at runtime. I applied a similar technique to your data below. If you add svp = fit$finalModelto the example, you should be able to follow it almost exactly. The statistical equation of the simple linear regression line, when only the response variable Y is random, is: Y E 0 E 1 x H (or in terms of each point: Y i E 0 E 1 x i H i) Here E 0 is called the intercept, E 1 the regression slope, H is the random error with mean 0, x is the regressor (independent variable), and Y the response variable (dependent variable). Take a look at this answer, which is similar, but does not use the caret library.

Equation for linear regression line