This is a function to model simple linear regression.
Right way to use the function: slr_function(dataset, yvar = , xvar= ,…) [Note: argument 1 is the dataset name, argument 2 is the response variable, argument 3 is the predictor]
slr_function <- function(variables, yvar, xvar, ...) {
attach(variables)
# Independent variable(x)
x <- model.matrix(yvar ~ xvar)
# Dependent variable(y)
y <- yvar
detach(variables)
#calculate the co-efficients(beta0 and beta1)
b <- solve(crossprod(x))%*%crossprod(x,y)
# calculate predicted values(yhat)
yhat_dummy <- x%*%b
#calculate the normal equations (e and xe), whose indivaidual sum is equal to zero
e <- y - yhat_dummy
xe <- e*x[,2]
slr_data <- as.matrix(cbind(x[,2], y, yhat_dummy, e, xe))
colnames(slr_data) <- c("x", "y","yhat", "e", "xe")
print(cat("Regression Equation: Y =",b[1],"+",b[2],"*","x"))
return(slr_data)
}Example: Lets model cars data
slr_function(cars, yvar = dist, xvar = speed)## Regression Equation: Y = -17.57909 + 3.932409 * xNULL## x y yhat e xe
## 1 4 2 -1.849460 3.849460 15.397839
## 2 4 10 -1.849460 11.849460 47.397839
## 3 7 4 9.947766 -5.947766 -41.634365
## 4 7 22 9.947766 12.052234 84.365635
## 5 8 16 13.880175 2.119825 16.958599
## 6 9 10 17.812584 -7.812584 -70.313255
## 7 10 18 21.744993 -3.744993 -37.449927
## 8 10 26 21.744993 4.255007 42.550073
## 9 10 34 21.744993 12.255007 122.550073
## 10 11 17 25.677401 -8.677401 -95.451416
## 11 11 28 25.677401 2.322599 25.548584
## 12 12 14 29.609810 -15.609810 -187.317723
## 13 12 20 29.609810 -9.609810 -115.317723
## 14 12 24 29.609810 -5.609810 -67.317723
## 15 12 28 29.609810 -1.609810 -19.317723
## 16 13 26 33.542219 -7.542219 -98.048847
## 17 13 34 33.542219 0.457781 5.951153
## 18 13 34 33.542219 0.457781 5.951153
## 19 13 46 33.542219 12.457781 161.951153
## 20 14 26 37.474628 -11.474628 -160.644788
## 21 14 36 37.474628 -1.474628 -20.644788
## 22 14 60 37.474628 22.525372 315.355212
## 23 14 80 37.474628 42.525372 595.355212
## 24 15 20 41.407036 -21.407036 -321.105547
## 25 15 26 41.407036 -15.407036 -231.105547
## 26 15 54 41.407036 12.592964 188.894453
## 27 16 32 45.339445 -13.339445 -213.431124
## 28 16 40 45.339445 -5.339445 -85.431124
## 29 17 32 49.271854 -17.271854 -293.621518
## 30 17 40 49.271854 -9.271854 -157.621518
## 31 17 50 49.271854 0.728146 12.378482
## 32 18 42 53.204263 -11.204263 -201.676730
## 33 18 56 53.204263 2.795737 50.323270
## 34 18 76 53.204263 22.795737 410.323270
## 35 18 84 53.204263 30.795737 554.323270
## 36 19 36 57.136672 -21.136672 -401.596759
## 37 19 46 57.136672 -11.136672 -211.596759
## 38 19 68 57.136672 10.863328 206.403241
## 39 20 32 61.069080 -29.069080 -581.381606
## 40 20 48 61.069080 -13.069080 -261.381606
## 41 20 52 61.069080 -9.069080 -181.381606
## 42 20 56 61.069080 -5.069080 -101.381606
## 43 20 64 61.069080 2.930920 58.618394
## 44 22 66 68.933898 -2.933898 -64.545752
## 45 23 54 72.866307 -18.866307 -433.925051
## 46 24 70 76.798715 -6.798715 -163.169168
## 47 24 92 76.798715 15.201285 364.830832
## 48 24 93 76.798715 16.201285 388.830832
## 49 24 120 76.798715 43.201285 1036.830832
## 50 25 85 80.731124 4.268876 106.721898