fit_linear_regression.RdFits a linear regression model to the given data
fit_linear_regression(data, x_vars, y_var, intercept = TRUE)dataframe containing the feature and label variables
character vector of variable names to be used as predictors
character string of the variable name to be used as the response variable
logical value indicating whether to include an intercept term in the model
a list containing the model object and fitted values
data(mtcars)
lm_fit <- fit_linear_regression(data = mtcars, x_vars = c("wt"), y_var = "mpg")
lm_fit$model # display the model object
#> $coefficients
#> mpg
#> 37.285126
#> wt -5.344472
#>
#> $residuals
#> mpg
#> Mazda RX4 -2.2826106
#> Mazda RX4 Wag -0.9197704
#> Datsun 710 -2.0859521
#> Hornet 4 Drive 1.2973499
#> Hornet Sportabout -0.2001440
#> Valiant -0.6932545
#> Duster 360 -3.9053627
#> Merc 240D 4.1637381
#> Merc 230 2.3499593
#> Merc 280 0.2998560
#> Merc 280C -1.1001440
#> Merc 450SE 0.8668731
#> Merc 450SL -0.0502472
#> Merc 450SLC -1.8830236
#> Cadillac Fleetwood 1.1733496
#> Lincoln Continental 2.1032876
#> Chrysler Imperial 5.9810744
#> Fiat 128 6.8727113
#> Honda Civic 1.7461954
#> Toyota Corolla 6.4219792
#> Toyota Corona -2.6110037
#> Dodge Challenger -2.9725862
#> AMC Javelin -3.7268663
#> Camaro Z28 -3.4623553
#> Pontiac Firebird 2.4643670
#> Fiat X1-9 0.3564263
#> Porsche 914-2 0.1520430
#> Lotus Europa 1.2010593
#> Ford Pantera L -4.5431513
#> Ferrari Dino -2.7809399
#> Maserati Bora -3.2053627
#> Volvo 142E -1.0274952
#>
lm_fit$fitted_values # display the fitted values
#> mpg
#> Mazda RX4 23.282611
#> Mazda RX4 Wag 21.919770
#> Datsun 710 24.885952
#> Hornet 4 Drive 20.102650
#> Hornet Sportabout 18.900144
#> Valiant 18.793255
#> Duster 360 18.205363
#> Merc 240D 20.236262
#> Merc 230 20.450041
#> Merc 280 18.900144
#> Merc 280C 18.900144
#> Merc 450SE 15.533127
#> Merc 450SL 17.350247
#> Merc 450SLC 17.083024
#> Cadillac Fleetwood 9.226650
#> Lincoln Continental 8.296712
#> Chrysler Imperial 8.718926
#> Fiat 128 25.527289
#> Honda Civic 28.653805
#> Toyota Corolla 27.478021
#> Toyota Corona 24.111004
#> Dodge Challenger 18.472586
#> AMC Javelin 18.926866
#> Camaro Z28 16.762355
#> Pontiac Firebird 16.735633
#> Fiat X1-9 26.943574
#> Porsche 914-2 25.847957
#> Lotus Europa 29.198941
#> Ford Pantera L 20.343151
#> Ferrari Dino 22.480940
#> Maserati Bora 18.205363
#> Volvo 142E 22.427495