Predict Using Linear Regression Model

Predicts the response variable using a linear regression model

predict_linear_regression(data, lm_fit, x_vars, intercept = TRUE)

Arguments

data

dataframe containing the feature variables

lm_fit

a list containing the model object and fitted values

x_vars

character vector of variable names to be used as predictors

intercept

logical value indicating whether to include an intercept term in the model

Value

a numeric vector of predicted values

Examples

data(mtcars)
lm_fit <- fit_linear_regression(data = mtcars, x_vars = c("wt"), y_var = "mpg")
lm_pred <- predict_linear_regression(data = mtcars, lm_fit, x_vars = c("wt"))
lm_pred # display the predicted 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