Returns the data of all model predictors.
Examples
model <- lm(mpg ~ wt + cyl + hp + disp + gear + drat, data = mtcars)
ols_get_predictors(model)
#> wt cyl hp disp gear drat
#> Mazda RX4 2.620 6 110 160.0 4 3.90
#> Mazda RX4 Wag 2.875 6 110 160.0 4 3.90
#> Datsun 710 2.320 4 93 108.0 4 3.85
#> Hornet 4 Drive 3.215 6 110 258.0 3 3.08
#> Hornet Sportabout 3.440 8 175 360.0 3 3.15
#> Valiant 3.460 6 105 225.0 3 2.76
#> Duster 360 3.570 8 245 360.0 3 3.21
#> Merc 240D 3.190 4 62 146.7 4 3.69
#> Merc 230 3.150 4 95 140.8 4 3.92
#> Merc 280 3.440 6 123 167.6 4 3.92
#> Merc 280C 3.440 6 123 167.6 4 3.92
#> Merc 450SE 4.070 8 180 275.8 3 3.07
#> Merc 450SL 3.730 8 180 275.8 3 3.07
#> Merc 450SLC 3.780 8 180 275.8 3 3.07
#> Cadillac Fleetwood 5.250 8 205 472.0 3 2.93
#> Lincoln Continental 5.424 8 215 460.0 3 3.00
#> Chrysler Imperial 5.345 8 230 440.0 3 3.23
#> Fiat 128 2.200 4 66 78.7 4 4.08
#> Honda Civic 1.615 4 52 75.7 4 4.93
#> Toyota Corolla 1.835 4 65 71.1 4 4.22
#> Toyota Corona 2.465 4 97 120.1 3 3.70
#> Dodge Challenger 3.520 8 150 318.0 3 2.76
#> AMC Javelin 3.435 8 150 304.0 3 3.15
#> Camaro Z28 3.840 8 245 350.0 3 3.73
#> Pontiac Firebird 3.845 8 175 400.0 3 3.08
#> Fiat X1-9 1.935 4 66 79.0 4 4.08
#> Porsche 914-2 2.140 4 91 120.3 5 4.43
#> Lotus Europa 1.513 4 113 95.1 5 3.77
#> Ford Pantera L 3.170 8 264 351.0 5 4.22
#> Ferrari Dino 2.770 6 175 145.0 5 3.62
#> Maserati Bora 3.570 8 335 301.0 5 3.54
#> Volvo 142E 2.780 4 109 121.0 4 4.11