Adds the prediction columns to a piped command set.
Source:R/predict-column.R
tidypredict_to_column.RdAdds a new column with the results from tidypredict_fit() to a piped command set. If add_interval is set to TRUE, it will add two additional columns- one for the lower and another for the upper prediction interval bounds.
Usage
tidypredict_to_column(
df,
model,
add_interval = FALSE,
interval = 0.95,
vars = c("fit", "upper", "lower")
)Arguments
- df
A data.frame or tibble
- model
An R model or a parsed model inside a data frame
- add_interval
Switch that indicates if the prediction interval columns should be added. Defaults to FALSE
- interval
The prediction interval, defaults to 0.95. Ignored if add_interval is set to FALSE
- vars
The name of the variables that this function will produce. Defaults to "fit", "upper", and "lower".
Value
The input data frame with one new column (the fit) added, or three
new columns (fit, upper and lower bounds) when add_interval is TRUE.
Examples
model <- lm(mpg ~ wt, data = mtcars)
tidypredict_to_column(mtcars, model)
#> mpg cyl disp hp drat wt qsec vs am gear
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4
#> carb fit
#> Mazda RX4 4 23.282611
#> Mazda RX4 Wag 4 21.919770
#> Datsun 710 1 24.885952
#> Hornet 4 Drive 1 20.102650
#> Hornet Sportabout 2 18.900144
#> Valiant 1 18.793255
#> Duster 360 4 18.205363
#> Merc 240D 2 20.236262
#> Merc 230 2 20.450041
#> Merc 280 4 18.900144
#> Merc 280C 4 18.900144
#> Merc 450SE 3 15.533127
#> Merc 450SL 3 17.350247
#> Merc 450SLC 3 17.083024
#> Cadillac Fleetwood 4 9.226650
#> Lincoln Continental 4 8.296712
#> Chrysler Imperial 4 8.718926
#> Fiat 128 1 25.527289
#> Honda Civic 2 28.653805
#> Toyota Corolla 1 27.478021
#> Toyota Corona 1 24.111004
#> Dodge Challenger 2 18.472586
#> AMC Javelin 2 18.926866
#> Camaro Z28 4 16.762355
#> Pontiac Firebird 2 16.735633
#> Fiat X1-9 1 26.943574
#> Porsche 914-2 2 25.847957
#> Lotus Europa 2 29.198941
#> Ford Pantera L 4 20.343151
#> Ferrari Dino 6 22.480940
#> Maserati Bora 8 18.205363
#> Volvo 142E 2 22.427495