Modern model matrices — mtrx • mdl

mdl::mtrx() (read: "model matrix") implements an opinionated and performant reimagining of model matrices. It takes in a formula and data frame, like model.frame(), and outputs a numeric matrix of predictor values, like model.matrix().

Usage

mtrx(formula, data)

Arguments

formula: A formula. Cannot contain inlined functions like -or *.
data: A data frame.

Comparison to Base R

Compared to model.matrix(), mdl::mtrx():

Does not accept formulae with inlined functions (like - or *).
Never drops rows (and thus doesn't accept an na.action).
Assumes that factors levels are encoded as they're intended (i.e. drop.unused.levels and xlev are not accepted).

mdl::mtrx() is intended to be paired with the recipes package for preprocessing.

Examples

mdl::mtrx(mpg ~ ., mtcars)
#>    (Intercept) cyl  disp  hp drat    wt  qsec vs am gear carb
#> 1            1   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> 2            1   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> 3            1   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> 4            1   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> 5            1   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> 6            1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> 7            1   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> 8            1   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> 9            1   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> 10           1   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> 11           1   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> 12           1   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> 13           1   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> 14           1   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> 15           1   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> 16           1   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> 17           1   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> 18           1   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> 19           1   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> 20           1   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> 21           1   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> 22           1   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> 23           1   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> 24           1   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> 25           1   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> 26           1   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> 27           1   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> 28           1   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> 29           1   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> 30           1   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> 31           1   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> 32           1   4 121.0 109 4.11 2.780 18.60  1  1    4    2