r2sd allows you to rescale a numeric vector such that the
ensuing output has a mean of 0 and a standard deviation of .5. r2sd_at is a wrapper for
mutate_at and rename_at from dplyr. It both rescales the supplied vectors to
new vectors and renames the vectors to each have a prefix of z_.
Value
The function returns a numeric vector rescaled with a mean of 0 and a standard deviation of .5.
Details
By default, na.rm is set to TRUE. If you have missing data, the function will just pass
over them.
Gelman (2008) argues that rescaling by two standard deviations puts regression inputs
on roughly the same scale no matter their original scale. This allows for some honest, if preliminary,
assessment of relative effect sizes from the regression output. This does that, but
without requiring the rescale function from arm.
I'm trying to reduce the packages on which my workflow relies.
Importantly, I tend to rescale only the ordinal and interval inputs and leave the binary inputs as 0/1.
So, my r2sd function doesn't have any of the fancier if-else statements that Gelman's rescale
function has.
References
Gelman, Andrew. 2008. "Scaling Regression Inputs by Dividing by Two Standard Deviations." Statistics in Medicine 27: 2865–2873.
Examples
x <- rnorm(100)
r2sd(x)
#> [1] 0.58938435 0.18197583 0.58039130 0.91074730 0.01668217 -0.18478770
#> [7] -0.01962033 -0.42179684 0.36908293 0.06542483 -0.88627356 0.28892837
#> [13] -0.98614536 0.50553860 -0.51143024 -0.23288148 0.31638425 -0.16144326
#> [19] -0.07254027 1.07923937 1.02523837 0.21173516 0.01595284 0.21346861
#> [25] -0.36107322 -0.05857578 -1.27614690 -0.35787795 -0.22397223 0.15244818
#> [31] -0.23264380 0.95027701 -0.03677793 -0.54346030 0.43066237 -0.39212414
#> [37] -0.58999446 0.58785329 -0.08272961 -0.05357957 0.17797965 0.60034952
#> [43] -0.25598906 0.04699418 0.15419367 0.58142698 0.74035987 0.56813722
#> [49] -0.09961741 0.03155173 0.36507027 0.24989286 0.13771068 -0.08150362
#> [55] 0.40591857 -0.22073909 1.06251295 -0.14558226 -0.53391304 -0.61590666
#> [61] 0.03739685 0.00918478 -0.24785673 -0.34481666 0.39803186 -1.28439892
#> [67] 0.42355915 -0.14919976 0.53538693 0.43795067 0.25980651 0.23653250
#> [73] -0.31081525 -0.53525793 -0.02476267 0.27148311 -1.27299036 -0.55505474
#> [79] -0.67254477 -0.12399040 -1.14078240 -0.22691123 -0.64409311 0.43743081
#> [85] 0.15814365 -0.09573691 -0.19889374 -0.21807596 0.06221853 0.08736401
#> [91] 0.59476649 -0.18209050 0.70475613 0.33387888 0.25281224 -0.22382176
#> [97] 0.29469543 0.21196985 -0.64322852 -0.62643329
r2sd_at(mtcars, c("mpg", "hp", "disp"))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> z_mpg z_hp z_disp
#> Mazda RX4 0.07544241 -0.26754642 -0.28530991
#> Mazda RX4 Wag 0.07544241 -0.26754642 -0.28530991
#> Datsun 710 0.22477172 -0.39152023 -0.49509105
#> Hornet 4 Drive 0.10862670 -0.26754642 0.11004685
#> Hornet Sportabout -0.11536726 0.20647109 0.52154061
#> Valiant -0.16514370 -0.30400931 -0.02308349
#> Duster 360 -0.48039447 0.71695148 0.52154061
#> Merc 240D 0.35750889 -0.61759012 -0.33896547
#> Merc 230 0.22477172 -0.37693508 -0.36276756
#> Merc 280 -0.07388690 -0.17274292 -0.25464959
#> Merc 280C -0.19003192 -0.17274292 -0.25464959
#> Merc 450SE -0.30617694 0.24293397 0.18185654
#> Merc 450SL -0.23151228 0.24293397 0.18185654
#> Merc 450SLC -0.40572981 0.24293397 0.18185654
#> Cadillac Fleetwood -0.80394131 0.42524840 0.97337691
#> Lincoln Continental -0.80394131 0.49817417 0.92496588
#> Chrysler Imperial -0.44721018 0.60756282 0.84428082
#> Fiat 128 1.02119472 -0.58841981 -0.61329465
#> Honda Civic 0.85527326 -0.69051589 -0.62539740
#> Toyota Corolla 1.14563581 -0.59571239 -0.64395497
#> Toyota Corona 0.11692278 -0.36234992 -0.44627659
#> Dodge Challenger -0.38084159 0.02415666 0.35210200
#> AMC Javelin -0.40572981 0.02415666 0.29562247
#> Camaro Z28 -0.56335520 0.71695148 0.48119809
#> Pontiac Firebird -0.07388690 0.20647109 0.68291072
#> Fiat X1-9 0.59809500 -0.58841981 -0.61208437
#> Porsche 914-2 0.49024605 -0.40610538 -0.44546974
#> Lotus Europa 0.85527326 -0.24566869 -0.54713290
#> Ford Pantera L -0.35595337 0.85551044 0.48523234
#> Ferrari Dino -0.03240653 0.20647109 -0.34582370
#> Maserati Bora -0.42232196 1.37328341 0.28351971
#> Volvo 142E 0.10862670 -0.27483900 -0.44264576