print and summary method for "transform" class.

# S3 method for transform
summary(object, ...)

Arguments

object

an object of class "transform", usually, a result of a call to transform().

...

further arguments passed to or from other methods.

Details

summary.transform compares the distribution of data before and after data transformation.

See also

Examples

# \donttest{ # Standardization ------------------------------ creatinine_minmax <- transform(heartfailure$creatinine, method = "minmax") creatinine_minmax
#> [1] 0.15730337 0.06741573 0.08988764 0.15730337 0.24719101 0.17977528 #> [7] 0.07865169 0.06741573 0.11235955 1.00000000 0.39325843 0.04494382 #> [13] 0.06741573 0.06741573 0.05617978 0.08988764 0.04494382 0.03370787 #> [19] 0.05617978 0.15730337 0.08988764 0.12359551 0.04494382 0.03370787 #> [25] 0.14943820 0.15730337 0.05617978 0.08988764 0.59550562 0.07865169 #> [31] 0.14943820 0.28089888 0.05617978 0.07865169 0.05617978 0.33707865 #> [37] 0.05617978 0.05617978 0.20224719 0.28089888 0.14943820 0.07865169 #> [43] 0.07865169 0.05617978 0.06741573 0.15730337 0.04494382 0.01123596 #> [49] 0.43820225 0.05617978 0.05617978 0.10112360 0.70786517 0.05617978 #> [55] 0.19101124 0.16853933 0.24719101 0.01123596 0.06741573 0.08988764 #> [61] 0.05617978 0.20224719 0.06741573 0.05617978 0.07640449 0.26966292 #> [67] 0.08988764 0.05617978 0.07865169 0.14943820 0.03370787 0.04494382 #> [73] 0.05617978 0.08988764 0.07865169 0.02247191 0.03370787 0.07865169 #> [79] 0.01123596 0.04494382 0.13483146 0.07640449 0.22471910 0.14606742 #> [85] 0.05617978 0.02247191 0.06741573 0.03370787 0.02247191 0.06741573 #> [91] 0.03370787 0.05617978 0.07640449 0.13483146 0.02247191 0.05617978 #> [97] 0.08988764 0.06741573 0.07865169 0.06741573 0.06741573 0.07640449 #> [103] 0.06741573 0.05617978 0.20224719 0.13483146 0.08988764 0.04494382 #> [109] 0.06741573 0.08988764 0.07865169 0.07865169 0.12359551 0.08988764 #> [115] 0.07865169 0.05617978 0.02247191 0.30337079 0.04494382 0.14943820 #> [121] 0.11235955 0.05617978 0.02808989 0.04494382 0.35955056 0.08988764 #> [127] 0.17977528 0.03370787 0.02247191 0.32584270 0.02247191 0.62921348 #> [133] 0.07640449 0.08988764 0.07640449 0.07640449 0.04494382 0.17977528 #> [139] 0.05617978 0.03370787 0.06741573 0.04494382 0.04494382 0.04494382 #> [145] 0.13483146 0.02247191 0.02247191 0.05617978 0.14943820 0.04494382 #> [151] 0.22471910 0.04494382 0.04494382 0.07640449 0.03370787 0.13483146 #> [157] 0.10112360 0.05617978 0.08988764 0.06741573 0.07865169 0.03370787 #> [163] 0.04494382 0.04494382 0.06741573 0.08988764 0.02247191 0.21348315 #> [169] 0.05617978 0.03370787 0.11235955 0.04494382 0.06741573 0.03370787 #> [175] 0.04494382 0.05617978 0.05617978 0.05617978 0.07865169 0.02247191 #> [181] 0.04494382 0.05617978 0.07865169 0.22471910 0.07865169 0.11235955 #> [187] 0.01123596 0.17977528 0.05617978 0.04494382 0.17977528 0.11235955 #> [193] 0.02247191 0.07640449 0.12359551 0.14606742 0.07640449 0.03370787 #> [199] 0.05617978 0.14606742 0.02247191 0.05617978 0.04494382 0.33707865 #> [205] 0.02247191 0.05617978 0.03370787 0.04494382 0.05617978 0.03370787 #> [211] 0.05617978 0.03370787 0.10112360 0.12359551 0.03370787 0.08988764 #> [217] 0.04494382 0.95505618 0.06741573 0.02247191 0.14943820 0.06741573 #> [223] 0.06741573 0.03370787 0.05617978 0.10112360 0.08988764 0.05617978 #> [229] 0.50561798 0.07865169 0.13483146 0.06741573 0.04494382 0.10112360 #> [235] 0.06741573 0.06741573 0.06741573 0.07865169 0.05617978 0.07640449 #> [241] 0.08988764 0.08988764 0.06741573 0.04494382 0.14606742 0.10112360 #> [247] 0.06741573 0.21348315 0.05617978 0.07865169 0.00000000 0.03370787 #> [253] 0.05617978 0.07865169 0.05617978 0.05617978 0.13483146 0.05617978 #> [259] 0.03370787 0.02247191 0.05617978 0.02247191 0.10112360 0.05617978 #> [265] 0.07865169 0.04494382 0.14943820 0.13483146 0.04494382 0.05617978 #> [271] 0.12359551 0.04494382 0.07865169 0.02247191 0.05617978 0.03370787 #> [277] 0.06741573 0.06741573 0.02247191 0.08988764 0.05617978 0.24719101 #> [283] 0.37078652 0.06741573 0.03370787 0.07865169 0.13483146 0.05617978 #> [289] 0.06741573 0.04494382 0.03370787 0.10112360 0.05617978 0.04494382 #> [295] 0.06741573 0.07865169 0.03370787 0.10112360 0.12359551 #> attr(,"method") #> [1] "minmax" #> attr(,"origin") #> [1] 1.90 1.10 1.30 1.90 2.70 2.10 1.20 1.10 1.50 9.40 4.00 0.90 1.10 1.10 1.00 #> [16] 1.30 0.90 0.80 1.00 1.90 1.30 1.60 0.90 0.80 1.83 1.90 1.00 1.30 5.80 1.20 #> [31] 1.83 3.00 1.00 1.20 1.00 3.50 1.00 1.00 2.30 3.00 1.83 1.20 1.20 1.00 1.10 #> [46] 1.90 0.90 0.60 4.40 1.00 1.00 1.40 6.80 1.00 2.20 2.00 2.70 0.60 1.10 1.30 #> [61] 1.00 2.30 1.10 1.00 1.18 2.90 1.30 1.00 1.20 1.83 0.80 0.90 1.00 1.30 1.20 #> [76] 0.70 0.80 1.20 0.60 0.90 1.70 1.18 2.50 1.80 1.00 0.70 1.10 0.80 0.70 1.10 #> [91] 0.80 1.00 1.18 1.70 0.70 1.00 1.30 1.10 1.20 1.10 1.10 1.18 1.10 1.00 2.30 #> [106] 1.70 1.30 0.90 1.10 1.30 1.20 1.20 1.60 1.30 1.20 1.00 0.70 3.20 0.90 1.83 #> [121] 1.50 1.00 0.75 0.90 3.70 1.30 2.10 0.80 0.70 3.40 0.70 6.10 1.18 1.30 1.18 #> [136] 1.18 0.90 2.10 1.00 0.80 1.10 0.90 0.90 0.90 1.70 0.70 0.70 1.00 1.83 0.90 #> [151] 2.50 0.90 0.90 1.18 0.80 1.70 1.40 1.00 1.30 1.10 1.20 0.80 0.90 0.90 1.10 #> [166] 1.30 0.70 2.40 1.00 0.80 1.50 0.90 1.10 0.80 0.90 1.00 1.00 1.00 1.20 0.70 #> [181] 0.90 1.00 1.20 2.50 1.20 1.50 0.60 2.10 1.00 0.90 2.10 1.50 0.70 1.18 1.60 #> [196] 1.80 1.18 0.80 1.00 1.80 0.70 1.00 0.90 3.50 0.70 1.00 0.80 0.90 1.00 0.80 #> [211] 1.00 0.80 1.40 1.60 0.80 1.30 0.90 9.00 1.10 0.70 1.83 1.10 1.10 0.80 1.00 #> [226] 1.40 1.30 1.00 5.00 1.20 1.70 1.10 0.90 1.40 1.10 1.10 1.10 1.20 1.00 1.18 #> [241] 1.30 1.30 1.10 0.90 1.80 1.40 1.10 2.40 1.00 1.20 0.50 0.80 1.00 1.20 1.00 #> [256] 1.00 1.70 1.00 0.80 0.70 1.00 0.70 1.40 1.00 1.20 0.90 1.83 1.70 0.90 1.00 #> [271] 1.60 0.90 1.20 0.70 1.00 0.80 1.10 1.10 0.70 1.30 1.00 2.70 3.80 1.10 0.80 #> [286] 1.20 1.70 1.00 1.10 0.90 0.80 1.40 1.00 0.90 1.10 1.20 0.80 1.40 1.60 #> attr(,"class") #> [1] "transform" "numeric"
summary(creatinine_minmax)
#> * Standardization with minmax #> #> * Information of Transformation (before vs after) #> Original Transformation #> n 299.00000000 2.990000e+02 #> na 0.00000000 0.000000e+00 #> mean 1.39387960 1.004359e-01 #> sd 1.03451006 1.162371e-01 #> se_mean 0.05982726 6.722164e-03 #> IQR 0.50000000 5.617978e-02 #> skewness 4.45599588 4.455996e+00 #> kurtosis 25.82823866 2.582824e+01 #> p00 0.50000000 0.000000e+00 #> p01 0.60000000 1.123596e-02 #> p05 0.70000000 2.247191e-02 #> p10 0.80000000 3.370787e-02 #> p20 0.90000000 4.494382e-02 #> p25 0.90000000 4.494382e-02 #> p30 1.00000000 5.617978e-02 #> p40 1.00000000 5.617978e-02 #> p50 1.10000000 6.741573e-02 #> p60 1.20000000 7.865169e-02 #> p70 1.30000000 8.988764e-02 #> p75 1.40000000 1.011236e-01 #> p80 1.70000000 1.348315e-01 #> p90 2.10000000 1.797753e-01 #> p95 3.00000000 2.808989e-01 #> p99 6.11400000 6.307865e-01 #> p100 9.40000000 1.000000e+00
plot(creatinine_minmax)
# Resolving Skewness -------------------------- creatinine_log <- transform(heartfailure$creatinine, method = "log") creatinine_log
#> [1] 0.64185389 0.09531018 0.26236426 0.64185389 0.99325177 0.74193734 #> [7] 0.18232156 0.09531018 0.40546511 2.24070969 1.38629436 -0.10536052 #> [13] 0.09531018 0.09531018 0.00000000 0.26236426 -0.10536052 -0.22314355 #> [19] 0.00000000 0.64185389 0.26236426 0.47000363 -0.10536052 -0.22314355 #> [25] 0.60431597 0.64185389 0.00000000 0.26236426 1.75785792 0.18232156 #> [31] 0.60431597 1.09861229 0.00000000 0.18232156 0.00000000 1.25276297 #> [37] 0.00000000 0.00000000 0.83290912 1.09861229 0.60431597 0.18232156 #> [43] 0.18232156 0.00000000 0.09531018 0.64185389 -0.10536052 -0.51082562 #> [49] 1.48160454 0.00000000 0.00000000 0.33647224 1.91692261 0.00000000 #> [55] 0.78845736 0.69314718 0.99325177 -0.51082562 0.09531018 0.26236426 #> [61] 0.00000000 0.83290912 0.09531018 0.00000000 0.16551444 1.06471074 #> [67] 0.26236426 0.00000000 0.18232156 0.60431597 -0.22314355 -0.10536052 #> [73] 0.00000000 0.26236426 0.18232156 -0.35667494 -0.22314355 0.18232156 #> [79] -0.51082562 -0.10536052 0.53062825 0.16551444 0.91629073 0.58778666 #> [85] 0.00000000 -0.35667494 0.09531018 -0.22314355 -0.35667494 0.09531018 #> [91] -0.22314355 0.00000000 0.16551444 0.53062825 -0.35667494 0.00000000 #> [97] 0.26236426 0.09531018 0.18232156 0.09531018 0.09531018 0.16551444 #> [103] 0.09531018 0.00000000 0.83290912 0.53062825 0.26236426 -0.10536052 #> [109] 0.09531018 0.26236426 0.18232156 0.18232156 0.47000363 0.26236426 #> [115] 0.18232156 0.00000000 -0.35667494 1.16315081 -0.10536052 0.60431597 #> [121] 0.40546511 0.00000000 -0.28768207 -0.10536052 1.30833282 0.26236426 #> [127] 0.74193734 -0.22314355 -0.35667494 1.22377543 -0.35667494 1.80828877 #> [133] 0.16551444 0.26236426 0.16551444 0.16551444 -0.10536052 0.74193734 #> [139] 0.00000000 -0.22314355 0.09531018 -0.10536052 -0.10536052 -0.10536052 #> [145] 0.53062825 -0.35667494 -0.35667494 0.00000000 0.60431597 -0.10536052 #> [151] 0.91629073 -0.10536052 -0.10536052 0.16551444 -0.22314355 0.53062825 #> [157] 0.33647224 0.00000000 0.26236426 0.09531018 0.18232156 -0.22314355 #> [163] -0.10536052 -0.10536052 0.09531018 0.26236426 -0.35667494 0.87546874 #> [169] 0.00000000 -0.22314355 0.40546511 -0.10536052 0.09531018 -0.22314355 #> [175] -0.10536052 0.00000000 0.00000000 0.00000000 0.18232156 -0.35667494 #> [181] -0.10536052 0.00000000 0.18232156 0.91629073 0.18232156 0.40546511 #> [187] -0.51082562 0.74193734 0.00000000 -0.10536052 0.74193734 0.40546511 #> [193] -0.35667494 0.16551444 0.47000363 0.58778666 0.16551444 -0.22314355 #> [199] 0.00000000 0.58778666 -0.35667494 0.00000000 -0.10536052 1.25276297 #> [205] -0.35667494 0.00000000 -0.22314355 -0.10536052 0.00000000 -0.22314355 #> [211] 0.00000000 -0.22314355 0.33647224 0.47000363 -0.22314355 0.26236426 #> [217] -0.10536052 2.19722458 0.09531018 -0.35667494 0.60431597 0.09531018 #> [223] 0.09531018 -0.22314355 0.00000000 0.33647224 0.26236426 0.00000000 #> [229] 1.60943791 0.18232156 0.53062825 0.09531018 -0.10536052 0.33647224 #> [235] 0.09531018 0.09531018 0.09531018 0.18232156 0.00000000 0.16551444 #> [241] 0.26236426 0.26236426 0.09531018 -0.10536052 0.58778666 0.33647224 #> [247] 0.09531018 0.87546874 0.00000000 0.18232156 -0.69314718 -0.22314355 #> [253] 0.00000000 0.18232156 0.00000000 0.00000000 0.53062825 0.00000000 #> [259] -0.22314355 -0.35667494 0.00000000 -0.35667494 0.33647224 0.00000000 #> [265] 0.18232156 -0.10536052 0.60431597 0.53062825 -0.10536052 0.00000000 #> [271] 0.47000363 -0.10536052 0.18232156 -0.35667494 0.00000000 -0.22314355 #> [277] 0.09531018 0.09531018 -0.35667494 0.26236426 0.00000000 0.99325177 #> [283] 1.33500107 0.09531018 -0.22314355 0.18232156 0.53062825 0.00000000 #> [289] 0.09531018 -0.10536052 -0.22314355 0.33647224 0.00000000 -0.10536052 #> [295] 0.09531018 0.18232156 -0.22314355 0.33647224 0.47000363 #> attr(,"method") #> [1] "log" #> attr(,"origin") #> [1] 1.90 1.10 1.30 1.90 2.70 2.10 1.20 1.10 1.50 9.40 4.00 0.90 1.10 1.10 1.00 #> [16] 1.30 0.90 0.80 1.00 1.90 1.30 1.60 0.90 0.80 1.83 1.90 1.00 1.30 5.80 1.20 #> [31] 1.83 3.00 1.00 1.20 1.00 3.50 1.00 1.00 2.30 3.00 1.83 1.20 1.20 1.00 1.10 #> [46] 1.90 0.90 0.60 4.40 1.00 1.00 1.40 6.80 1.00 2.20 2.00 2.70 0.60 1.10 1.30 #> [61] 1.00 2.30 1.10 1.00 1.18 2.90 1.30 1.00 1.20 1.83 0.80 0.90 1.00 1.30 1.20 #> [76] 0.70 0.80 1.20 0.60 0.90 1.70 1.18 2.50 1.80 1.00 0.70 1.10 0.80 0.70 1.10 #> [91] 0.80 1.00 1.18 1.70 0.70 1.00 1.30 1.10 1.20 1.10 1.10 1.18 1.10 1.00 2.30 #> [106] 1.70 1.30 0.90 1.10 1.30 1.20 1.20 1.60 1.30 1.20 1.00 0.70 3.20 0.90 1.83 #> [121] 1.50 1.00 0.75 0.90 3.70 1.30 2.10 0.80 0.70 3.40 0.70 6.10 1.18 1.30 1.18 #> [136] 1.18 0.90 2.10 1.00 0.80 1.10 0.90 0.90 0.90 1.70 0.70 0.70 1.00 1.83 0.90 #> [151] 2.50 0.90 0.90 1.18 0.80 1.70 1.40 1.00 1.30 1.10 1.20 0.80 0.90 0.90 1.10 #> [166] 1.30 0.70 2.40 1.00 0.80 1.50 0.90 1.10 0.80 0.90 1.00 1.00 1.00 1.20 0.70 #> [181] 0.90 1.00 1.20 2.50 1.20 1.50 0.60 2.10 1.00 0.90 2.10 1.50 0.70 1.18 1.60 #> [196] 1.80 1.18 0.80 1.00 1.80 0.70 1.00 0.90 3.50 0.70 1.00 0.80 0.90 1.00 0.80 #> [211] 1.00 0.80 1.40 1.60 0.80 1.30 0.90 9.00 1.10 0.70 1.83 1.10 1.10 0.80 1.00 #> [226] 1.40 1.30 1.00 5.00 1.20 1.70 1.10 0.90 1.40 1.10 1.10 1.10 1.20 1.00 1.18 #> [241] 1.30 1.30 1.10 0.90 1.80 1.40 1.10 2.40 1.00 1.20 0.50 0.80 1.00 1.20 1.00 #> [256] 1.00 1.70 1.00 0.80 0.70 1.00 0.70 1.40 1.00 1.20 0.90 1.83 1.70 0.90 1.00 #> [271] 1.60 0.90 1.20 0.70 1.00 0.80 1.10 1.10 0.70 1.30 1.00 2.70 3.80 1.10 0.80 #> [286] 1.20 1.70 1.00 1.10 0.90 0.80 1.40 1.00 0.90 1.10 1.20 0.80 1.40 1.60 #> attr(,"class") #> [1] "transform" "numeric"
summary(creatinine_log)
#> * Resolving Skewness with log #> #> * Information of Transformation (before vs after) #> Original Transformation #> n 299.00000000 299.00000000 #> na 0.00000000 0.00000000 #> mean 1.39387960 0.19858693 #> sd 1.03451006 0.45310789 #> se_mean 0.05982726 0.02620391 #> IQR 0.50000000 0.44183275 #> skewness 4.45599588 1.58398978 #> kurtosis 25.82823866 3.60164397 #> p00 0.50000000 -0.69314718 #> p01 0.60000000 -0.51082562 #> p05 0.70000000 -0.35667494 #> p10 0.80000000 -0.22314355 #> p20 0.90000000 -0.10536052 #> p25 0.90000000 -0.10536052 #> p30 1.00000000 0.00000000 #> p40 1.00000000 0.00000000 #> p50 1.10000000 0.09531018 #> p60 1.20000000 0.18232156 #> p70 1.30000000 0.26236426 #> p75 1.40000000 0.33647224 #> p80 1.70000000 0.53062825 #> p90 2.10000000 0.74193734 #> p95 3.00000000 1.09861229 #> p99 6.11400000 1.81046145 #> p100 9.40000000 2.24070969
plot(creatinine_log)
plot(creatinine_log, typographic = FALSE)
# }