Optimise the Value of Lambda for a Box-Cox Transformation
opt_bc.RdOptimise the value of \(\lambda\) for a Box-Cox transformation by minimising skewness and return the optimally transformed data together with the optimal \(\lambda\) and corresponding skewness minimum as attributes.
Usage
opt_bc(x, interval = c(-5, 5), tol = 1e-10)Value
A numeric vector the same length as x with attributes lambda and
skew representing the optimal value of \(\lambda\) and corresponding skewness minimum respectively.
Details
Uses the functions boxcox3 to create a Box-Cox transformation function for the given dataset,
skewness to calculate the skewness of the transformed data and optimise
to search for the value of \(\lambda\) that minimises skewness.
Examples
runif(20) |> opt_bc()
#> [1] -0.314671546 -0.487459633 -0.351738249 -0.333842651 -0.845747595
#> [6] -0.231712404 -0.227734177 -0.009283771 -0.029439394 -0.589327755
#> [11] -0.522696513 -0.656591404 -0.780202252 -0.456639730 -0.492348001
#> [16] -0.218320738 -0.754828147 -0.282507142 -0.870261776 -0.621304537
#> attr(,"skew")
#> [1] 1.562456e-09
#> attr(,"lambda")
#> [1] 1.090555
rlnorm(20) |> opt_bc()
#> [1] 0.91383337 0.17571240 0.24220731 1.55941560 0.11172494 -0.13444677
#> [7] -2.00424626 -0.28119515 -0.31591440 1.03933660 0.06991240 -0.64944211
#> [13] -0.05002735 -0.25307076 0.43988879 2.57408103 0.04647731 0.56944736
#> [19] 0.11784641 -2.00604296
#> attr(,"skew")
#> [1] 2.976226e-10
#> attr(,"lambda")
#> [1] -0.04998641
rpois(100, 3) |> opt_bc() ## Provokes warning
#> Warning: NA/Inf replaced by maximum positive value
#> [1] 2.3219564 2.3219564 0.8886786 0.8886786 0.8886786 2.3219564
#> [7] 1.6431935 2.3219564 1.6431935 2.9494335 2.3219564 2.9494335
#> [13] -1.4501483 2.9494335 5.1483239 1.6431935 0.8886786 2.3219564
#> [19] 1.6431935 1.6431935 2.9494335 1.6431935 0.8886786 3.5388524
#> [25] 2.9494335 0.8886786 0.8886786 2.3219564 0.0000000 0.0000000
#> [31] 0.0000000 2.3219564 2.9494335 3.5388524 1.6431935 1.6431935
#> [37] 2.3219564 0.8886786 -1.4501483 2.9494335 0.0000000 5.1483239
#> [43] 1.6431935 0.0000000 2.9494335 -1.4501483 1.6431935 0.8886786
#> [49] 2.9494335 2.9494335 2.3219564 0.8886786 2.3219564 0.8886786
#> [55] 1.6431935 0.8886786 1.6431935 0.8886786 1.6431935 3.5388524
#> [61] 4.0983958 2.9494335 2.9494335 1.6431935 2.3219564 4.6335743
#> [67] 2.3219564 0.8886786 0.8886786 2.3219564 2.9494335 0.8886786
#> [73] -1.4501483 3.5388524 1.6431935 2.3219564 2.3219564 0.8886786
#> [79] 0.8886786 2.3219564 2.3219564 0.0000000 2.9494335 0.0000000
#> [85] 2.9494335 0.0000000 1.6431935 0.8886786 2.3219564 0.0000000
#> [91] 3.5388524 0.8886786 0.8886786 2.9494335 2.9494335 0.0000000
#> [97] 2.9494335 2.3219564 0.8886786 5.6455813
#> attr(,"skew")
#> [1] 3.822212e-09
#> attr(,"lambda")
#> [1] 0.6895846
rpois(100, 3) |> opt_bc(c(-3, 5)) ## Redefining interval avoids warning
#> [1] 0.9195554 3.9062541 0.9195554 -1.2813094 0.0000000 3.2182117
#> [7] 0.9195554 0.9195554 -1.2813094 3.9062541 0.9195554 2.4990467
#> [13] 0.9195554 3.2182117 0.9195554 4.5694489 1.7388097 2.4990467
#> [19] 3.2182117 2.4990467 0.9195554 0.0000000 0.0000000 2.4990467
#> [25] 1.7388097 2.4990467 0.9195554 4.5694489 2.4990467 3.2182117
#> [31] 4.5694489 0.9195554 2.4990467 0.9195554 -1.2813094 1.7388097
#> [37] 1.7388097 0.9195554 3.2182117 1.7388097 4.5694489 -1.2813094
#> [43] 2.4990467 0.9195554 3.9062541 2.4990467 2.4990467 2.4990467
#> [49] 1.7388097 0.9195554 1.7388097 0.9195554 2.4990467 1.7388097
#> [55] 0.9195554 3.9062541 0.9195554 0.0000000 1.7388097 0.9195554
#> [61] 0.0000000 3.9062541 2.4990467 2.4990467 1.7388097 1.7388097
#> [67] 0.0000000 0.9195554 0.0000000 2.4990467 2.4990467 3.9062541
#> [73] 0.0000000 4.5694489 0.9195554 2.4990467 2.4990467 0.0000000
#> [79] 1.7388097 2.4990467 1.7388097 1.7388097 0.9195554 -1.2813094
#> [85] 4.5694489 0.0000000 2.4990467 3.2182117 3.2182117 1.7388097
#> [91] 3.2182117 -1.2813094 0.0000000 0.0000000 2.4990467 0.9195554
#> [97] 0.0000000 0.0000000 3.9062541 0.9195554
#> attr(,"skew")
#> [1] 9.547003e-09
#> attr(,"lambda")
#> [1] 0.7804517