Score transformation

These functions calculate the scores according to:

• score_z: Normal(z) distribution

• score_mad: Mean absolute deviation

• score_t: t-distribution

• score_chi: chi-distribution

score_z(x, na.rm = getOption("transx.na.rm"))

score_mad(x, na.rm = getOption("transx.na.rm"))

score_t(x, na.rm = getOption("transx.na.rm"))

score_chisq(x, na.rm = getOption("transx.na.rm"))

Arguments

x	[univariate vector] Univariate vector, numeric or ts object with only one dimension.
na.rm	[logical(1): getOption("transx.na.rm")] A value indicating whether NA values should be stripped before the computation proceeds.

Value

Returns a vector with the same class and attributes as the input vector.

Details

Because function are known with different names:

• score_z is identical to std_mean

• score_mad is identical to std_median

See also

scores

Examples

x <- seq(-3,3,0.5)
score_z(x)
#>  [1] -1.5406578 -1.2838815 -1.0271052 -0.7703289 -0.5135526 -0.2567763
#>  [7]  0.0000000  0.2567763  0.5135526  0.7703289  1.0271052  1.2838815
#> [13]  1.5406578
score_mad(x)
#>  [1] -1.3489815 -1.1241513 -0.8993210 -0.6744908 -0.4496605 -0.2248303
#>  [7]  0.0000000  0.2248303  0.4496605  0.6744908  0.8993210  1.1241513
#> [13]  1.3489815
score_t(x)
#>  [1] -1.6469149 -1.3234774 -1.0296800 -0.7564749 -0.4971831 -0.2465228
#>  [7]  0.0000000  0.2465228  0.4971831  0.7564749  1.0296800  1.3234774
#> [13]  1.6469149