Description Usage Arguments Details Value Author(s) See Also Examples
Calculates the magnitude of disproportionality for values within a dataset.
1 | disprop(z)
|
z |
|
Calculates the magnitude of disproportionality for each value within the data by dividing
the difference between each value and the median by the difference between the hot spot cutoff,
(Ch
, as calculated by the function hotspots
), and the median:
disproportionality = (x - med(x)) / (Ch - med(x))
Using this equation, all hot spots have a magnitude of disproportionality of > 1. Increasingly skewed distributions (for example, lognormal distributions with higher standard deviation) will have higher magnitudes of disproportionality for some of their values.
A list containing the objects positive
, negative
, or both, depending on the which tails were
calculated in the hotspots
object. These objects are numeric vectors of the magnitudes of disproportionality.
NA values are preserved.
Anthony Darrouzet-Nardi
1 2 3 4 5 6 7 8 |
Loading required package: lattice
Loading required package: ineq
[1] -4.1059871 -2.2890583 -2.2355564 -1.7113327 -1.6485105 -1.4999897
[7] -1.3453997 -1.1560398 -0.7847109 -0.7289945 -0.6159303 -0.5967237
[13] -0.5866681 -0.5775989 -0.4499879 0.2616265 0.3620220 0.4193956
[19] 0.4942839 0.6509198 0.7390413 0.8521111 0.9042273 1.0789752
[25] 1.0975336 1.1732652 1.2278229 4.8399564 8.4467287 12.6139661
[31] NA
$positive
[1] -1.06489414 -0.58260847 -0.56840690 -0.42925693 -0.41258139 -0.37315804
[7] -0.33212366 -0.28185994 -0.18329439 -0.16850501 -0.13849324 -0.13339506
[13] -0.13072590 -0.12831855 -0.09444549 0.09444549 0.12109448 0.13632374
[19] 0.15620209 0.19777952 0.22117051 0.25118377 0.26501750 0.31140257
[25] 0.31632873 0.33643092 0.35091273 1.30971769 2.26709957 3.37325134
[31] NA
$negative
[1] 1.06489414 0.58260847 0.56840690 0.42925693 0.41258139 0.37315804
[7] 0.33212366 0.28185994 0.18329439 0.16850501 0.13849324 0.13339506
[13] 0.13072590 0.12831855 0.09444549 -0.09444549 -0.12109448 -0.13632374
[19] -0.15620209 -0.19777952 -0.22117051 -0.25118377 -0.26501750 -0.31140257
[25] -0.31632873 -0.33643092 -0.35091273 -1.30971769 -2.26709957 -3.37325134
[31] NA
[1] -5.824752e+03 -6.791703e+02 -4.376427e+02 -4.564945e+01 -1.216731e+01
[6] -7.341244e+00 -5.967454e+00 -4.798547e+00 -2.223625e+00 -4.022950e-01
[11] -2.580310e-01 -1.778074e-01 -1.002476e-01 -2.126799e-02 -7.233896e-03
[16] 1.364273e-02 1.803004e-01 3.693364e-01 1.474913e+00 1.975723e+00
[21] 2.181088e+00 2.342581e+00 4.627229e+00 8.298387e+00 1.035225e+01
[26] 1.193587e+01 3.068017e+01 8.539469e+01 1.240564e+02 7.920127e+02
[31] NA
$positive
[1] -3.385904e+02 -3.948005e+01 -2.544016e+01 -2.653769e+00 -7.074667e-01
[6] -4.269295e-01 -3.470717e-01 -2.791237e-01 -1.294446e-01 -2.357150e-02
[11] -1.518550e-02 -1.052214e-02 -6.013620e-03 -1.422570e-03 -6.067745e-04
[16] 6.067745e-04 1.029451e-02 2.128309e-02 8.554977e-02 1.146616e-01
[21] 1.265994e-01 1.359869e-01 2.687925e-01 4.821953e-01 6.015855e-01
[26] 6.936406e-01 1.783238e+00 4.963769e+00 7.211158e+00 4.603916e+01
[31] NA
$negative
[1] 3.385904e+02 3.948005e+01 2.544016e+01 2.653769e+00 7.074667e-01
[6] 4.269295e-01 3.470717e-01 2.791237e-01 1.294446e-01 2.357150e-02
[11] 1.518550e-02 1.052214e-02 6.013620e-03 1.422570e-03 6.067745e-04
[16] -6.067745e-04 -1.029451e-02 -2.128309e-02 -8.554977e-02 -1.146616e-01
[21] -1.265994e-01 -1.359869e-01 -2.687925e-01 -4.821953e-01 -6.015855e-01
[26] -6.936406e-01 -1.783238e+00 -4.963769e+00 -7.211158e+00 -4.603916e+01
[31] NA
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