disprop: Disproportionality In hotspots: Hot Spots

Description

Calculates the magnitude of disproportionality for values within a dataset.

Usage

 1 disprop(z)

Arguments

 z "hotspots" object

Details

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.

Value

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.

Author(s)

Anthony Darrouzet-Nardi

hotspots

Examples

 1 2 3 4 5 6 7 8 rln30 <- sort(c(rlnorm(15),rlnorm(15)*-1,NA), na.last = TRUE) rln30 disprop(hotspots(rln30, tail = "both")) #higher levels of disproportionality rln30sd2 <- sort(c(rlnorm(15,sd = 3),rlnorm(15,sd = 3)*-1,NA), na.last = TRUE) rln30sd2 disprop(hotspots(rln30sd2, tail = "both"))

Example output

[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

hotspots documentation built on May 1, 2019, 8:19 p.m.