CDFtestTrackx: Test a single CDF implementation with one set of parameters.

In CDF.PSIdekick: Evaluate Differentially Private Algorithms for Publishing Cumulative Distribution Functions

Description

Applies diagnostic functions to a single dpCDF, and only releases a complete set of diagnostic results (called withinCDFtest in Data Collection mode — e.g., when Visualization = FALSE)

Usage

CDFtestTrackx(funct, eps, data, range = range, gran, reps, samplesize,
  SmoothAll = FALSE, ExtraTests_CDF = list(), ExtraTests_PDF = list(),
  ...)

Arguments

funct	The differentially-private CDF-generating function to be tested
eps	Epsilon value for Differential privacy control
data	A vector of the data (single variable to compute CDFs from)
range	A vector length 2 containing user-specified min and max to truncate the universe to
gran	The smallest unit of measurement in the data (one [year] for a list of ages)
reps	The number of times the combination of CDFfunction, dataset, and epsilon will be tested
samplesize	The specified sample size is randomly selected from each dataset without replacement.
SmoothAll	Applies L2 monotonicity post-processing to every DP-CDF
ExtraTests_CDF	If a user wishes to add extra diagnostics, the proper syntax would be: ExtraTests_CDF = list( functionName1 = function1, functionName2 = function2)
ExtraTests_PDF	See above
...	Optionally add additional parameters. This is primarily used to allow automated execution of varied diagnostic functions.

Value

A complete set of diagnostic results in the form of ...$allscores, which holds out a row of output for each of reps results.

Examples

CDFtestTrackx(badCDF, eps = .01, cdfstep = 0, data = rexp(10000,.4),
  range= c(1,10), gran = .1, reps = 20, samplesize = 10000)

Example output

$allscores
$allscores$MaxError_CDF
 [1] 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858
[11] 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858 0.8858

$allscores$MaxErrorAt_CDF
 [1] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10

$allscores$diffat25
 [1] 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322
[11] 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322 0.3322

$allscores$diffatMedian
 [1] 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102
[11] 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102 0.5102

$allscores$diffat75
 [1] 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505
[11] 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505 0.7505

$allscores$horzdiffat25
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$allscores$horzdiffatMed
 [1] 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8
[20] 1.8

$allscores$horzdiffat75
 [1] 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4
[20] 3.4

$allscores$Medians
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$allscores$MAE_CDF
 [1] 0.7216264 0.7216264 0.7216264 0.7216264 0.7216264 0.7216264 0.7216264
 [8] 0.7216264 0.7216264 0.7216264 0.7216264 0.7216264 0.7216264 0.7216264
[15] 0.7216264 0.7216264 0.7216264 0.7216264 0.7216264 0.7216264

$allscores$MSE
 [1] 0.5515305 0.5515305 0.5515305 0.5515305 0.5515305 0.5515305 0.5515305
 [8] 0.5515305 0.5515305 0.5515305 0.5515305 0.5515305 0.5515305 0.5515305
[15] 0.5515305 0.5515305 0.5515305 0.5515305 0.5515305 0.5515305

$allscores$MaxError_PDF
 [1] 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322
[11] 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322 0.2322

$allscores$MaxErrorAt_PDF
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$allscores$MAE_PDF
 [1] 0.009734066 0.009734066 0.009734066 0.009734066 0.009734066 0.009734066
 [7] 0.009734066 0.009734066 0.009734066 0.009734066 0.009734066 0.009734066
[13] 0.009734066 0.009734066 0.009734066 0.009734066 0.009734066 0.009734066
[19] 0.009734066 0.009734066

$allscores$MSE_PDF
 [1] 0.0006937793 0.0006937793 0.0006937793 0.0006937793 0.0006937793
 [6] 0.0006937793 0.0006937793 0.0006937793 0.0006937793 0.0006937793
[11] 0.0006937793 0.0006937793 0.0006937793 0.0006937793 0.0006937793
[16] 0.0006937793 0.0006937793 0.0006937793 0.0006937793 0.0006937793

$allscores$MeanDiff
 [1] 2.37978 2.37978 2.37978 2.37978 2.37978 2.37978 2.37978 2.37978 2.37978
[10] 2.37978 2.37978 2.37978 2.37978 2.37978 2.37978 2.37978 2.37978 2.37978
[19] 2.37978 2.37978

$allscores$ModeDiff
 [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

$allscores$StdDiff
 [1] 1.63915 1.63915 1.63915 1.63915 1.63915 1.63915 1.63915 1.63915 1.63915
[10] 1.63915 1.63915 1.63915 1.63915 1.63915 1.63915 1.63915 1.63915 1.63915
[19] 1.63915 1.63915

$allscores$VarDiff
 [1] 3.619833 3.619833 3.619833 3.619833 3.619833 3.619833 3.619833 3.619833
 [9] 3.619833 3.619833 3.619833 3.619833 3.619833 3.619833 3.619833 3.619833
[17] 3.619833 3.619833 3.619833 3.619833

$allscores$SkewDiff
 [1] -2.030268 -2.030268 -2.030268 -2.030268 -2.030268 -2.030268 -2.030268
 [8] -2.030268 -2.030268 -2.030268 -2.030268 -2.030268 -2.030268 -2.030268
[15] -2.030268 -2.030268 -2.030268 -2.030268 -2.030268 -2.030268

$allscores$KurtDiff
 [1] -0.1326975 -0.1326975 -0.1326975 -0.1326975 -0.1326975 -0.1326975
 [7] -0.1326975 -0.1326975 -0.1326975 -0.1326975 -0.1326975 -0.1326975
[13] -0.1326975 -0.1326975 -0.1326975 -0.1326975 -0.1326975 -0.1326975
[19] -0.1326975 -0.1326975

$allscores$MSEanalytic
 [1] 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11
[16] 0.11 0.11 0.11 0.11 0.11

CDF.PSIdekick documentation built on May 30, 2017, 5:09 a.m.