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Apr 7, 2014 10:12 PM
(650 views)

Is there a cdf function in JSL that can return the percentile of a value x with a distribution defined by an array or a column in data table?

I only found functions that return the percentiles for special distribution functions (normal, beta, gamma...), but I need t find out in an array of measured data what is the percentile of one measured value.

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Solution

There is a function CDF() that generates a list with two matrices that can be used for finding the approximate percentiles. The distribution platform can also be used (interactively or via JSL).

The results will differ between the three methods below, especially for extreme percentiles and small number of data:

dt = Open**(** "$SAMPLE_DATA/Big Class.jmp" **)**;

Y = :Weight << **get values**;

**{**Quant, CumProb**}** = CDF**(** Y **)**;

P = **[****0.1**, **0.5**, **0.9****]**; // Example of percentile levels

// Find approximate values of the P percentiles by "matching"

P1 = Quant**[**Loc Sorted**(** CumProb, P **)]**;

// Find approximate P percentiles by interpolating the CDF

// For small data sets the results depends much on the choice of smoothing term lambda (here 1)

P2 = Spline Eval**(** P, Spline Coef**(** CumProb, Quant, **1** **)** **)**;

// Find approximate percentiles using Distribution platform

dist = dt << **Distribution****(**

Continuous Distribution**(**

Column**(** :weight **)**,

Fit Distribution**(** Smooth Curve**(** Quantiles**(** P**[****1****]**, P**[****3****]**, P**[****2****]** **)** **)** **)**

**)**

**)**;

P3 =Report**(** dist **)[**Number Col Box**(**"Quantile"**)]**<<**get ****as matrix**;

Show**(** P1, P2, P3 **)**;

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There is a function CDF() that generates a list with two matrices that can be used for finding the approximate percentiles. The distribution platform can also be used (interactively or via JSL).

The results will differ between the three methods below, especially for extreme percentiles and small number of data:

dt = Open**(** "$SAMPLE_DATA/Big Class.jmp" **)**;

Y = :Weight << **get values**;

**{**Quant, CumProb**}** = CDF**(** Y **)**;

P = **[****0.1**, **0.5**, **0.9****]**; // Example of percentile levels

// Find approximate values of the P percentiles by "matching"

P1 = Quant**[**Loc Sorted**(** CumProb, P **)]**;

// Find approximate P percentiles by interpolating the CDF

// For small data sets the results depends much on the choice of smoothing term lambda (here 1)

P2 = Spline Eval**(** P, Spline Coef**(** CumProb, Quant, **1** **)** **)**;

// Find approximate percentiles using Distribution platform

dist = dt << **Distribution****(**

Continuous Distribution**(**

Column**(** :weight **)**,

Fit Distribution**(** Smooth Curve**(** Quantiles**(** P**[****1****]**, P**[****3****]**, P**[****2****]** **)** **)** **)**

**)**

**)**;

P3 =Report**(** dist **)[**Number Col Box**(**"Quantile"**)]**<<**get ****as matrix**;

Show**(** P1, P2, P3 **)**;

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Apr 8, 2014 9:30 AM
(523 views)