IDL Analyst Reference Guide: Nonparametric Statistics

IMSL_WILCOXON

The IMSL_WILCOXON function performs a Wilcoxon rank sum test or a Wilcoxon signed rank test.

Syntax

Result = IMSL_WILCOXON( x1 [ , x2 ] [, /DOUBLE] [, FUZZ=value] [, STATS=variable] )

Return Value

If a Wilcoxon rank sum test is performed, returns the two-sided
p-value for the Wilcoxon rank sum statistic that is computed with average ranks used in the case of ties.

If a Wilcoxon signed rank test is performed, returns an array of length two containing the following values:

Arguments

x1

One-dimensional array containing the first sample.

x2

(Optional) One-dimensional array containing the second sample.

Keywords

DOUBLE

If present and nonzero, double precision is used.

FUZZ

Nonnegative constant used to determine ties in computing ranks in the combined samples. A tie is declared when two observations in the combined sample are within Fuzz of each other. Default: Fuzz = 100 x e x max { |x_{i 1}|, |x_{j 2}|}, where e is machine precision for a Wilcoxon rank sum test, and Fuzz = 0.0 for a Wilcoxon signed rank test.

STATS

Named variable into which one-dimensional array of length 10 containing the statistics shown in Table 18-1 and Table 18-2 is stored. If a Wilcoxon rank sum test is performed:

If a Wilcoxon signed rank test is performed:

Discussion

If Two Positional Arguments Are Supplied

The IMSL_WILCOXON function performs the Wilcoxon rank sum test for identical population distribution functions. The Wilcoxon test is a linear transformation of the Mann-Whitney U test. If the difference between the two populations can be attributed solely to a difference in location, then the Wilcoxon test becomes a test of equality of the population means (or medians) and is the nonparametric equivalent of the two-sample t-test. The IMSL_WILCOXON function obtains ranks in the combined sample after first eliminating missing values from the data. The rank sum statistic is then computed as the sum of the ranks in the x1 sample.

Three methods for handling ties are used. (A tie is counted when two observations are within Fuzz of each other.) Method 1 uses the largest possible rank for tied observations in the smallest sample, while Method 2 uses the smallest possible rank for these observations. Thus, the range of possible rank sums is obtained. Method 3 for handling tied observations between samples uses the average rank of the tied observations. Asymptotic standard normal scores are computed for the W score (based on a variance that has been adjusted for ties) when average ranks are used (see Conover 1980, p. 217). The probability associated with the two-sided alternative is then computed.

Hypothesis Tests

In each of the tests listed in Table 18-3, the first line gives the hypothesis (and its alternative) under the assumptions 1 to 3 below, while the second line gives the hypothesis when assumption 4 is also true. The rejection region is the same for both hypotheses and is given in terms of Method 3 for handling ties. Another output statistic should be used, (Stats(0) or Stats (3)), if another method for handling ties is desired.

Assumptions

Tables of critical values of the W statistic are given in the references for small samples.

If One Positional Argument is Supplied

The IMSL_WILCOXON function performs a Wilcoxon signed rank test of symmetry about zero. In one sample, this test can be viewed as a test that the population median is zero. In matched samples, a test that the medians of the two populations are equal can be computed by first computing difference scores. These difference scores would then be used as input to IMSL_WILCOXON. A general reference for the methods used is Conover (1980).

Routine IMSL_WILCOXON computes statistics for two methods for handling zero and tied observations. In the first method, observations within Fuzz of zero are not counted, and the average rank of tied observations is used. (Observations within Fuzz of each other are said to be tied.) In the second method, observations within Fuzz of zero are randomly assigned a positive or negative sign, and the ranks of tied observations are randomly permuted.

The W+ and W– statistics are computed as the sums of the ranks of the positive observations and the sum of the ranks of the negative observations, respectively. Asymptotic probabilities are computed using standard methods (see, e.g., Conover 1980, page 282).

Hypothesis Tests

The W+ and W– statistics may be used to test the following hypotheses about the median, M. In deciding whether to reject the null hypothesis, use the bracketed statistic if method 2 for handling ties is preferred. Possible null hypotheses and alternatives are given as follows:

Tabled values of the test statistic can be found in the references. If possible, tabled values should be used. If the number of nonzero observations is too large, then the asymptotic probabilities computed by IMSL_WILCOXON can be used.

Assumptions

The assumptions required for the hypothesis tests are as follows:

If other assumptions are made, related hypotheses that are more (or less) restrictive can be tested.

Examples

Example 1

The following example is taken from Conover (1980, p. 224). It involves the mixing time of two mixing machines using a total of 10 batches of a certain kind of batter, five batches for each machine. The null hypothesis is not rejected at the 5-percent level of significance. The warning error is always printed when one or more ties are detected.

x1 = [7.3, 6.9, 7.2, 7.8, 7.2]  
x2 = [7.4, 6.8, 6.9, 6.7, 7.1]  
p = IMSL_WILCOXON(x1, x2, Stats = stats)  
PRINT, 'p-Value = ', p  
  
p-Value =      0.141238  

.RUN  
PRO print_results, stats  
   PRINT, 'Wilcoxon W Statistic .....', stats(0)  
   PRINT, '2*E(W) - W ...............', stats(1)  
   PRINT, 'P-Value .....................', stats(2)  
   PRINT, 'Adjusted Wilcoxon Statistic..', stats(3)  
     
   PRINT, 'Adjusted 2*E(W) - W .........', stats(4)  
   PRINT, 'Adjusted P-Value ............', stats(5)  
   PRINT, 'W Statistics for Averaged Ranks ..', stats(6)  
   PRINT, 'Std Error of W (Averaged Ranks) ..', stats(7)  
   PRINT, 'Std Normal Score of W (Averaged Ranks)..', stats(8)  
   PRINT, 'Two-Sided P-Value of W (Averaged Ranks) ..', stats(9)  
END  
  
x1 = [7.3, 6.9, 7.2, 7.8, 7.2]  
x2 = [7.4, 6.8, 6.9, 6.7, 7.1]  
p = IMSL_WILCOXON(x1, x2, Stats = stats)  
print_results, stats  
  
   Wilcoxon W Statistic .................... 34.0000  
   2*E(W) - W .............................. 21.0000  
   P-Value ................................ 0.110072  
   Adjusted Wilcoxon Statistic ............. 35.0000  
   Adjusted 2*E(W) - W ..................... 20.0000  
   Adjusted P-Value ...................... 0.0745036  
   W Statistics for Averaged Ranks ......... 34.5000  
   Std Error of W (Averaged Ranks) ......... 4.75803  
   Std Normal Score of W (Averaged Ranks)... 1.47120  
   Two-Sided P-Value of W (Averaged Ranks). 0.141238  

.RUN  
PRO output_results, stats   
   PRINT, 'Statistic                Method 1     Method2'   
   PRINT, 'W+ ...................', stats(0), stats(4)   
   PRINT, 'W- ...................', stats(1), stats(5)   
   PRINT, 'Standardized Minimum...', stats(2), stats(6)   
   PRINT, 'p-value ...............', stats(3), stats(7)   
   PRINT  
   PRINT, 'Number of zeros .......', stats(8)  
   PRINT, 'Number of ties ........', stats(9)  
END  
  
x = [-25.0, -21.0, -19.0, -15.0, -13.0, -11.0, -8.0]  
p = IMSL_WILCOXON(x, Fuzz = 0.0001, Stats = stats)  
OUTPUT_RESULTS, stats  
  
Statistic               Method 1     Method 2  
W+ .....................0.00000      0.00000  
W- .....................28.0000      28.0000  
Standardized Minimum ... -2.36643    -2.36643  
p-value ................ 0.00898023   0.00898024  
  
Number of zeros .........0.00000  
Number of ties ..........0.00000