Interface UnknownDistributionChiSquareTest

 All Superinterfaces:
ChiSquareTest
 All Known Implementing Classes:
ChiSquareTestImpl
public interface UnknownDistributionChiSquareTest extends ChiSquareTest
An interface for ChiSquare tests for unknown distributions.Two samples tests are used when the distribution is unknown a priori but provided by one sample. We compare the second sample against the first.
 Since:
 1.2


Method Summary
All Methods Instance Methods Abstract Methods Modifier and Type Method Description double
chiSquareDataSetsComparison(long[] observed1, long[] observed2)
Computes a ChiSquare two sample test statistic comparing bin frequency counts inobserved1
andobserved2
.double
chiSquareTestDataSetsComparison(long[] observed1, long[] observed2)
Returns the observed significance level, or pvalue, associated with a ChiSquare two sample test comparing bin frequency counts inobserved1
andobserved2
.boolean
chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha)
Performs a ChiSquare two sample test comparing two binned data sets.
Methods inherited from interface org.apache.commons.math.stat.inference.ChiSquareTest
chiSquare, chiSquare, chiSquareTest, chiSquareTest, chiSquareTest, chiSquareTest




Method Detail

chiSquareDataSetsComparison
double chiSquareDataSetsComparison(long[] observed1, long[] observed2) throws java.lang.IllegalArgumentException
Computes a ChiSquare two sample test statistic comparing bin frequency counts in
observed1
andobserved2
. The sums of frequency counts in the two samples are not required to be the same. The formula used to compute the test statistic is∑[(K * observed1[i]  observed2[i]/K)^{2} / (observed1[i] + observed2[i])]
whereK = &sqrt;[&sum(observed2 / ∑(observed1)]
This statistic can be used to perform a ChiSquare test evaluating the null hypothesis that both observed counts follow the same distribution.
Preconditions:
 Observed counts must be nonnegative.
 Observed counts for a specific bin must not both be zero.
 Observed counts for a specific sample must not all be 0.
 The arrays
observed1
andobserved2
must have the same length and their common length must be at least 2.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown. Parameters:
observed1
 array of observed frequency counts of the first data setobserved2
 array of observed frequency counts of the second data set Returns:
 chiSquare statistic
 Throws:
java.lang.IllegalArgumentException
 if preconditions are not met

chiSquareTestDataSetsComparison
double chiSquareTestDataSetsComparison(long[] observed1, long[] observed2) throws java.lang.IllegalArgumentException, MathException
Returns the observed significance level, or pvalue, associated with a ChiSquare two sample test comparing bin frequency counts in
observed1
andobserved2
.The number returned is the smallest significance level at which one can reject the null hypothesis that the observed counts conform to the same distribution.
See
Preconditions:chiSquareDataSetsComparison(long[], long[])
for details on the formula used to compute the test statistic. The degrees of of freedom used to perform the test is one less than the common length of the input observed count arrays. Observed counts must be nonnegative.
 Observed counts for a specific bin must not both be zero.
 Observed counts for a specific sample must not all be 0.
 The arrays
observed1
andobserved2
must have the same length and their common length must be at least 2.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown. Parameters:
observed1
 array of observed frequency counts of the first data setobserved2
 array of observed frequency counts of the second data set Returns:
 pvalue
 Throws:
java.lang.IllegalArgumentException
 if preconditions are not metMathException
 if an error occurs computing the pvalue

chiSquareTestDataSetsComparison
boolean chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) throws java.lang.IllegalArgumentException, MathException
Performs a ChiSquare two sample test comparing two binned data sets. The test evaluates the null hypothesis that the two lists of observed counts conform to the same frequency distribution, with significance level
alpha
. Returns true iff the null hypothesis can be rejected with 100 * (1  alpha) percent confidence.See
Preconditions:chiSquareDataSetsComparison(long[], long[])
for details on the formula used to compute the Chisquare statistic used in the test. The degrees of of freedom used to perform the test is one less than the common length of the input observed count arrays. Observed counts must be nonnegative.
 Observed counts for a specific bin must not both be zero.
 Observed counts for a specific sample must not all be 0.
 The arrays
observed1
andobserved2
must have the same length and their common length must be at least 2. 
0 < alpha < 0.5
If any of the preconditions are not met, an
IllegalArgumentException
is thrown. Parameters:
observed1
 array of observed frequency counts of the first data setobserved2
 array of observed frequency counts of the second data setalpha
 significance level of the test Returns:
 true iff null hypothesis can be rejected with confidence 1  alpha
 Throws:
java.lang.IllegalArgumentException
 if preconditions are not metMathException
 if an error occurs performing the test

