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java.lang.Object org.apache.commons.math3.stat.inference.ChiSquareTest
public class ChiSquareTest
Implements ChiSquare test statistics.
This implementation handles both known and unknown distributions.
Two samples tests can be used when the distribution is unknown a priori but provided by one sample, or when the hypothesis under test is that the two samples come from the same underlying distribution.
Constructor Summary  

ChiSquareTest()
Construct a ChiSquareTest 
Method Summary  

double 
chiSquare(double[] expected,
long[] observed)
Computes the ChiSquare statistic comparing observed and expected
frequency counts. 
double 
chiSquare(long[][] counts)
Computes the ChiSquare statistic associated with a chisquare test of independence based on the input counts
array, viewed as a twoway table. 
double 
chiSquareDataSetsComparison(long[] observed1,
long[] observed2)
Computes a ChiSquare two sample test statistic comparing bin frequency counts in observed1 and observed2 . 
double 
chiSquareTest(double[] expected,
long[] observed)
Returns the observed significance level, or pvalue, associated with a Chisquare goodness of fit test comparing the observed
frequency counts to those in the expected array. 
boolean 
chiSquareTest(double[] expected,
long[] observed,
double alpha)
Performs a Chisquare goodness of fit test evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance level alpha . 
double 
chiSquareTest(long[][] counts)
Returns the observed significance level, or pvalue, associated with a chisquare test of independence based on the input counts
array, viewed as a twoway table. 
boolean 
chiSquareTest(long[][] counts,
double alpha)
Performs a chisquare test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2way table are independent of the rows, with significance level alpha . 
double 
chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2)
Returns the observed significance level, or pvalue, associated with a ChiSquare two sample test comparing bin frequency counts in observed1 and
observed2 . 
boolean 
chiSquareTestDataSetsComparison(long[] observed1,
long[] observed2,
double alpha)
Performs a ChiSquare two sample test comparing two binned data sets. 
Methods inherited from class java.lang.Object 

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait 
Constructor Detail 

public ChiSquareTest()
Method Detail 

public double chiSquare(double[] expected, long[] observed) throws NotPositiveException, NotStrictlyPositiveException, DimensionMismatchException
observed
and expected
frequency counts.
This statistic can be used to perform a ChiSquare test evaluating the null hypothesis that the observed counts follow the expected distribution.
Preconditions:
If any of the preconditions are not met, an
IllegalArgumentException
is thrown.
Note: This implementation rescales the
expected
array if necessary to ensure that the sum of the
expected and observed counts are equal.
observed
 array of observed frequency countsexpected
 array of expected frequency counts
NotPositiveException
 if observed
has negative entries
NotStrictlyPositiveException
 if expected
has entries that are
not strictly positive
DimensionMismatchException
 if the arrays length is less than 2public double chiSquareTest(double[] expected, long[] observed) throws NotPositiveException, NotStrictlyPositiveException, DimensionMismatchException, MaxCountExceededException
observed
frequency counts to those in the expected
array.
The number returned is the smallest significance level at which one can reject the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts.
Preconditions:
If any of the preconditions are not met, an
IllegalArgumentException
is thrown.
Note: This implementation rescales the
expected
array if necessary to ensure that the sum of the
expected and observed counts are equal.
observed
 array of observed frequency countsexpected
 array of expected frequency counts
NotPositiveException
 if observed
has negative entries
NotStrictlyPositiveException
 if expected
has entries that are
not strictly positive
DimensionMismatchException
 if the arrays length is less than 2
MaxCountExceededException
 if an error occurs computing the pvaluepublic boolean chiSquareTest(double[] expected, long[] observed, double alpha) throws NotPositiveException, NotStrictlyPositiveException, DimensionMismatchException, OutOfRangeException, MaxCountExceededException
alpha
. Returns true iff the null
hypothesis can be rejected with 100 * (1  alpha) percent confidence.
Example:
To test the hypothesis that observed
follows
expected
at the 99% level, use
chiSquareTest(expected, observed, 0.01)
Preconditions:
0 < alpha < 0.5
If any of the preconditions are not met, an
IllegalArgumentException
is thrown.
Note: This implementation rescales the
expected
array if necessary to ensure that the sum of the
expected and observed counts are equal.
observed
 array of observed frequency countsexpected
 array of expected frequency countsalpha
 significance level of the test
NotPositiveException
 if observed
has negative entries
NotStrictlyPositiveException
 if expected
has entries that are
not strictly positive
DimensionMismatchException
 if the arrays length is less than 2
OutOfRangeException
 if alpha
is not in the range (0, 0.5]
MaxCountExceededException
 if an error occurs computing the pvaluepublic double chiSquare(long[][] counts) throws NullArgumentException, NotPositiveException, DimensionMismatchException
counts
array, viewed as a twoway table.
The rows of the 2way table are
count[0], ... , count[count.length  1]
Preconditions:
counts
must have at
least 2 columns and at least 2 rows.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown.
counts
 array representation of 2way table
NullArgumentException
 if the array is null
DimensionMismatchException
 if the array is not rectangular
NotPositiveException
 if counts
has negative entriespublic double chiSquareTest(long[][] counts) throws NullArgumentException, DimensionMismatchException, NotPositiveException, MaxCountExceededException
counts
array, viewed as a twoway table.
The rows of the 2way table are
count[0], ... , count[count.length  1]
Preconditions:
counts
must have at least 2
columns and at least 2 rows.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown.
counts
 array representation of 2way table
NullArgumentException
 if the array is null
DimensionMismatchException
 if the array is not rectangular
NotPositiveException
 if counts
has negative entries
MaxCountExceededException
 if an error occurs computing the pvaluepublic boolean chiSquareTest(long[][] counts, double alpha) throws NullArgumentException, DimensionMismatchException, NotPositiveException, OutOfRangeException, MaxCountExceededException
alpha
.
Returns true iff the null hypothesis can be rejected with 100 * (1  alpha) percent
confidence.
The rows of the 2way table are
count[0], ... , count[count.length  1]
Example:
To test the null hypothesis that the counts in
count[0], ... , count[count.length  1]
all correspond to the same underlying probability distribution at the 99% level, use
chiSquareTest(counts, 0.01)
Preconditions:
counts
must have at least 2 columns and
at least 2 rows.
If any of the preconditions are not met, an
IllegalArgumentException
is thrown.
counts
 array representation of 2way tablealpha
 significance level of the test
NullArgumentException
 if the array is null
DimensionMismatchException
 if the array is not rectangular
NotPositiveException
 if counts
has any negative entries
OutOfRangeException
 if alpha
is not in the range (0, 0.5]
MaxCountExceededException
 if an error occurs computing the pvaluepublic double chiSquareDataSetsComparison(long[] observed1, long[] observed2) throws DimensionMismatchException, NotPositiveException, ZeroException
Computes a
ChiSquare two sample test statistic comparing bin frequency counts
in observed1
and observed2
. 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])]
where
K = &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:
observed1
and observed2
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.
observed1
 array of observed frequency counts of the first data setobserved2
 array of observed frequency counts of the second data set
DimensionMismatchException
 the the length of the arrays does not match
NotPositiveException
 if any entries in observed1
or
observed2
are negative
ZeroException
 if either all counts of observed1
or
observed2
are zero, or if the count at some index is zero
for both arrayspublic double chiSquareTestDataSetsComparison(long[] observed1, long[] observed2) throws DimensionMismatchException, NotPositiveException, ZeroException, MaxCountExceededException
Returns the observed significance level, or
pvalue, associated with a ChiSquare two sample test comparing
bin frequency counts in observed1
and
observed2
.
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 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.
observed1
and observed2
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.
observed1
 array of observed frequency counts of the first data setobserved2
 array of observed frequency counts of the second data set
DimensionMismatchException
 the the length of the arrays does not match
NotPositiveException
 if any entries in observed1
or
observed2
are negative
ZeroException
 if either all counts of observed1
or
observed2
are zero, or if the count at the same index is zero
for both arrays
MaxCountExceededException
 if an error occurs computing the pvaluepublic boolean chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) throws DimensionMismatchException, NotPositiveException, ZeroException, OutOfRangeException, MaxCountExceededException
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 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.
observed1
and observed2
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.
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
DimensionMismatchException
 the the length of the arrays does not match
NotPositiveException
 if any entries in observed1
or
observed2
are negative
ZeroException
 if either all counts of observed1
or
observed2
are zero, or if the count at the same index is zero
for both arrays
OutOfRangeException
 if alpha
is not in the range (0, 0.5]
MaxCountExceededException
 if an error occurs performing the test


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