| org.apache.commons.math.stat.inference.ChiSquareTest
All known Subclasses: org.apache.commons.math.stat.inference.ChiSquareTestImpl,
| ChiSquareTest | | public interface ChiSquareTest (Code) | | An interface for Chi-Square tests.
version:
$Revision: 155427 $ $Date: 2005-02-26 06:11:52 -0700 (Sat, 26 Feb 2005) $ |
| Method Summary | |
| double | chiSquare(double[] expected, long[] observed)
Computes the
Chi-Square statistic comparing observed and expected
freqeuncy counts. | | double | chiSquare(long[][] counts)
Computes the Chi-Square statistic associated with a
chi-square test of independence based on the input counts
array, viewed as a two-way table. | | double | chiSquareTest(double[] expected, long[] observed)
Returns the observed significance level, or
p-value, associated with a
Chi-square goodness of fit test comparing the 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. | | boolean | chiSquareTest(double[] expected, long[] observed, double alpha)
Performs a
Chi-square 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
p-value, associated with a
chi-square test of independence based on the input counts
array, viewed as a two-way table. | | boolean | chiSquareTest(long[][] counts, double alpha)
Performs a
chi-square test of independence evaluating the null hypothesis that the classifications
represented by the counts in the columns of the input 2-way table are independent of the rows,
with significance level alpha. |
| chiSquare | | double chiSquare(double[] expected, long[] observed) throws IllegalArgumentException(Code) | | Computes the
Chi-Square statistic comparing observed and expected
freqeuncy counts.
This statistic can be used to perform a Chi-Square test evaluating the null hypothesis that
the observed counts follow the expected distribution.
Preconditions:
- Expected counts must all be positive.
- Observed counts must all be >= 0.
- The observed and expected arrays 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:
observed - array of observed frequency counts
Parameters:
expected - array of expected frequency counts chiSquare statistic
throws:
IllegalArgumentException - if preconditions are not met |
| chiSquare | | double chiSquare(long[][] counts) throws IllegalArgumentException(Code) | | Computes the Chi-Square statistic associated with a
chi-square test of independence based on the input counts
array, viewed as a two-way table.
The rows of the 2-way table are count[0], ... , count[count.length - 1]
Preconditions:
- All counts must be >= 0.
- The count array must be rectangular (i.e. all count[i] subarrays must have the same length).
- The 2-way table represented by
counts must have at least 2 columns and
at least 2 rows.
If any of the preconditions are not met, an
IllegalArgumentException is thrown.
Parameters:
counts - array representation of 2-way table chiSquare statistic
throws:
IllegalArgumentException - if preconditions are not met |
| chiSquareTest | | double chiSquareTest(double[] expected, long[] observed) throws IllegalArgumentException, MathException(Code) | | Returns the observed significance level, or
p-value, associated with a
Chi-square goodness of fit test comparing the 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:
- Expected counts must all be positive.
- Observed counts must all be >= 0.
- The observed and expected arrays 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:
observed - array of observed frequency counts
Parameters:
expected - array of expected frequency counts p-value
throws:
IllegalArgumentException - if preconditions are not met
throws:
MathException - if an error occurs computing the p-value |
| chiSquareTest | | boolean chiSquareTest(double[] expected, long[] observed, double alpha) throws IllegalArgumentException, MathException(Code) | | Performs a
Chi-square 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. 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:
- Expected counts must all be positive.
- Observed counts must all be >= 0.
- The observed and expected arrays 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:
observed - array of observed frequency counts
Parameters:
expected - array of expected frequency counts
Parameters:
alpha - significance level of the test true iff null hypothesis can be rejected with confidence1 - alpha
throws:
IllegalArgumentException - if preconditions are not met
throws:
MathException - if an error occurs performing the test |
| chiSquareTest | | double chiSquareTest(long[][] counts) throws IllegalArgumentException, MathException(Code) | | Returns the observed significance level, or
p-value, associated with a
chi-square test of independence based on the input counts
array, viewed as a two-way table.
The rows of the 2-way table are count[0], ... , count[count.length - 1]
Preconditions:
- All counts must be >= 0.
- The count array must be rectangular (i.e. all count[i] subarrays must have the same length).
- The 2-way table represented by
counts must have at least 2 columns and
at least 2 rows.
If any of the preconditions are not met, an
IllegalArgumentException is thrown.
Parameters:
counts - array representation of 2-way table p-value
throws:
IllegalArgumentException - if preconditions are not met
throws:
MathException - if an error occurs computing the p-value |
| chiSquareTest | | boolean chiSquareTest(long[][] counts, double alpha) throws IllegalArgumentException, MathException(Code) | | Performs a
chi-square test of independence evaluating the null hypothesis that the classifications
represented by the counts in the columns of the input 2-way table are independent of the rows,
with significance level alpha. Returns true iff the null hypothesis can be rejected
with 100 * (1 - alpha) percent confidence.
The rows of the 2-way 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:
- All counts must be >= 0.
- The count array must be rectangular (i.e. all count[i] subarrays must have the same length).
- The 2-way table represented by
counts must have at least 2 columns and
at least 2 rows.
If any of the preconditions are not met, an
IllegalArgumentException is thrown.
Parameters:
counts - array representation of 2-way table
Parameters:
alpha - significance level of the test true iff null hypothesis can be rejected with confidence1 - alpha
throws:
IllegalArgumentException - if preconditions are not met
throws:
MathException - if an error occurs performing the test |
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