Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/******************************************************************************
* Confidential Proprietary *
* (c) Copyright Haifeng Li 2011, All Rights Reserved *
******************************************************************************/
package smile.stat.hypothesis;
import smile.math.special.Gamma;
/**
* Pearson's chi-square test, also known as the chi-square goodness-of-fit test
* or chi-square test for independence. Note that the chi-square distribution
* is only approximately valid for large sample size. If a siginficant fraction
* of bins have small numbers of counts (say, < 10), then it the statistic is
* not well approximated by a chi-square probability function.
*
* @author Haifeng Li
*/
public class ChiSqTest {
/**
* The degree of freedom of chisq-statistic.
*/
public double df;
/**
* chi-square statistic
*/
public double chisq;
/**
* p-value
*/
public double pvalue;
/**
* Constructor.
*/
private ChiSqTest(double chisq, double df, double pvalue) {
this.chisq = chisq;
this.df = df;
this.pvalue = pvalue;
}
/**
* One-sample chisq test. Given the array bins containing the observed numbers of events,
* and an array prob containing the expected probabilities of events, and given
* one constraint, a small value of p-value indicates a significant
* differenece between the distributions bins and ebins.
*/
public static ChiSqTest test(int[] bins, double[] prob) {
return test(bins, prob, 1);
}
/**
* One-sample chisq test. Given the array bins containing the observed numbers of events,
* and an array prob containing the expected probabilities of events, and given
* the number of constraints (normally one), a small value of p-value
* indicates a significant differenece between the distributions bins
* and ebins.
*/
public static ChiSqTest test(int[] bins, double[] prob, int constraints) {
int nbins = bins.length;
int df = nbins - constraints;
int n = 0;
for (int i = 0; i < nbins; i++)
n+= bins[i];
double chisq = 0.0;
for (int j = 0; j < nbins; j++) {
if (prob[j] < 0.0 || prob[j] > 1.0 || (prob[j] == 0.0 && bins[j] > 0)) {
throw new IllegalArgumentException("Bad expected number");
}
if (prob[j] == 0.0 && bins[j] == 0) {
--df;
} else {
double nj = n * prob[j];
double temp = bins[j] - nj;
chisq += temp * temp / nj;
}
}
double p = Gamma.regularizedUpperIncompleteGamma(0.5 * df, 0.5 * chisq);
return new ChiSqTest(chisq, df, p);
}
/**
* Two-sample chisq test. Given the arrays bins1 and bins2, containing two
* sets of binned data, and given one constraint, a small value of
* p-value indicates a significant differenece between the distributions
* bins1 and bins2.
*/
public static ChiSqTest test(int[] bins1, int[] bins2) {
return test(bins1, bins2, 1);
}
/**
* Two-sample chisq test. Given the arrays bins1 and bins2, containing two
* sets of binned data, and given the number of constraints (normally one),
* a small value of p-value indicates a significant differenece between
* the distributions bins1 and bins2.
*/
public static ChiSqTest test(int[] bins1, int[] bins2, int constraints) {
if (bins1.length != bins2.length) {
throw new IllegalArgumentException("Input vectors have different size");
}
int nbins = bins1.length;
int df = nbins - constraints;
double chisq = 0.0;
for (int j = 0; j < nbins; j++) {
if (bins1[j] == 0 && bins2[j] == 0) {
--df;
} else {
double temp = bins1[j] - bins2[j];
chisq += temp * temp / (bins1[j] + bins2[j]);
}
}
double p = Gamma.regularizedUpperIncompleteGamma(0.5 * df, 0.5 * chisq);
return new ChiSqTest(chisq, df, p);
}
}
