/**
* The calculation is done here. Example: From 1.96 (Z-score) to 0.975 (P-value) From -1.96
* (Z-score) to 0.025
*
* @return double a p value
*/
public static double cumulativeProbability(Distribution obj, Number n) {
NormalDistribution dist = new NormalDistributionImpl();
double lQuantile = 0;
try {
lQuantile = dist.cumulativeProbability(obj.zScore(n));
} catch (MathException e) {
e.printStackTrace();
}
return lQuantile;
}
// return Phi(z) = standard Gaussian cdf using Taylor approximation
public static double Phi(double z) throws MathException {
return nd.cumulativeProbability(z);
/*
if (z < -8.0) return 0.0;
if (z > 8.0) return 1.0;
double sum = 0.0, term = z;
int iter = 0;
for (int i = 3; sum + term != sum && iter++ < 500; i += 2) {
sum = sum + term;
term = term * z * z / i;
}
return 0.5 + sum * phi(z);*/
}
// Compute z such that Phi(z) = y via bisection search
public static double PhiInverse(double y) throws MathException {
return nd.inverseCumulativeProbability(y);
// return PhiInverse(y, .00000001, -8, 8);
}
/**
* @param param mean
* @param param2 standard deviation
* @return normal distribution
*/
NormalDistribution getNormalDistribution(double param, double param2) {
if (normal == null || normal.getMean() != param || normal.getStandardDeviation() != param2)
normal = new NormalDistributionImpl(param, param2);
return normal;
}