Java 中的 z 分数和 p 值(生存函数)
zScore and p-value in Java (survival function)
以下代码的 java 等价物是什么?
import scipy
from scipy.stats import zscore
zlist = [9967,11281,10752,10576,2366,11882,11798,]
z = zscore(zlist)
for e in z:
print e,scipy.stats.norm.sf(abs(e))
答案是:
private void run() {
double[] values = {9967,11281,10752,10576,2366,11882,11798};
double variance = StatUtils.populationVariance(values);
double sd = Math.sqrt(variance);
double mean = StatUtils.mean(values);
NormalDistribution nd = new NormalDistribution();
for ( double value: values ) {
double stdscore = (value-mean)/sd;
double sf = 1.0 - nd.cumulativeProbability(Math.abs(stdscore));
System.out.println("" + stdscore + " " + sf);
}
}
这是使用The Apache Commons Mathematics Library
编辑:或者,甚至更好:
import java.util.function.BiConsumer;
import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
public class ZScore {
public static void main(String[] args) {
ZScore program = new ZScore();
double[] values = {9967,11281,10752,10576,2366,11882,11798};
program.computeZScoreAndSurvivalFunctions(
new DescriptiveStatistics(values),
new NormalDistribution(),
(zscore, sf)->System.out.println(""+zscore+" "+sf)
);
}
private void computeZScoreAndSurvivalFunctions(
DescriptiveStatistics ds,
RealDistribution dist,
BiConsumer<Double, Double> consumer
) {
double variance = ds.getPopulationVariance();
double sd = Math.sqrt(variance);
double mean = ds.getMean();
for ( int index = 0; index < ds.getN(); ++index) {
double zscore = (ds.getElement(index)-mean)/sd;
double sf = 1.0 - dist.cumulativeProbability(Math.abs(zscore));
consumer.accept(zscore, sf);
}
}
}
以下代码的 java 等价物是什么?
import scipy
from scipy.stats import zscore
zlist = [9967,11281,10752,10576,2366,11882,11798,]
z = zscore(zlist)
for e in z:
print e,scipy.stats.norm.sf(abs(e))
答案是:
private void run() {
double[] values = {9967,11281,10752,10576,2366,11882,11798};
double variance = StatUtils.populationVariance(values);
double sd = Math.sqrt(variance);
double mean = StatUtils.mean(values);
NormalDistribution nd = new NormalDistribution();
for ( double value: values ) {
double stdscore = (value-mean)/sd;
double sf = 1.0 - nd.cumulativeProbability(Math.abs(stdscore));
System.out.println("" + stdscore + " " + sf);
}
}
这是使用The Apache Commons Mathematics Library
编辑:或者,甚至更好:
import java.util.function.BiConsumer;
import org.apache.commons.math3.distribution.NormalDistribution;
import org.apache.commons.math3.distribution.RealDistribution;
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics;
public class ZScore {
public static void main(String[] args) {
ZScore program = new ZScore();
double[] values = {9967,11281,10752,10576,2366,11882,11798};
program.computeZScoreAndSurvivalFunctions(
new DescriptiveStatistics(values),
new NormalDistribution(),
(zscore, sf)->System.out.println(""+zscore+" "+sf)
);
}
private void computeZScoreAndSurvivalFunctions(
DescriptiveStatistics ds,
RealDistribution dist,
BiConsumer<Double, Double> consumer
) {
double variance = ds.getPopulationVariance();
double sd = Math.sqrt(variance);
double mean = ds.getMean();
for ( int index = 0; index < ds.getN(); ++index) {
double zscore = (ds.getElement(index)-mean)/sd;
double sf = 1.0 - dist.cumulativeProbability(Math.abs(zscore));
consumer.accept(zscore, sf);
}
}
}