Package ffx.numerics.math
Class SummaryStatistics
java.lang.Object
ffx.numerics.math.SummaryStatistics
The SummaryStatistics class uses online, stable algorithms to calculate summary statistics from
double arrays/lists, including mean, variance, standard deviation, max, min, sum, and count.
This is intended for accuracy and numerical stability, not necessarily for performance (e.g. using Kahan summation).
- Since:
- 1.0
- Author:
- Michael J. Schnieders, Jacob M. Litman
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Field Summary
Modifier and TypeFieldDescriptionfinal long
final long
final double
final double
final double
final double
final double
final double
final double
final double
final double
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Constructor Summary
ConstructorDescriptionSummaryStatistics
(double[] values) Constructs a static summary of a statistic from provided values.SummaryStatistics
(double[] values, double[] weights, int first, int last, int stride) Constructs a static summary of a statistic from provided values.SummaryStatistics
(double[] values, int first) Constructs a static summary of a statistic from provided values.SummaryStatistics
(double[] values, int first, int last) Constructs a static summary of a statistic from provided values.SummaryStatistics
(double[] values, int first, int last, int stride) Constructs a static summary of a statistic from provided values.Builds a static view of a running statistic. -
Method Summary
Modifier and TypeMethodDescriptiondouble
Computes a 95% confidence interval based on a Student's T-distribution.double
confidenceInterval
(double alpha) Computes a confidence interval based on a Student's T-distribution.describe()
Describe the Summary Statistics.double
getMean()
The mean.double
getSd()
The standard deviation.double
getVar()
The variance.toString()
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Field Details
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mean
public final double mean -
var
public final double var -
varPopulation
public final double varPopulation -
sd
public final double sd -
sdPopulation
public final double sdPopulation -
sumWeights
public final double sumWeights -
min
public final double min -
max
public final double max -
count
public final long count -
sum
public final double sum -
dof
public final long dof
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Constructor Details
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SummaryStatistics
Builds a static view of a running statistic.- Parameters:
rs
- Running statistic.
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SummaryStatistics
public SummaryStatistics(double[] values) Constructs a static summary of a statistic from provided values. Assumes weights are all constant (1.0). Assumes all values will be used.- Parameters:
values
- Values to summarize.
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SummaryStatistics
public SummaryStatistics(double[] values, int first) Constructs a static summary of a statistic from provided values. Assumes weights are all constant (1.0). Assumes all values from first to end will be used.- Parameters:
values
- Values to summarize.first
- First value to use.
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SummaryStatistics
public SummaryStatistics(double[] values, int first, int last) Constructs a static summary of a statistic from provided values. Assumes weights are all constant (1.0). Assumes a stride of 1.- Parameters:
values
- Values to summarize.first
- First value to use.last
- Last value to use.
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SummaryStatistics
public SummaryStatistics(double[] values, int first, int last, int stride) Constructs a static summary of a statistic from provided values. Assumes weights are all constant (1.0).- Parameters:
values
- Values to summarize.first
- First value to use.last
- Last value to use.stride
- Stride between values used.
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SummaryStatistics
public SummaryStatistics(@Nullable double[] values, @Nullable double[] weights, int first, int last, int stride) Constructs a static summary of a statistic from provided values.- Parameters:
values
- Values to summarize.weights
- Weights for each value.first
- First value to use.last
- Last value to use.stride
- Stride between values used.
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Method Details
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confidenceInterval
public double confidenceInterval()Computes a 95% confidence interval based on a Student's T-distribution.- Returns:
- 95% confidence interval.
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confidenceInterval
public double confidenceInterval(double alpha) Computes a confidence interval based on a Student's T-distribution.- Parameters:
alpha
- Alpha (e.g. 0.05 for a 95% CI).- Returns:
- Confidence interval.
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getMean
public double getMean()The mean.- Returns:
- Return the mean.
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getSd
public double getSd()The standard deviation.- Returns:
- Return the standard deviation.
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getVar
public double getVar()The variance.- Returns:
- Return the variance.
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toString
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describe
Describe the Summary Statistics.- Returns:
- Return the description.
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