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java.lang.ObjectbaseCode.math.metaanalysis.MetaAnalysis
baseCode.math.metaanalysis.CorrelationEffectMetaAnalysis
Implementation of meta-analysis of correlations along the lines of chapter 18 of Cooper and Hedges, "Handbook of Research Synthesis". Both fixed and random effects models are supported, with z-transformed or untransformed correlations.
Copyright (c) 2004 Columbia University
| Constructor Summary | |
CorrelationEffectMetaAnalysis()
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CorrelationEffectMetaAnalysis(boolean fixed,
boolean transform)
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| Method Summary | |
protected double |
fisherTransformedSamplingVariance(double sampleSize)
Equation 18-8 from CH. |
protected cern.colt.list.DoubleArrayList |
fisherTransformedSamplingVariances(cern.colt.list.DoubleArrayList sampleSizes)
Run equation CH 18-8 on a list of sample sizes. |
double |
getBsv()
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double |
getE()
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double |
getN()
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double |
getP()
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double |
getQ()
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double |
getV()
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double |
getZ()
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double |
run(cern.colt.list.DoubleArrayList effects,
cern.colt.list.DoubleArrayList sampleSizes)
Following CH section 2.2. |
protected double |
samplingVariance(double r,
double numsamples)
Equation 18-10 from CH. |
protected cern.colt.list.DoubleArrayList |
samplingVariances(cern.colt.list.DoubleArrayList effectSizes,
cern.colt.list.DoubleArrayList sampleSizes)
Run equation CH 18-10 on a list of sample sizes and effects. |
void |
setFixed(boolean fixed)
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void |
setTransform(boolean transform)
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| Methods inherited from class baseCode.math.metaanalysis.MetaAnalysis |
fisherCombineLogPvalues, fisherCombinePvalues, metaFEWeights, metaRESampleVariance, metaREVariance, metaREWeights, metaVariance, metaVariance, metaZscore, qStatistic, qTest, weightedMean, weightedMean |
| Methods inherited from class java.lang.Object |
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
public CorrelationEffectMetaAnalysis(boolean fixed,
boolean transform)
public CorrelationEffectMetaAnalysis()
| Method Detail |
public double run(cern.colt.list.DoubleArrayList effects,
cern.colt.list.DoubleArrayList sampleSizes)
There are four possible cases (for now):
sampleSizes -
protected double samplingVariance(double r,
double numsamples)
v_i = ( 1 - r_i ˆ 2 ) ˆ 2 / ( n_i - 1 )
I added a regularization to this, so that we don't get ridiculous variances when correlations are close to 1 (this happens). If the correlation is very close to 1 (or -1), we fudge it to be a value less close to 1 (e.g., 0.999)
r -
protected cern.colt.list.DoubleArrayList samplingVariances(cern.colt.list.DoubleArrayList effectSizes,
cern.colt.list.DoubleArrayList sampleSizes)
effectSizes - sampleSizes -
samplingVarianceprotected double fisherTransformedSamplingVariance(double sampleSize)
v_i = 1 / ( n_i - 3 )
protected cern.colt.list.DoubleArrayList fisherTransformedSamplingVariances(cern.colt.list.DoubleArrayList sampleSizes)
sampleSizes -
public void setFixed(boolean fixed)
public void setTransform(boolean transform)
public double getP()
public double getQ()
public double getZ()
public double getE()
public double getV()
public double getN()
public double getBsv()
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