Detailed Description
public methods for statistical tests
Below are the public methods for statistical tests inside of SCIP
Functions | |
SCIP_EXPORT SCIP_Real | SCIPstudentTGetCriticalValue (SCIP_CONFIDENCELEVEL clevel, int df) |
SCIP_EXPORT SCIP_Real | SCIPcomputeTwoSampleTTestValue (SCIP_Real meanx, SCIP_Real meany, SCIP_Real variancex, SCIP_Real variancey, SCIP_Real countx, SCIP_Real county) |
SCIP_EXPORT SCIP_Real | SCIPerf (SCIP_Real x) |
SCIP_EXPORT SCIP_Real | SCIPnormalGetCriticalValue (SCIP_CONFIDENCELEVEL clevel) |
SCIP_EXPORT SCIP_Real | SCIPnormalCDF (SCIP_Real mean, SCIP_Real variance, SCIP_Real value) |
Function Documentation
◆ SCIPstudentTGetCriticalValue()
SCIP_EXPORT SCIP_Real SCIPstudentTGetCriticalValue | ( | SCIP_CONFIDENCELEVEL | clevel, |
int | df | ||
) |
get critical value of a Student-T distribution for a given number of degrees of freedom at a confidence level
- Parameters
-
clevel (one-sided) confidence level df degrees of freedom
Definition at line 95 of file misc.c.
References studentt_maxdf, studentt_quartiles, and studentt_quartilesabove.
Referenced by SCIPvarCalcPscostConfidenceBound(), and SCIPvarSignificantPscostDifference().
◆ SCIPcomputeTwoSampleTTestValue()
SCIP_EXPORT SCIP_Real SCIPcomputeTwoSampleTTestValue | ( | SCIP_Real | meanx, |
SCIP_Real | meany, | ||
SCIP_Real | variancex, | ||
SCIP_Real | variancey, | ||
SCIP_Real | countx, | ||
SCIP_Real | county | ||
) |
compute a t-value for the hypothesis that x and y are from the same population; Assuming that x and y represent normally distributed random samples with equal variance, the returned value comes from a Student-T distribution with countx + county - 2 degrees of freedom; this value can be compared with a critical value (see also SCIPstudentTGetCriticalValue()) at a predefined confidence level for checking if x and y significantly differ in location
- Parameters
-
meanx the mean of the first distribution meany the mean of the second distribution variancex the variance of the x-distribution variancey the variance of the y-distribution countx number of samples of x county number of samples of y
Definition at line 112 of file misc.c.
References MAX, SCIP_INVALID, and SCIP_Real.
Referenced by SCIPvarSignificantPscostDifference().
◆ SCIPerf()
SCIP_EXPORT SCIP_Real SCIPerf | ( | SCIP_Real | x | ) |
returns the value of the Gauss error function evaluated at a given point
- Parameters
-
x value to evaluate
Definition at line 145 of file misc.c.
References exp(), REALABS, SCIP_Real, sign(), x, and y.
Referenced by SCIPcalcCumulativeDistribution(), and SCIPnormalCDF().
◆ SCIPnormalGetCriticalValue()
SCIP_EXPORT SCIP_Real SCIPnormalGetCriticalValue | ( | SCIP_CONFIDENCELEVEL | clevel | ) |
get critical value of a standard normal distribution at a given confidence level
- Parameters
-
clevel (one-sided) confidence level
Definition at line 172 of file misc.c.
References studentt_quartilesabove.
◆ SCIPnormalCDF()
SCIP_EXPORT SCIP_Real SCIPnormalCDF | ( | SCIP_Real | mean, |
SCIP_Real | variance, | ||
SCIP_Real | value | ||
) |
calculates the cumulative distribution P(-infinity <= x <= value) that a normally distributed random variable x takes a value between -infinity and parameter value
.
The distribution is given by the respective mean and deviation. This implementation uses the error function erf().
calculates the cumulative distribution P(-infinity <= x <= value) that a normally distributed random variable x takes a value between -infinity and parameter value
.
The distribution is given by the respective mean and deviation. This implementation uses the error function SCIPerf().
- Parameters
-
mean the mean value of the distribution variance the square of the deviation of the distribution value the upper limit of the calculated distribution integral
Definition at line 185 of file misc.c.
References SCIP_Real, SCIPdebugMessage, SCIPerf(), sqrt(), and SQRTOFTWO.
Referenced by SCIPvarPscostThresholdProbabilityTest().