mspass::utility::VectorStatistics< T > Class Template Reference

Generic object to compute common robust statistics from a vector container of data. More...

#include <VectorStatistics.h>

Public Member Functions
	VectorStatistics (std::vector< T > din)
	VectorStatistics (T *din, int n)
T	median ()
T	mean ()
T	q1_4 ()
T	q3_4 ()
T	interquartile ()
T	mad (T center)
T	ssq ()
T	upper_bound ()
T	lower_bound ()
T	range ()
T	quantile (size_t n)

Detailed Description

template<class T>
class mspass::utility::VectorStatistics< T >

Generic object to compute common robust statistics from a vector container of data.

Robust estimators commonly use statistics based on ranked data. This fits naturally with an STL vector container that is by definition sortable by a standard method. This object has methods that return common statistics derived from sorted vector data.

Constructor & Destructor Documentation

VectorStatistics() [1/2]

template<class T >

mspass::utility::VectorStatistics< T >::VectorStatistics

(

std::vector< T >

din

)

Primary constructor.

Parameters

din	is vector container from which statistics are to be derived. Currently assume default sort is used. Probably should have an optional order template parameter for the container.

{
    if(din.size()<=1) throw mspass::utility::MsPASSError(std::string("VectorStatistics constructor:  ")
        + "input vector has insufficient data to compute statistics",
      mspass::utility::ErrorSeverity::Invalid);
    d=din;
    std::sort(d.begin(),d.end());
}

VectorStatistics() [2/2]

template<class T >

mspass::utility::VectorStatistics< T >::VectorStatistics	(	T *	din,
		int	n
	)

Construct from a C style pointer to an array of T.

{
    if(n<=1) throw mspass::utility::MsPASSError(std::string("VectorStatistics constructor:  ")
        + "input vector has insufficient data to compute statistics",
      mspass::utility::ErrorSeverity::Invalid);
    d.reserve(n);
    for(int i=0;i<n;++i) d.push_back(din[i]);
    std::sort(d.begin(),d.end());
}

Member Function Documentation

interquartile()

template<class T >

T mspass::utility::VectorStatistics< T >::interquartile

Return the interquartile (q3/4 - q1/4)

{
    T result;
    T d1_4,d3_4;
    d1_4=this->q1_4();
    d3_4=this->q3_4();
    return(d3_4 - d1_4);
}

lower_bound()

template<class T >

T mspass::utility::VectorStatistics< T >::lower_bound

Return smallest value in the data set.

{
    return(d[0]);
}

mad()

template<class T >

T mspass::utility::VectorStatistics< T >::mad

(

T

center

)

Return the median absolute distance robust measure of spread.

{
    std::vector<T> absdiff;
    int n=d.size();
    int i;
    for(i=0;i<n;++i)
    {
        T diff;
        diff=d[i]-center;
        if(diff<0) diff=-diff;
        absdiff.push_back(diff);
    }
    VectorStatistics<T> result(absdiff);
    return(result.median());
}

References mspass::utility::VectorStatistics< T >::median().

mean()

template<class T >

T mspass::utility::VectorStatistics< T >::mean

Return the mean

{
    T result;
    result=0;
    for(int i=0;i<d.size();++i)
    {
        result += d[i];
    }
    return (result/d.size());
}

median()

template<class T >

T mspass::utility::VectorStatistics< T >::median

Return median

{
    int count=d.size();
    int medposition=count/2;
    if(count%2)
        return(d[medposition]);
    else
        return( (d[medposition]+d[medposition-1])/2 );
}

q1_4()

template<class T >

T mspass::utility::VectorStatistics< T >::q1_4

Return the lower quartile.

{
    int n=d.size();
    double result;
    if(n<4)
        return(d[0]);
    else
    {
        int nover4=(n-1)/4;
        switch(n%4)
        {
        case(0):
            result=static_cast<double>(d[nover4]);
            break;
        case(1):
            result=0.75*static_cast<double>(d[nover4]) + 0.25*static_cast<double>(d[nover4+1]);
            break;
        case(2):
            result=static_cast<double>(d[nover4]) + static_cast<double>(d[nover4+1]);
            result /= 2.0;
            break;
        case(3):
            result=0.25*static_cast<double>(d[nover4]) + 0.75*static_cast<double>(d[nover4+1]);
        }
    }
    return(static_cast<T>(result));
}

q3_4()

template<class T >

T mspass::utility::VectorStatistics< T >::q3_4

Return the upper (3/4) quartile.

{
    int n=d.size();
    double result;
    if(n<4)
        return(d[n-1]);
    else
    {
        int n3_4=3*(n-1)/4;
        switch(n%4)
        {
        case(0):
            result=static_cast<double>(d[n3_4]);
            break;
        case(1):
            result=0.75*static_cast<double>(d[n3_4]) + 0.25*static_cast<double>(d[n3_4+1]);
            break;
        case(2):
            result=static_cast<double>(d[n3_4]) + static_cast<double>(d[n3_4+1]);
            result /= 2.0;
            break;
        case(3):
            result=0.25*static_cast<double>(d[n3_4]) + 0.75*static_cast<double>(d[n3_4+1]);
        }
    }
    return(static_cast<T>(result));
}

quantile()

template<class T >

T mspass::utility::VectorStatistics< T >::quantile

(

size_t

n

)

Return nth value from the sorted data that is the nth 1-quantile. Will throw a MsPASSError if n exceed the data length.

{
    if(n>=d.size())
    {
        std::stringstream ss;
        ss << "VectorStatistics::quantile method:  asked for 1-quantile number "
          << n<<" but data vecotor length is only "<<d.size()<<std::endl;
        throw mspass::utility::MsPASSError(ss.str(),mspass::utility::ErrorSeverity::Invalid);
    }
}

range()

template<class T >

T mspass::utility::VectorStatistics< T >::range

Return full range of data (largest - smallest)

{
    T result;
    result=this->upper_bound();
    result-=this->lower_bound();
    return(result);
}

ssq()

template<class T >

T mspass::utility::VectorStatistics< T >::ssq

Return sum of squares.

{
    T result;
    int i;
    for(i=0;i<d.size();++i) result=d[i]*d[i];
    return(result);
}

upper_bound()

template<class T >

T mspass::utility::VectorStatistics< T >::upper_bound

Return largest value in the data set.

{
    return(d[d.size()-1]);
}

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The documentation for this class was generated from the following file:

• /home/runner/work/mspass/mspass/cxx/include/mspass/utility/VectorStatistics.h