mo_standard_score::classified_standard_score Interface Reference

Calculates the classified standard score (e.g. classes are months). More...

Public Member Functions
real(sp) function, dimension(size(data, dim=1))	classified_standard_score_sp (data, classes, mask)
real(dp) function, dimension(size(data, dim=1))	classified_standard_score_dp (data, classes, mask)

Detailed Description

Calculates the classified standard score (e.g. classes are months).

In statistics, the standard score is the (signed) number of standard deviations an observation or datum is above the mean. Thus, a positive standard score indicates a datum above the mean, while a negative standard score indicates a datum below the mean. It is a dimensionless quantity obtained by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing (however, "normalizing" can refer to many types of ratios).
Standard scores are also called z-values, z-scores, normal scores, and standardized variables; the use of "Z" is because the normal distribution is also known as the "Z distribution". They are most frequently used to compare a sample to a standard normal deviate, though they can be defined without assumptions of normality (Wikipedia, May 2015).
In this particular case the standard score is calculated for means and standard deviations derived from classes of the time series. Such classes could be for example months. Thus, the output would be a deseasonalized time series.

\[ classified\_standard\_score = \frac{x_i - \mu_{c_{x_i}}}{\sigma_{c_{x_i}}} \]

where \( x_i \) is an element of class \( c_{x_i} \). \( x \) is a population, \( \mu_{c_{x_i}} \) is the mean of all members of a class \( c_{x_i} \) and \( \sigma_{c_{x_i}} \) its standard deviation.

If an optinal mask is given, the calculations are over those locations that correspond to true values in the mask. data can be single or double precision. The result will have the same numerical precision.

Parameters

[in]	integer, dimension(:) :: classes	classes to categorize data (e.g. months)
[in]	real(sp/dp), dimension(:) :: data	data to calculate the standard score for
[in]	logical, dimension(:), optional :: mask	indication which cells to use for calculation If present, only those locations in mask having true values in mask are evaluated.

Returns

real(sp/dp) :: classified_standard_score — classified standard score (e.g. deseasonalized time series)

Author

Matthias Zink

Date

May 2015

Definition at line 165 of file mo_standard_score.f90.

Member Function/Subroutine Documentation

classified_standard_score_dp()

real(dp) function, dimension(size(data, dim = 1)) mo_standard_score::classified_standard_score::classified_standard_score_dp	(	real(dp), dimension(:), intent(in)	data,
		integer, dimension(:), intent(in)	classes,
		logical, dimension(:), intent(in), optional	mask
	)

Definition at line 295 of file mo_standard_score.f90.

classified_standard_score_sp()

real(sp) function, dimension(size(data, dim = 1)) mo_standard_score::classified_standard_score::classified_standard_score_sp	(	real(sp), dimension(:), intent(in)	data,
		integer, dimension(:), intent(in)	classes,
		logical, dimension(:), intent(in), optional	mask
	)

Definition at line 237 of file mo_standard_score.f90.

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

• src/mo_standard_score.f90