Module stats

Statistical analysis framework for performing basic statistical analysis of data. The data is analysed in a single pass, when a data value is pushed to the RunningStat or RunningRegress objects

RunningStat calculates for a single data set

  • n (data count)
  • min (smallest value)
  • max (largest value)
  • sum
  • mean
  • variance
  • varianceS (sample var)
  • standardDeviation
  • standardDeviationS (sample stddev)
  • skewness (the third statistical moment)
  • kurtosis (the fourth statistical moment)

RunningRegress calculates for two sets of data

  • n
  • slope
  • intercept
  • correlation

Procs have been provided to calculate statistics on arrays and sequences.

However, if more than a single statistical calculation is required, it is more efficient to push the data once to the RunningStat object, and call the numerous statistical procs for the RunningStat object.

var rs: RunningStat
rs.push(MySeqOfData)
rs.mean()
rs.variance()
rs.skewness()
rs.kurtosis()

Types

RunningStat = object
  n*: int                      ## number of pushed data
  min*, max*, sum*: float        ## self-explaining
  mom1, mom2, mom3, mom4: float   ## statistical moments, mom1 is mean
  
an accumulator for statistical data   Source
RunningRegress = object
  n*: int                      ## number of pushed data
  x_stats*: RunningStat        ## stats for first set of data
  y_stats*: RunningStat        ## stats for second set of data
  s_xy: float                  ## accumulated data for combined xy
  
an accumulator for regression calculations   Source

Procs

proc clear(s: var RunningStat) {.raises: [], tags: [].}
reset s   Source
proc push(s: var RunningStat; x: float) {.raises: [], tags: [].}
pushes a value x for processing   Source
proc push(s: var RunningStat; x: int) {.raises: [], tags: [].}

pushes a value x for processing.

x is simply converted to float and the other push operation is called.

  Source
proc push[](s: var RunningStat; x: openArray[float | int])

pushes all values of x for processing.

Int values of x are simply converted to float and the other push operation is called.

  Source
proc mean(s: RunningStat): float {.raises: [], tags: [].}
computes the current mean of s   Source
proc variance(s: RunningStat): float {.raises: [], tags: [].}
computes the current population variance of s   Source
proc varianceS(s: RunningStat): float {.raises: [], tags: [].}
computes the current sample variance of s   Source
proc standardDeviation(s: RunningStat): float {.raises: [], tags: [].}
computes the current population standard deviation of s   Source
proc standardDeviationS(s: RunningStat): float {.raises: [], tags: [].}
computes the current sample standard deviation of s   Source
proc skewness(s: RunningStat): float {.raises: [], tags: [].}
computes the current population skewness of s   Source
proc skewnessS(s: RunningStat): float {.raises: [], tags: [].}
computes the current sample skewness of s   Source
proc kurtosis(s: RunningStat): float {.raises: [], tags: [].}
computes the current population kurtosis of s   Source
proc kurtosisS(s: RunningStat): float {.raises: [], tags: [].}
computes the current sample kurtosis of s   Source
proc `+`(a, b: RunningStat): RunningStat {.raises: [], tags: [].}

combine two RunningStats.

Useful if performing parallel analysis of data series and need to re-combine parallel result sets

  Source
proc `+=`(a: var RunningStat; b: RunningStat) {.inline, raises: [], tags: [].}
add a second RunningStats b to a   Source
proc mean[T](x: openArray[T]): float
computes the mean of x   Source
proc variance[T](x: openArray[T]): float
computes the population variance of x   Source
proc varianceS[T](x: openArray[T]): float
computes the sample variance of x   Source
proc standardDeviation[T](x: openArray[T]): float
computes the population standardDeviation of x   Source
proc standardDeviationS[T](x: openArray[T]): float
computes the sanple standardDeviation of x   Source
proc skewness[T](x: openArray[T]): float
computes the population skewness of x   Source
proc skewnessS[T](x: openArray[T]): float
computes the sample skewness of x   Source
proc kurtosis[T](x: openArray[T]): float
computes the population kurtosis of x   Source
proc kurtosisS[T](x: openArray[T]): float
computes the sample kurtosis of x   Source
proc clear(r: var RunningRegress) {.raises: [], tags: [].}
reset r   Source
proc push(r: var RunningRegress; x, y: float) {.raises: [], tags: [].}
pushes two values x and y for processing   Source
proc push(r: var RunningRegress; x, y: int) {.inline, raises: [], tags: [].}

pushes two values x and y for processing.

x and y are converted to float and the other push operation is called.

  Source
proc push[](r: var RunningRegress; x, y: openArray[float | int])
pushes two sets of values x and y for processing.   Source
proc slope(r: RunningRegress): float {.raises: [], tags: [].}
computes the current slope of r   Source
proc intercept(r: RunningRegress): float {.raises: [], tags: [].}
computes the current intercept of r   Source
proc correlation(r: RunningRegress): float {.raises: [], tags: [].}
computes the current correlation of the two data sets pushed into r   Source
proc `+`(a, b: RunningRegress): RunningRegress {.raises: [], tags: [].}

combine two RunningRegress objects.

Useful if performing parallel analysis of data series and need to re-combine parallel result sets

  Source
proc `+=`(a: var RunningRegress; b: RunningRegress) {.raises: [], tags: [].}
add RunningRegress b to a   Source