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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()

math

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 Edit
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 Edit

Procs

proc clear(s: var RunningStat) {...}{.raises: [], tags: [].}
reset s   Source Edit
proc push(s: var RunningStat; x: float) {...}{.raises: [], tags: [].}
pushes a value x for processing   Source Edit
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 Edit
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 Edit
proc mean(s: RunningStat): float {...}{.raises: [], tags: [].}
computes the current mean of s   Source Edit
proc variance(s: RunningStat): float {...}{.raises: [], tags: [].}
computes the current population variance of s   Source Edit
proc varianceS(s: RunningStat): float {...}{.raises: [], tags: [].}
computes the current sample variance of s   Source Edit
proc standardDeviation(s: RunningStat): float {...}{.raises: [], tags: [].}
computes the current population standard deviation of s   Source Edit
proc standardDeviationS(s: RunningStat): float {...}{.raises: [], tags: [].}
computes the current sample standard deviation of s   Source Edit
proc skewness(s: RunningStat): float {...}{.raises: [], tags: [].}
computes the current population skewness of s   Source Edit
proc skewnessS(s: RunningStat): float {...}{.raises: [], tags: [].}
computes the current sample skewness of s   Source Edit
proc kurtosis(s: RunningStat): float {...}{.raises: [], tags: [].}
computes the current population kurtosis of s   Source Edit
proc kurtosisS(s: RunningStat): float {...}{.raises: [], tags: [].}
computes the current sample kurtosis of s   Source Edit
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 Edit
proc `+=`(a: var RunningStat; b: RunningStat) {...}{.inline, raises: [], tags: [].}
add a second RunningStats b to a   Source Edit
proc `\$`(a: RunningStat): string {...}{.raises: [], tags: [].}
produces a string representation of the RunningStat. The exact format is currently unspecified and subject to change. Currently it contains:
• the number of probes
• min, max values
• sum, mean and standard deviation.
Source Edit
proc mean[T](x: openArray[T]): float
computes the mean of x   Source Edit
proc variance[T](x: openArray[T]): float
computes the population variance of x   Source Edit
proc varianceS[T](x: openArray[T]): float
computes the sample variance of x   Source Edit
proc standardDeviation[T](x: openArray[T]): float
computes the population standardDeviation of x   Source Edit
proc standardDeviationS[T](x: openArray[T]): float
computes the sample standardDeviation of x   Source Edit
proc skewness[T](x: openArray[T]): float
computes the population skewness of x   Source Edit
proc skewnessS[T](x: openArray[T]): float
computes the sample skewness of x   Source Edit
proc kurtosis[T](x: openArray[T]): float
computes the population kurtosis of x   Source Edit
proc kurtosisS[T](x: openArray[T]): float
computes the sample kurtosis of x   Source Edit
proc clear(r: var RunningRegress) {...}{.raises: [], tags: [].}
reset r   Source Edit
proc push(r: var RunningRegress; x, y: float) {...}{.raises: [], tags: [].}
pushes two values x and y for processing   Source Edit
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 Edit
proc push(r: var RunningRegress; x, y: openArray[float | int])
pushes two sets of values x and y for processing.   Source Edit
proc slope(r: RunningRegress): float {...}{.raises: [], tags: [].}
computes the current slope of r   Source Edit
proc intercept(r: RunningRegress): float {...}{.raises: [], tags: [].}
computes the current intercept of r   Source Edit
proc correlation(r: RunningRegress): float {...}{.raises: [], tags: [].}
computes the current correlation of the two data sets pushed into r   Source Edit
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 Edit
proc `+=`(a: var RunningRegress; b: RunningRegress) {...}{.raises: [], tags: [].}
add RunningRegress b to a   Source Edit