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 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.
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