Statistical analysis framework for performing basic statistical analysis of data. The data is analysed in a single pass, when it is pushed to a RunningStat or RunningRegress object.
RunningStat calculates for a single data set
- n (data count)
- min (smallest value)
- max (largest value)
- sum
- mean
- variance
- varianceS (sample variance)
- standardDeviation
- standardDeviationS (sample standard deviation)
- skewness (the third statistical moment)
- kurtosis (the fourth statistical moment)
RunningRegress calculates for two sets of data
- n (data count)
- slope
- intercept
- correlation
Procs are provided to calculate statistics on openArrays.
However, if more than a single statistical calculation is required, it is more efficient to push the data once to a RunningStat object and then call the numerous statistical procs for the RunningStat object:
Example:
import std/stats from std/math import almostEqual template `~=`(a, b: float): bool = almostEqual(a, b) var statistics: RunningStat # must be var statistics.push(@[1.0, 2.0, 1.0, 4.0, 1.0, 4.0, 1.0, 2.0]) doAssert statistics.n == 8 doAssert statistics.mean() ~= 2.0 doAssert statistics.variance() ~= 1.5 doAssert statistics.varianceS() ~= 1.714285714285715 doAssert statistics.skewness() ~= 0.8164965809277261 doAssert statistics.skewnessS() ~= 1.018350154434631 doAssert statistics.kurtosis() ~= -1.0 doAssert statistics.kurtosisS() ~= -0.7000000000000008
Types
RunningRegress = object n*: int ## amount of pushed data x_stats*: RunningStat ## stats for the first set of data y_stats*: RunningStat ## stats for the second set of data ## accumulated data for combined xy
- An accumulator for regression calculations. Source Edit
RunningStat = object n*: int ## amount of pushed data min*, max*, sum*: float ## self-explaining ## statistical moments, mom1 is mean
- An accumulator for statistical data. Source Edit
Procs
proc `$`(a: RunningStat): string {....raises: [], tags: [], forbids: [].}
-
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 `+`(a, b: RunningRegress): RunningRegress {....raises: [], tags: [], forbids: [].}
-
Combines two RunningRegress objects.
Useful when performing parallel analysis of data series and needing to re-combine parallel result sets
Source Edit proc `+`(a, b: RunningStat): RunningStat {....raises: [], tags: [], forbids: [].}
-
Combines two RunningStats.
Useful when performing parallel analysis of data series and needing to re-combine parallel result sets.
Source Edit proc `+=`(a: var RunningRegress; b: RunningRegress) {....raises: [], tags: [], forbids: [].}
- Adds the RunningRegress b to a. Source Edit
proc `+=`(a: var RunningStat; b: RunningStat) {.inline, ...raises: [], tags: [], forbids: [].}
- Adds the RunningStat b to a. Source Edit
proc clear(r: var RunningRegress) {....raises: [], tags: [], forbids: [].}
- Resets r. Source Edit
proc clear(s: var RunningStat) {....raises: [], tags: [], forbids: [].}
- Resets s. Source Edit
proc correlation(r: RunningRegress): float {....raises: [], tags: [], forbids: [].}
- Computes the current correlation of the two data sets pushed into r. Source Edit
proc intercept(r: RunningRegress): float {....raises: [], tags: [], forbids: [].}
- Computes the current intercept of r. Source Edit
proc kurtosis(s: RunningStat): float {....raises: [], tags: [], forbids: [].}
- Computes the current population kurtosis of s. Source Edit
proc kurtosisS(s: RunningStat): float {....raises: [], tags: [], forbids: [].}
- Computes the current sample kurtosis of s. Source Edit
proc mean(s: RunningStat): float {....raises: [], tags: [], forbids: [].}
- Computes the current mean of s. Source Edit
proc push(r: var RunningRegress; x, y: float) {....raises: [], tags: [], forbids: [].}
- Pushes two values x and y for processing. Source Edit
proc push(r: var RunningRegress; x, y: int) {.inline, ...raises: [], tags: [], forbids: [].}
-
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 push(s: var RunningStat; x: float) {....raises: [], tags: [], forbids: [].}
- Pushes a value x for processing. Source Edit
proc push(s: var RunningStat; x: int) {....raises: [], tags: [], forbids: [].}
-
Pushes a value x for processing.
x is simply converted to float and the other push operation is called.
Source Edit proc skewness(s: RunningStat): float {....raises: [], tags: [], forbids: [].}
- Computes the current population skewness of s. Source Edit
proc skewnessS(s: RunningStat): float {....raises: [], tags: [], forbids: [].}
- Computes the current sample skewness of s. Source Edit
proc slope(r: RunningRegress): float {....raises: [], tags: [], forbids: [].}
- Computes the current slope of r. Source Edit
proc standardDeviation(s: RunningStat): float {....raises: [], tags: [], forbids: [].}
- Computes the current population standard deviation of s. Source Edit
proc standardDeviation[T](x: openArray[T]): float
- Computes the population standard deviation of x. Source Edit
proc standardDeviationS(s: RunningStat): float {....raises: [], tags: [], forbids: [].}
- Computes the current sample standard deviation of s. Source Edit
proc standardDeviationS[T](x: openArray[T]): float
- Computes the sample standard deviation of x. Source Edit
proc variance(s: RunningStat): float {....raises: [], tags: [], forbids: [].}
- Computes the current population variance of s. Source Edit