fastmath.stats — generateme/fastmath 2.1.7

Statistics functions.

• Descriptive statistics.
• Correlation / covariance
• Outliers
• Confidence intervals
• Extents
• Effect size
• Student's t-test
• Histogram
• ACF/PACF
• Bootstrap
• Binary measures

All functions are backed by Apache Commons Math or SMILE libraries. All work with Clojure sequences.

Descriptive statistics

All in one function stats-map contains:

• :Size - size of the samples, (count ...)
• :Min - minimum value
• :Max - maximum value
• :Range - range of values
• :Mean - mean/average
• :Median - median, see also: median-3
• :Mode - mode, see also: modes
• :Q1 - first quartile, use: percentile, [[quartile]]
• :Q3 - third quartile, use: percentile, [[quartile]]
• :Total - sum of all samples
• :SD - sample standard deviation
• :Variance - variance
• :MAD - median-absolute-deviation
• :SEM - standard error of mean
• :LAV - lower adjacent value, use: adjacent-values
• :UAV - upper adjacent value, use: adjacent-values
• :IQR - interquartile range, (- q3 q1)
• :LOF - lower outer fence, (- q1 (* 3.0 iqr))
• :UOF - upper outer fence, (+ q3 (* 3.0 iqr))
• :LIF - lower inner fence, (- q1 (* 1.5 iqr))
• :UIF - upper inner fence, (+ q3 (* 1.5 iqr))
• :Outliers - list of outliers, samples which are outside outer fences
• :Kurtosis - kurtosis
• :Skewness - skewness

Note: percentile and [[quartile]] can have 10 different interpolation strategies. See docs

acf clj

(acf data)

(acf data lags)

Calculate acf (autocorrelation function) for given number of lags or a list of lags.

If lags is omitted function returns maximum possible number of lags.

See also acf-ci, pacf, pacf-ci

acf-ci clj

(acf-ci data lags)

(acf-ci data lags alpha)

acf with added confidence interval data.

:cis contains list of calculated ci for every lag.

adjacent-values clj

(adjacent-values vs)

(adjacent-values vs estimation-strategy)

(adjacent-values vs q1 q3)

Lower and upper adjacent values (LAV and UAV).

Let Q1 is 25-percentile and Q3 is 75-percentile. IQR is (- Q3 Q1).

• LAV is smallest value which is greater or equal to the LIF = (- Q1 (* 1.5 IQR)).
• UAV is largest value which is lower or equal to the UIF = (+ Q3 (* 1.5 IQR)).
• third value is a median of samples

Optional estimation-strategy argument can be set to change quantile calculations estimation type. See [[estimation-strategies]].

ameasure clj

(ameasure group1 group2)

Vargha-Delaney A measure for two populations a and b

binary-measures clj

(binary-measures truth prediction)

(binary-measures truth prediction true-value)

Subset of binary measures. See binary-measures-all.

Following keys are returned: [:tp :tn :fp :fn :accuracy :fdr :f-measure :fall-out :precision :recall :sensitivity :specificity :prevalance]

binary-measures-all clj

(binary-measures-all truth prediction)

(binary-measures-all truth prediction true-value)

Collection of binary measures.

• truth - list of ground truth values
• prediction - list of predicted values
• true-value - optional, what is true in truth and prediction

true-value can be one of:

• nil - values are treating as booleans
• any sequence - values from sequence will be treated as true
• map - conversion will be done according to provided map (if there is no correspondin key, value is treated as false)

https://en.wikipedia.org/wiki/Precision_and_recall

bootstrap clj

(bootstrap vs)

(bootstrap vs samples)

(bootstrap vs samples size)

Generate set of samples of given size from provided data.

Default samples is 50, number of size defaults to 1000

bootstrap-ci clj

(bootstrap-ci vs)

(bootstrap-ci vs alpha)

(bootstrap-ci vs alpha samples)

(bootstrap-ci vs alpha samples stat-fn)

Bootstrap method to calculate confidence interval.

Alpha defaults to 0.98, samples to 1000. Last parameter is statistical function used to measure, default: mean.

Returns ci and statistical function value.

ci clj

(ci vs)

(ci vs alpha)

T-student based confidence interval for given data. Alpha value defaults to 0.98.

Last value is mean.

cliffs-delta clj

(cliffs-delta group1 group2)

Cliff's delta effect size for ordinal data.

cohens-d clj

(cohens-d group1 group2)

Cohen's d effect size for two groups, using sqrt of mean of variances as pooled sd

cohens-d-corrected clj

(cohens-d-corrected group1 group2)

Cohen's d corrected for small group size

cohens-f2 clj

(cohens-f2 group1 group2)

(cohens-f2 type group1 group2)

Cohens f2, by default based on eta-sq.

Possible type values are: :eta (default), :omega and :epsilon.

cohens-q clj

(cohens-q r1 r2)

(cohens-q group1 group2a group2b)

(cohens-q group1a group2a group1b group2b)

Comparison of two correlations.

Arity:

• 2 - compare two correlation values
• 3 - compare correlation of group1 and group2a with correlation of group1 and group2b
• 4 - compare correlation of first two arguments with correlation of last two arguments

cohens-w clj

(cohens-w group1 group2)

Cohen's W effect size for discrete data.

correlation clj

(correlation vs1 vs2)

Correlation of two sequences.

count= clj

(count= vs1 vs2)

Count equal values in both seqs.

covariance clj

(covariance vs1 vs2)

Covariance of two sequences.

covariance-matrix clj

(covariance-matrix vss)

Generate covariance matrix from seq of seqs. Row order.

cramers-v clj

(cramers-v group1 group2)

Cramer's V effect size for discrete data.

cramers-v-corrected clj

(cramers-v-corrected group1 group2)

Corrected Cramer's V

demean clj

(demean vs)

Subtract mean from sequence

epsilon-sq clj

(epsilon-sq group1 group2)

Less biased R2

estimate-bins clj

(estimate-bins vs)

(estimate-bins vs bins-or-estimate-method)

Estimate number of bins for histogram.

Possible methods are: :sqrt :sturges :rice :doane :scott :freedman-diaconis (default).

estimation-strategies-list clj

eta-sq clj

(eta-sq group1 group2)

R2

extent clj

(extent vs)

Return extent (min, max, mean) values from sequence

geomean clj

(geomean vs)

Geometric mean for positive values only

glass-delta clj

(glass-delta group1 group2)

Glass's delta effect size for two groups

harmean clj

(harmean vs)

Harmonic mean

hedges-g clj

(hedges-g group1 group2)

Hedges's g effect size for two groups

hedges-g* clj

(hedges-g* group1 group2)

Less biased Hedges's g effect size for two groups

hedges-g-corrected clj

(hedges-g-corrected group1 group2)

Cohen's d corrected for small group size

histogram clj

(histogram vs)

(histogram vs bins-or-estimate-method)

(histogram vs bins [mn mx])

Calculate histogram.

Returns map with keys:

• :size - number of bins
• :step - distance between bins
• :bins - list of pairs of range lower value and number of hits
• :min - min value
• :max - max value
• :samples - number of used samples

For estimation methods check estimate-bins.

iqr clj

(iqr vs)

(iqr vs estimation-strategy)

Interquartile range.

jensen-shannon-divergence clj

(jensen-shannon-divergence vs1 vs2)

Jensen-Shannon divergence of two sequences.

kendall-correlation clj

(kendall-correlation vs1 vs2)

Kendall's correlation of two sequences.

kullback-leibler-divergence clj

(kullback-leibler-divergence vs1 vs2)

Kullback-Leibler divergence of two sequences.

kurtosis clj

(kurtosis vs)

(kurtosis vs typ)

Calculate kurtosis from sequence.

Possible typs: :G2 (default), :g2, :excess or :kurt.

L0 clj

L1 clj

L2 clj

L2sq clj

LInf clj

mad clj

Alias for median-absolute-deviation

mad-extent clj

(mad-extent vs)

-/+ median-absolute-deviation and median

mae clj

(mae vs1 vs2)

Mean absolute error

maximum clj

(maximum vs)

Maximum value from sequence.

mean clj

(mean vs)

Calculate mean of vs

median clj

(median vs)

Calculate median of vs. See median-3.

median-3 clj

(median-3 a b c)

Median of three values. See median.

median-absolute-deviation clj

(median-absolute-deviation vs)

Calculate MAD

minimum clj

(minimum vs)

Minimum value from sequence.

mode clj

(mode vs)

Find the value that appears most often in a dataset vs.

See also modes.

modes clj

(modes vs)

Find the values that appears most often in a dataset vs.

Returns sequence with all most appearing values in increasing order.

See also mode.

moment clj

(moment vs)

(moment vs order)

(moment vs
        order
        {:keys [absolute? center mean?]
         :or {absolute? false center nil mean? true}})

Calculate moment (central or/and absolute) of given order (default: 2).

Additional parameters as a map:

• :absolute? - calculate sum as absolute values (default: false)
• :mean? - returns mean (proper moment) or just sum of differences (default: true)
• :center - value of central (default: nil = mean)

mse clj

(mse vs1 vs2)

Mean squared error

omega-sq clj

(omega-sq group1 group2)

Adjusted R2

outliers clj

(outliers vs)

(outliers vs estimation-strategy)

(outliers vs q1 q3)

Find outliers defined as values outside outer fences.

Let Q1 is 25-percentile and Q3 is 75-percentile. IQR is (- Q3 Q1).

• LIF (Lower Outer Fence) equals (- Q1 (* 1.5 IQR)).
• UIF (Upper Outer Fence) equals (+ Q3 (* 1.5 IQR)).

Returns sequence.

Optional estimation-strategy argument can be set to change quantile calculations estimation type. See [[estimation-strategies]].

pacf clj

(pacf data)

(pacf data lags)

Caluclate pacf (partial autocorrelation function) for given number of lags.

If lags is omitted function returns maximum possible number of lags.

pacf returns also lag 0 (which is 0.0).

See also acf, acf-ci, pacf-ci

pacf-ci clj

(pacf-ci data lags)

(pacf-ci data lags alpha)

pacf with added confidence interval data.

pearson-correlation clj

(pearson-correlation vs1 vs2)

Pearson's correlation of two sequences.

pearson-r clj

(pearson-r group1 group2)

Pearson r correlation coefficient

percentile clj

(percentile vs p)

(percentile vs p estimation-strategy)

Calculate percentile of a vs.

Percentile p is from range 0-100.

See docs.

Optionally you can provide estimation-strategy to change interpolation methods for selecting values. Default is :legacy. See more here

See also quantile.

percentile-bc-extent clj

(percentile-bc-extent vs)

(percentile-bc-extent vs p)

(percentile-bc-extent vs p1 p2)

(percentile-bc-extent vs p1 p2 estimation-strategy)

Return bias corrected percentile range and mean for bootstrap samples. See https://projecteuclid.org/euclid.ss/1032280214

p - calculates extent of bias corrected p and 100-p (default: p=2.5)

Set estimation-strategy to :r7 to get the same result as in R coxed::bca.

percentile-bca-extent clj

(percentile-bca-extent vs)

(percentile-bca-extent vs p)

(percentile-bca-extent vs p1 p2)

(percentile-bca-extent vs p1 p2 estimation-strategy)

(percentile-bca-extent vs p1 p2 accel estimation-strategy)

Return bias corrected percentile range and mean for bootstrap samples. Also accounts for variance variations throught the accelaration parameter. See https://projecteuclid.org/euclid.ss/1032280214

p - calculates extent of bias corrected p and 100-p (default: p=2.5)

Set estimation-strategy to :r7 to get the same result as in R coxed::bca.

percentile-extent clj

(percentile-extent vs)

(percentile-extent vs p)

(percentile-extent vs p1 p2)

(percentile-extent vs p1 p2 estimation-strategy)

Return percentile range and median.

p - calculates extent of p and 100-p (default: p=25)

percentiles clj

(percentiles vs ps)

(percentiles vs ps estimation-strategy)

Calculate percentiles of a vs.

Percentiles are sequence of values from range 0-100.

See docs.

Optionally you can provide estimation-strategy to change interpolation methods for selecting values. Default is :legacy. See more here

See also quantile.

population-stddev clj

(population-stddev vs)

(population-stddev vs u)

Calculate population standard deviation of vs.

See stddev.

population-variance clj

(population-variance vs)

(population-variance vs u)

Calculate population variance of vs.

See variance.

powmean clj

(powmean vs power)

Generalized power mean

psnr clj

(psnr vs1 vs2)

(psnr vs1 vs2 max-value)

Peak signal to noise, max-value is maximum possible value (default: max from vs1 and vs2)

quantile clj

(quantile vs q)

(quantile vs q estimation-strategy)

Calculate quantile of a vs.

Quantile q is from range 0.0-1.0.

See docs for interpolation strategy.

Optionally you can provide estimation-strategy to change interpolation methods for selecting values. Default is :legacy. See more here

See also percentile.

quantiles clj

(quantiles vs qs)

(quantiles vs qs estimation-strategy)

Calculate quantiles of a vs.

Quantilizes is sequence with values from range 0.0-1.0.

See docs for interpolation strategy.

Optionally you can provide estimation-strategy to change interpolation methods for selecting values. Default is :legacy. See more here

See also percentiles.

r2-determination clj

(r2-determination group1 group2)

Coefficient of determination

rmse clj

(rmse vs1 vs2)

Root mean squared error

rss clj

(rss vs1 vs2)

Residual sum of squares

second-moment clj deprecated

sem clj

(sem vs)

Standard error of mean

sem-extent clj

(sem-extent vs)

-/+ sem and mean

skewness clj

(skewness vs)

(skewness vs typ)

Calculate skewness from sequence.

Possible types: :G1 (default), :g1 (:pearson), :b1, :B1 (:yule), :B3, :skew, :mode or :median.

spearman-correlation clj

(spearman-correlation vs1 vs2)

Spearman's correlation of two sequences.

standardize clj

(standardize vs)

Normalize samples to have mean = 0 and stddev = 1.

stats-map clj

(stats-map vs)

(stats-map vs estimation-strategy)

Calculate several statistics of vs and return as map.

Optional estimation-strategy argument can be set to change quantile calculations estimation type. See [[estimation-strategies]].

stddev clj

(stddev vs)

(stddev vs u)

Calculate standard deviation of vs.

See population-stddev.

stddev-extent clj

(stddev-extent vs)

-/+ stddev and mean

sum clj

(sum vs)

Sum of all vs values.

trim clj

(trim vs)

(trim vs quantile)

(trim vs quantile estimation-strategy)

(trim vs low high nan)

Return trimmed data. Trim is done by using quantiles, by default is set to 0.2.

tschuprows-t clj

(tschuprows-t group1 group2)

Tschuprows T effect size for discrete data

ttest-one-sample clj

(ttest-one-sample xs)

(ttest-one-sample xs
                  {:keys [alpha sides mu]
                   :or {alpha 0.05 sides :two-sided mu 0.0}})

One-sample Student's t-test

• alpha - significance level (default: 0.05)
• sides - one of: :two-sided, :one-sided-less (short: :one-sided) or :one-sided-greater
• mu - mean (default: 0.0)

ttest-two-samples clj

(ttest-two-samples xs ys)

(ttest-two-samples
  xs
  ys
  {:keys [alpha sides mu paired? equal-variances?]
   :or {alpha 0.05 sides :two-sided mu 0.0 paired? false equal-variances? false}
   :as params})

Two-sample Student's t-test

• alpha - significance level (default: 0.05)
• sides - one of: :two-sided, :one-sided-less (short: :one-sided) or :one-sided-greater
• mu - mean (default: 0.0)
• paired? - unpaired or paired test, boolean (default: false)
• equal-variances? - unequal or equal variances, boolean (default: false)

variance clj

(variance vs)

(variance vs u)

Calculate variance of vs.

See population-variance.

variation clj

(variation vs)

stddev / mean

winsor clj

(winsor vs)

(winsor vs quantile)

(winsor vs quantile estimation-strategy)

(winsor vs low high nan)

Return winsorized data. Trim is done by using quantiles, by default is set to 0.2.