fastmath.stats — generateme/fastmath 3.0.0-alpha1

Statistics functions.

• Descriptive statistics.
• Correlation / covariance
• Outliers
• Confidence intervals
• Extents
• Effect size
• Tests
• Histogram
• ACF/PACF
• Bootstrap (see fastmath.stats.bootstrap)
• Binary measures

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

->confusion-matrix clj

(->confusion-matrix confusion-matrix)

(->confusion-matrix actual prediction)

(->confusion-matrix actual prediction encode-true)

(->confusion-matrix tp fn fp tn)

Convert input to confusion matrix

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)

(acf-ci data lags)

(acf-ci data lags alpha)

acf with added confidence interval data.

:cis contains list of calculated ci for every lag.

ad-test-one-sample clj

(ad-test-one-sample xs)

(ad-test-one-sample xs distribution-or-ys)

(ad-test-one-sample xs
                    distribution-or-ys
                    {:keys [sides kernel bandwidth]
                     :or {sides :one-sided-greater kernel :gaussian}})

Anderson-Darling test

adjacent-values clj

(adjacent-values vs)

(adjacent-values vs estimation-strategy)

(adjacent-values vs q1 q3 m)

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])

(ameasure group1 group2)

Vargha-Delaney A measure for two populations a and b

binary-measures clj

(binary-measures confusion-matrix)

(binary-measures actual prediction)

(binary-measures actual prediction true-value)

(binary-measures tp fn fp tn)

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 :prevalence]

binary-measures-all clj

(binary-measures-all confusion-matrix)

(binary-measures-all actual prediction)

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

(binary-measures-all tp fn fp tn)

Collection of binary measures.

Arguments:

• confusion-matrix - either map or sequence with [:tp :fn :fp :tn] values

or

• actual - list of ground truth values
• prediction - list of predicted values
• true-value - optional, true/false encoding, 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)
• any predicate

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

binomial-ci clj

(binomial-ci number-of-successes number-of-trials)

(binomial-ci number-of-successes number-of-trials method)

(binomial-ci number-of-successes number-of-trials method alpha)

Return confidence interval for a binomial distribution.

Possible methods are:

• :asymptotic (normal aproximation, based on central limit theorem), default
• :agresti-coull
• :clopper-pearson
• :wilson
• :prop.test - one sample proportion test
• :cloglog
• :logit
• :probit
• :arcsine
• :all - apply all methods and return a map of triplets

Default alpha is 0.05

Returns a triple [lower ci, upper ci, p=successes/trials]

binomial-ci-methods clj

binomial-test clj

(binomial-test xs)

(binomial-test xs maybe-params)

(binomial-test number-of-successes
               number-of-trials
               {:keys [alpha p ci-method sides]
                :or {alpha 0.05 p 0.5 ci-method :asymptotic sides :two-sided}})

Binomial test

• alpha - significance level (default: 0.05)
• sides - one of: :two-sided (default), :one-sided-less (short: :one-sided) or :one-sided-greater
• ci-method - see binomial-ci-methods
• p - tested probability

bootstrap clj deprecated

(bootstrap vs)

(bootstrap vs samples)

(bootstrap vs samples size)

Generate set of samples of given size from provided data.

Default samples is 200, number of size defaults to sample size.

bootstrap-ci clj deprecated

(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.

brown-forsythe-test clj

(brown-forsythe-test xss)

(brown-forsythe-test xss params)

chisq-test clj

(chisq-test contingency-table-or-xs)

(chisq-test contingency-table-or-xs params)

Chi square test, a power divergence test for lambda 1.0

Power divergence test.

First argument should be one of:

• contingency table
• sequence of counts (for goodness of fit)
• sequence of data (for goodness of fit against distribution)

For goodness of fit there are two options:

• comparison of observed counts vs expected probabilities or weights (:p)
• comparison of data against given distribution (:p), in this case histogram from data is created and compared to distribution PDF in bins ranges. Use :bins option to control histogram creation.

Options are:

• :lambda - test type:
  • 1.0 - chisq-test
  • 0.0 - multinomial-likelihood-ratio-test
  • -1.0 - minimum-discrimination-information-test
  • -2.0 - neyman-modified-chisq-test
  • -0.5 - freeman-tukey-test
  • 2/3 - cressie-read-test - default
• :p - probabilites, weights or distribution object.
• :alpha - significance level (default: 0.05)
• :ci-sides - confidence interval sides (default: :two-sided)
• :sides - p-value sides (:two-sided, :one-side-greater - default, :one-side-less)
• :bootstrap-samples - number of samples to estimate confidence intervals (default: 1000)
• :ddof - delta degrees of freedom, adjustment for dof (default: 0.0)
• :bins - number of bins or estimator name for histogram

ci clj

(ci vs)

(ci vs alpha)

T-student based confidence interval for given data. Alpha value defaults to 0.05.

Last value is mean.

cliffs-delta clj

(cliffs-delta [group1 group2])

(cliffs-delta group1 group2)

Cliff's delta effect size for ordinal data.

coefficient-matrix clj

(coefficient-matrix vss)

(coefficient-matrix vss measure-fn)

(coefficient-matrix vss measure-fn symmetric?)

Generate coefficient (correlation, covariance, any two arg function) matrix from seq of seqs. Row order.

Default method: pearson-correlation

cohens-d clj

(cohens-d [group1 group2])

(cohens-d group1 group2)

(cohens-d group1 group2 method)

Cohen's d effect size for two groups

cohens-d-corrected clj

(cohens-d-corrected [group1 group2])

(cohens-d-corrected group1 group2)

(cohens-d-corrected group1 group2 method)

Cohen's d corrected for small group size

cohens-f clj

(cohens-f [group1 group2])

(cohens-f group1 group2)

(cohens-f group1 group2 type)

Cohens f, sqrt of Cohens f2.

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

cohens-f2 clj

(cohens-f2 [group1 group2])

(cohens-f2 group1 group2)

(cohens-f2 group1 group2 type)

Cohens f2, by default based on eta-sq.

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

cohens-kappa clj

(cohens-kappa contingency-table)

(cohens-kappa group1 group2)

Cohens kappa

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-u2 clj

(cohens-u2 [group1 group2])

(cohens-u2 group1 group2)

(cohens-u2 group1 group2 estimation-strategy)

Cohen's U2, the proportion of one of the groups that exceeds the same proportion in the other group.

cohens-u3 clj

(cohens-u3 [group1 group2])

(cohens-u3 group1 group2)

(cohens-u3 group1 group2 estimation-strategy)

Cohen's U3, the proportion of the second group that is smaller than the median of the first group.

cohens-w clj

(cohens-w contingency-table)

(cohens-w group1 group2)

Cohen's W effect size for discrete data.

contingency-2x2-measures clj

(contingency-2x2-measures & args)

contingency-2x2-measures-all clj

(contingency-2x2-measures-all map-or-seq)

(contingency-2x2-measures-all [a b] [c d])

(contingency-2x2-measures-all a b c d)

contingency-table clj

(contingency-table & seqs)

Returns frequencies map of tuples built from seqs.

contingency-table->marginals clj

(contingency-table->marginals ct)

correlation clj

(correlation [vs1 vs2])

(correlation vs1 vs2)

Correlation of two sequences.

correlation-matrix clj

(correlation-matrix vss)

(correlation-matrix vss measure)

Generate correlation matrix from seq of seqs. Row order.

Possible measures: :pearson (default), :kendall, :spearman.

count= clj

(count= [vs1 vs2-or-val])

(count= vs1 vs2-or-val)

Count equal values in both seqs. Same as L0

covariance clj

(covariance [vs1 vs2])

(covariance vs1 vs2)

Covariance of two sequences.

covariance-matrix clj

(covariance-matrix vss)

Generate covariance matrix from seq of seqs. Row order.

cramers-c clj

(cramers-c contingency-table)

(cramers-c group1 group2)

Cramer's C effect size for discrete data.

cramers-v clj

(cramers-v contingency-table)

(cramers-v group1 group2)

Cramer's V effect size for discrete data.

cramers-v-corrected clj

(cramers-v-corrected contingency-table)

(cramers-v-corrected group1 group2)

Corrected Cramer's V

cressie-read-test clj

(cressie-read-test contingency-table-or-xs)

(cressie-read-test contingency-table-or-xs params)

Cressie-Read test, a power divergence test for lambda 2/3

Power divergence test.

First argument should be one of:

• contingency table
• sequence of counts (for goodness of fit)
• sequence of data (for goodness of fit against distribution)

For goodness of fit there are two options:

• comparison of observed counts vs expected probabilities or weights (:p)
• comparison of data against given distribution (:p), in this case histogram from data is created and compared to distribution PDF in bins ranges. Use :bins option to control histogram creation.

Options are:

• :lambda - test type:
  • 1.0 - chisq-test
  • 0.0 - multinomial-likelihood-ratio-test
  • -1.0 - minimum-discrimination-information-test
  • -2.0 - neyman-modified-chisq-test
  • -0.5 - freeman-tukey-test
  • 2/3 - cressie-read-test - default
• :p - probabilites, weights or distribution object.
• :alpha - significance level (default: 0.05)
• :ci-sides - confidence interval sides (default: :two-sided)
• :sides - p-value sides (:two-sided, :one-side-greater - default, :one-side-less)
• :bootstrap-samples - number of samples to estimate confidence intervals (default: 1000)
• :ddof - delta degrees of freedom, adjustment for dof (default: 0.0)
• :bins - number of bins or estimator name for histogram

demean clj

(demean vs)

Subtract mean from sequence

dissimilarity clj

(dissimilarity method P-observed Q-expected)

(dissimilarity method
               P-observed
               Q-expected
               {:keys [bins probabilities? epsilon log-base power remove-zeros?]
                :or
                  {probabilities? true epsilon 1.0E-6 log-base m/E power 2.0}})

Various PDF distance between two histograms (frequencies) or probabilities.

Q can be a distribution object. Then, histogram will be created out of P.

Arguments:

• method - distance method
• P-observed - frequencies, probabilities or actual data (when Q is a distribution of :bins is set)
• Q-expected - frequencies, probabilities or distribution object (when P is a data or :bins is set)

Options:

• :probabilities? - should P/Q be converted to a probabilities, default: true.
• :epsilon - small number which replaces 0.0 when division or logarithm is used`
• :log-base - base for logarithms, default: e
• :power - exponent for :minkowski distance, default: 2.0
• :bins - number of bins or bins estimation method, see histogram.

The list of methods: :euclidean, :city-block, :manhattan, :chebyshev, :minkowski, :sorensen, :gower, :soergel, :kulczynski, :canberra, :lorentzian, :non-intersection, :wave-hedges, :czekanowski, :motyka, :tanimoto, :jaccard, :dice, :bhattacharyya, :hellinger, :matusita, :squared-chord, :euclidean-sq, :squared-euclidean, :pearson-chisq, :chisq, :neyman-chisq, :squared-chisq, :symmetric-chisq, :divergence, :clark, :additive-symmetric-chisq, :kullback-leibler, :jeffreys, :k-divergence, :topsoe, :jensen-shannon, :jensen-difference, :taneja, :kumar-johnson, :avg

See more: Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions by Sung-Hyuk Cha

durbin-watson clj

(durbin-watson rs)

Lag-1 Autocorrelation test for residuals

epsilon-sq clj

(epsilon-sq [group1 group2])

(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).

The number returned is not higher than number of samples.

estimation-strategies-list clj

List of estimation strategies for percentile/quantile functions.

eta-sq clj

(eta-sq [group1 group2])

(eta-sq group1 group2)

R2, coefficient of determination

extent clj

(extent vs)

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

f-test clj

(f-test xs ys)

(f-test xs ys {:keys [sides alpha] :or {sides :two-sided alpha 0.05}})

Variance F-test of two samples.

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

fligner-killeen-test clj

(fligner-killeen-test xss)

(fligner-killeen-test xss {:keys [sides] :or {sides :one-sided-greater}})

freeman-tukey-test clj

(freeman-tukey-test contingency-table-or-xs)

(freeman-tukey-test contingency-table-or-xs params)

Freeman-Tukey test, a power divergence test for lambda -0.5

Power divergence test.

First argument should be one of:

• contingency table
• sequence of counts (for goodness of fit)
• sequence of data (for goodness of fit against distribution)

For goodness of fit there are two options:

• comparison of observed counts vs expected probabilities or weights (:p)
• comparison of data against given distribution (:p), in this case histogram from data is created and compared to distribution PDF in bins ranges. Use :bins option to control histogram creation.

Options are:

• :lambda - test type:
  • 1.0 - chisq-test
  • 0.0 - multinomial-likelihood-ratio-test
  • -1.0 - minimum-discrimination-information-test
  • -2.0 - neyman-modified-chisq-test
  • -0.5 - freeman-tukey-test
  • 2/3 - cressie-read-test - default
• :p - probabilites, weights or distribution object.
• :alpha - significance level (default: 0.05)
• :ci-sides - confidence interval sides (default: :two-sided)
• :sides - p-value sides (:two-sided, :one-side-greater - default, :one-side-less)
• :bootstrap-samples - number of samples to estimate confidence intervals (default: 1000)
• :ddof - delta degrees of freedom, adjustment for dof (default: 0.0)
• :bins - number of bins or estimator name for histogram

geomean clj

(geomean vs)

(geomean vs weights)

Geometric mean for positive values only with optional weights

glass-delta clj

(glass-delta [group1 group2])

(glass-delta group1 group2)

Glass's delta effect size for two groups

harmean clj

(harmean vs)

(harmean vs weights)

Harmonic mean with optional weights

hedges-g clj

(hedges-g [group1 group2])

(hedges-g group1 group2)

Hedges's g effect size for two groups

hedges-g* clj

(hedges-g* [group1 group2])

(hedges-g* group1 group2)

Less biased Hedges's g effect size for two groups, J term correction.

hedges-g-corrected clj

(hedges-g-corrected [group1 group2])

(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-or-estimate-method [mn mx])

(histogram vs bins-or-estimate-method mn mx)

Calculate histogram.

Estimation method can be a number, named method: :sqrt :sturges :rice :doane :scott :freedman-diaconis (default) or a sequence of points used as intervals. In the latter case or when mn and mx values are provided - data will be filtered to fit in desired interval(s).

Returns map with keys:

• :size - number of bins
• :step - average distance between bins
• :bins - seq of pairs of range lower value and number of elements
• :min - min value
• :max - max value
• :samples - number of used samples
• :frequencies - a map containing counts for bin's average
• :intervals - intervals used to create bins
• :bins-maps - seq of maps containing:
  • :min - lower bound
  • :max - upper bound
  • :step - actual distance between bins
  • :count - number of elements
  • :avg - average value
  • :probability - probability for bin

If difference between min and max values is 0, number of bins is set to 1.

hpdi-extent clj

(hpdi-extent vs)

(hpdi-extent vs size)

Higher Posterior Density interval + median.

size parameter is the target probability content of the interval.

inner-fence-extent clj

(inner-fence-extent vs)

(inner-fence-extent vs estimation-strategy)

Returns LIF, UIF and median

iqr clj

(iqr vs)

(iqr vs estimation-strategy)

Interquartile range.

jarque-bera-test clj

(jarque-bera-test xs)

(jarque-bera-test xs params)

(jarque-bera-test xs skew kurt {:keys [sides] :or {sides :one-sided-greater}})

Goodness of fit test whether skewness and kurtosis of data match normal distribution

jensen-shannon-divergence clj deprecated

(jensen-shannon-divergence [vs1 vs2])

(jensen-shannon-divergence vs1 vs2)

Jensen-Shannon divergence of two sequences.

kendall-correlation clj

(kendall-correlation [vs1 vs2])

(kendall-correlation vs1 vs2)

Kendall's correlation of two sequences.

kruskal-test clj

(kruskal-test xss)

(kruskal-test xss {:keys [sides] :or {sides :right}})

Kruskal-Wallis rank sum test.

ks-test-one-sample clj

(ks-test-one-sample xs)

(ks-test-one-sample xs distribution-or-ys)

(ks-test-one-sample xs
                    distribution-or-ys
                    {:keys [sides kernel bandwidth distinct?]
                     :or {sides :two-sided kernel :gaussian distinct? true}})

One sample Kolmogorov-Smirnov test

ks-test-two-samples clj

(ks-test-two-samples xs ys)

(ks-test-two-samples xs
                     ys
                     {:keys [sides distinct?]
                      :or {sides :two-sided distinct? true}})

Two samples Kolmogorov-Smirnov test

kullback-leibler-divergence clj deprecated

(kullback-leibler-divergence [vs1 vs2])

(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 (or :excess), :geary, ,:crow, :moors, :hogg or :kurt.

kurtosis-test clj

(kurtosis-test xs)

(kurtosis-test xs params)

(kurtosis-test xs kurt {:keys [sides type] :or {sides :two-sided type :kurt}})

Normality test for kurtosis

L0 clj

Count equal values in both seqs. Same as [[count==]]

L1 clj

(L1 [vs1 vs2-or-val])

(L1 vs1 vs2-or-val)

Manhattan distance

L2 clj

(L2 [vs1 vs2-or-val])

(L2 vs1 vs2-or-val)

Euclidean distance

L2sq clj

(L2sq [vs1 vs2-or-val])

(L2sq vs1 vs2-or-val)

Squared euclidean distance

levene-test clj

(levene-test xss)

(levene-test xss
             {:keys [sides statistic scorediff]
              :or {sides :one-sided-greater statistic mean scorediff abs}})

LInf clj

(LInf [vs1 vs2-or-val])

(LInf vs1 vs2-or-val)

Chebyshev distance

mad clj

Alias for median-absolute-deviation

mad-extent clj

(mad-extent vs)

-/+ median-absolute-deviation and median

mae clj

(mae [vs1 vs2-or-val])

(mae vs1 vs2-or-val)

Mean absolute error

mape clj

(mape [vs1 vs2-or-val])

(mape vs1 vs2-or-val)

Mean absolute percentage error

maximum clj

(maximum vs)

Maximum value from sequence.

mcc clj

(mcc ct)

(mcc group1 group2)

Matthews correlation coefficient also known as phi coefficient.

me clj

(me [vs1 vs2-or-val])

(me vs1 vs2-or-val)

Mean error

mean clj

(mean vs)

(mean vs weights)

Calculate mean of vs with optional weights.

mean-absolute-deviation clj

(mean-absolute-deviation vs)

(mean-absolute-deviation vs center)

Calculate mean absolute deviation

means-ratio clj

(means-ratio [group1 group2])

(means-ratio group1 group2)

(means-ratio group1 group2 adjusted?)

Means ratio

means-ratio-corrected clj

(means-ratio-corrected [group1 group2])

(means-ratio-corrected group1 group2)

Bias correced means ratio

median clj

(median vs)

(median vs estimation-strategy)

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)

(median-absolute-deviation vs center)

(median-absolute-deviation vs center estimation-strategy)

Calculate MAD

minimum clj

(minimum vs)

Minimum value from sequence.

minimum-discrimination-information-test clj

(minimum-discrimination-information-test contingency-table-or-xs)

(minimum-discrimination-information-test contingency-table-or-xs params)

Minimum discrimination information test, a power divergence test for lambda -1.0

Power divergence test.

First argument should be one of:

• contingency table
• sequence of counts (for goodness of fit)
• sequence of data (for goodness of fit against distribution)

For goodness of fit there are two options:

• comparison of observed counts vs expected probabilities or weights (:p)
• comparison of data against given distribution (:p), in this case histogram from data is created and compared to distribution PDF in bins ranges. Use :bins option to control histogram creation.

Options are:

• :lambda - test type:
  • 1.0 - chisq-test
  • 0.0 - multinomial-likelihood-ratio-test
  • -1.0 - minimum-discrimination-information-test
  • -2.0 - neyman-modified-chisq-test
  • -0.5 - freeman-tukey-test
  • 2/3 - cressie-read-test - default
• :p - probabilites, weights or distribution object.
• :alpha - significance level (default: 0.05)
• :ci-sides - confidence interval sides (default: :two-sided)
• :sides - p-value sides (:two-sided, :one-side-greater - default, :one-side-less)
• :bootstrap-samples - number of samples to estimate confidence intervals (default: 1000)
• :ddof - delta degrees of freedom, adjustment for dof (default: 0.0)
• :bins - number of bins or estimator name for histogram

mode clj

(mode vs)

(mode vs method)

(mode vs method opts)

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

For sample from continuous distribution, three algorithms are possible:

• :histogram - calculated from histogram
• :kde - calculated from KDE
• :pearson - mode = mean-3(median-mean)
• :default - discrete mode

Histogram accepts optional :bins (see histogram). KDE method accepts :kde for kernel name (default :gaussian) and :bandwidth (auto). Pearson can accept :estimation-strategy for median.

See also modes.

modes clj

(modes vs)

(modes vs method)

(modes vs method opts)

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

Returns sequence with all most appearing values in increasing order.

See also mode.

modified-power-transformation clj

(modified-power-transformation xs)

(modified-power-transformation xs lambda)

(modified-power-transformation xs lambda alpha)

Modified power transformation (Box-Cox transformation) of data.

There is no scaling by geometric mean.

Arguments:

• lambda - power parameter (default: 0.0)
• alpha - shift parameter (optional)

moment clj

(moment vs)

(moment vs order)

(moment vs order {:keys [absolute? center mean? normalize?] :or {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 center (default: nil = mean)
• :normalize? - apply normalization by standard deviation to the order power

mse clj

(mse [vs1 vs2-or-val])

(mse vs1 vs2-or-val)

Mean squared error

multinomial-likelihood-ratio-test clj

(multinomial-likelihood-ratio-test contingency-table-or-xs)

(multinomial-likelihood-ratio-test contingency-table-or-xs params)

Multinomial likelihood ratio test, a power divergence test for lambda 0.0

Power divergence test.

First argument should be one of:

• contingency table
• sequence of counts (for goodness of fit)
• sequence of data (for goodness of fit against distribution)

For goodness of fit there are two options:

• comparison of observed counts vs expected probabilities or weights (:p)
• comparison of data against given distribution (:p), in this case histogram from data is created and compared to distribution PDF in bins ranges. Use :bins option to control histogram creation.

Options are:

• :lambda - test type:
  • 1.0 - chisq-test
  • 0.0 - multinomial-likelihood-ratio-test
  • -1.0 - minimum-discrimination-information-test
  • -2.0 - neyman-modified-chisq-test
  • -0.5 - freeman-tukey-test
  • 2/3 - cressie-read-test - default
• :p - probabilites, weights or distribution object.
• :alpha - significance level (default: 0.05)
• :ci-sides - confidence interval sides (default: :two-sided)
• :sides - p-value sides (:two-sided, :one-side-greater - default, :one-side-less)
• :bootstrap-samples - number of samples to estimate confidence intervals (default: 1000)
• :ddof - delta degrees of freedom, adjustment for dof (default: 0.0)
• :bins - number of bins or estimator name for histogram

neyman-modified-chisq-test clj

(neyman-modified-chisq-test contingency-table-or-xs)

(neyman-modified-chisq-test contingency-table-or-xs params)

Neyman modifield chi square test, a power divergence test for lambda -2.0

Power divergence test.

First argument should be one of:

• contingency table
• sequence of counts (for goodness of fit)
• sequence of data (for goodness of fit against distribution)

For goodness of fit there are two options:

• comparison of observed counts vs expected probabilities or weights (:p)
• comparison of data against given distribution (:p), in this case histogram from data is created and compared to distribution PDF in bins ranges. Use :bins option to control histogram creation.

Options are:

• :lambda - test type:
  • 1.0 - chisq-test
  • 0.0 - multinomial-likelihood-ratio-test
  • -1.0 - minimum-discrimination-information-test
  • -2.0 - neyman-modified-chisq-test
  • -0.5 - freeman-tukey-test
  • 2/3 - cressie-read-test - default
• :p - probabilites, weights or distribution object.
• :alpha - significance level (default: 0.05)
• :ci-sides - confidence interval sides (default: :two-sided)
• :sides - p-value sides (:two-sided, :one-side-greater - default, :one-side-less)
• :bootstrap-samples - number of samples to estimate confidence intervals (default: 1000)
• :ddof - delta degrees of freedom, adjustment for dof (default: 0.0)
• :bins - number of bins or estimator name for histogram

normality-test clj

(normality-test xs)

(normality-test xs params)

(normality-test xs skew kurt {:keys [sides] :or {sides :one-sided-greater}})

Normality test based on skewness and kurtosis

omega-sq clj

(omega-sq [group1 group2])

(omega-sq group1 group2)

(omega-sq group1 group2 degrees-of-freedom)

Adjusted R2

one-way-anova-test clj

(one-way-anova-test xss)

(one-way-anova-test xss {:keys [sides] :or {sides :one-sided-greater}})

outer-fence-extent clj

(outer-fence-extent vs)

(outer-fence-extent vs estimation-strategy)

Returns LOF, UOF and median

outliers clj

(outliers vs)

(outliers vs estimation-strategy)

(outliers vs q1 q3)

Find outliers defined as values outside inner fences.

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

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

Returns a sequence of outliers.

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

p-overlap clj

(p-overlap [group1 group2])

(p-overlap group1 group2)

(p-overlap group1
           group2
           {:keys [kde bandwidth min-iterations steps]
            :or {kde :gaussian min-iterations 3 steps 500}})

Overlapping index, kernel density approximation

p-value clj

(p-value stat)

(p-value distribution stat)

(p-value distribution stat sides)

Calculate p-value for given distribution (default: N(0,1)), stat and sides (one of :two-sided, :one-sided-greater or :one-sided-less/:one-sided).

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)

(pacf-ci data lags)

(pacf-ci data lags alpha)

pacf with added confidence interval data.

pearson-correlation clj

(pearson-correlation [vs1 vs2])

(pearson-correlation vs1 vs2)

Pearson's correlation of two sequences.

pearson-r clj

(pearson-r [group1 group2])

(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)

(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.

pi clj

(pi vs)

(pi vs size)

(pi vs size estimation-strategy)

Returns PI as a map, quantile intervals based on interval size.

Quantiles are (1-size)/2 and 1-(1-size)/2

pi-extent clj

(pi-extent vs)

(pi-extent vs size)

(pi-extent vs size estimation-strategy)

Returns PI extent, quantile intervals based on interval size + median.

Quantiles are (1-size)/2 and 1-(1-size)/2

pooled-stddev clj

(pooled-stddev groups)

(pooled-stddev groups method)

Calculate pooled standard deviation for samples and method

pooled-variance clj

(pooled-variance groups)

(pooled-variance groups method)

Calculate pooled variance for samples and method.

Methods:

• :unbiased - sqrt of weighted average of variances (default)
• :biased - biased version of :unbiased
• :avg - sqrt of average of variances

population-stddev clj

(population-stddev vs)

(population-stddev vs mu)

Calculate population standard deviation of vs.

See stddev.

population-variance clj

(population-variance vs)

(population-variance vs mu)

Calculate population variance of vs.

See variance.

population-wstddev clj

(population-wstddev vs weights)

Calculate population weighted standard deviation of vs

population-wvariance clj

(population-wvariance vs freqs)

Calculate population weighted variance of vs.

power-divergence-test clj

(power-divergence-test contingency-table-or-xs)

(power-divergence-test contingency-table-or-xs
                       {:keys [lambda ci-sides sides p alpha bootstrap-samples
                               ddof bins]
                        :or {lambda m/TWO_THIRD
                             sides :one-sided-greater
                             ci-sides :two-sided
                             alpha 0.05
                             bootstrap-samples 1000
                             ddof 0}})

Power divergence test.

First argument should be one of:

• contingency table
• sequence of counts (for goodness of fit)
• sequence of data (for goodness of fit against distribution)

For goodness of fit there are two options:

• comparison of observed counts vs expected probabilities or weights (:p)
• comparison of data against given distribution (:p), in this case histogram from data is created and compared to distribution PDF in bins ranges. Use :bins option to control histogram creation.

Options are:

• :lambda - test type:
  • 1.0 - chisq-test
  • 0.0 - multinomial-likelihood-ratio-test
  • -1.0 - minimum-discrimination-information-test
  • -2.0 - neyman-modified-chisq-test
  • -0.5 - freeman-tukey-test
  • 2/3 - cressie-read-test - default
• :p - probabilites, weights or distribution object.
• :alpha - significance level (default: 0.05)
• :ci-sides - confidence interval sides (default: :two-sided)
• :sides - p-value sides (:two-sided, :one-side-greater - default, :one-side-less)
• :bootstrap-samples - number of samples to estimate confidence intervals (default: 1000)
• :ddof - delta degrees of freedom, adjustment for dof (default: 0.0)
• :bins - number of bins or estimator name for histogram

power-transformation clj

(power-transformation xs)

(power-transformation xs lambda)

(power-transformation xs lambda alpha)

Power transformation of data.

All values should be positive.

Arguments:

• lambda - power parameter (default: 0.0)
• alpha - shift parameter (optional)

powmean clj

(powmean vs power)

(powmean vs weights power)

Generalized power mean

psnr clj

(psnr [vs1 vs2-or-val])

(psnr vs1 vs2-or-val)

(psnr vs1 vs2-or-val 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.

quantile-extent clj

(quantile-extent vs)

(quantile-extent vs q)

(quantile-extent vs q1 q2)

(quantile-extent vs q1 q2 estimation-strategy)

Return quantile range and median.

q - calculates extent of q and 1.0-q (default: q=0.25)

quantiles clj

(quantiles vs)

(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 clj

(r2 [vs1 vs2-or-val])

(r2 vs1 vs2-or-val)

(r2 vs1 vs2-or-val no-of-variables)

R2

r2-determination clj

(r2-determination [group1 group2])

(r2-determination group1 group2)

Coefficient of determination

rank-epsilon-sq clj

(rank-epsilon-sq xs)

Effect size for Kruskal-Wallis test

rank-eta-sq clj

(rank-eta-sq xs)

Effect size for Kruskal-Wallis test

remove-outliers clj

(remove-outliers vs)

(remove-outliers vs estimation-strategy)

(remove-outliers vs q1 q3)

Remove outliers defined as values outside inner fences.

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

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

Returns a sequence without outliers.

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

rescale clj

(rescale vs)

(rescale vs low high)

Lineary rascale data to desired range, [0,1] by default

rmse clj

(rmse [vs1 vs2-or-val])

(rmse vs1 vs2-or-val)

Root mean squared error

robust-standardize clj

(robust-standardize vs)

(robust-standardize vs q)

Normalize samples to have median = 0 and MAD = 1.

If q argument is used, scaling is done by quantile difference (Q_q, Q_(1-q)). Set 0.25 for IQR.

rows->contingency-table clj

(rows->contingency-table xss)

rss clj

(rss [vs1 vs2-or-val])

(rss vs1 vs2-or-val)

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

similarity clj

(similarity method P-observed Q-expected)

(similarity method
            P-observed
            Q-expected
            {:keys [bins probabilities? epsilon]
             :or {probabilities? true epsilon 1.0E-6}})

Various PDF similarities between two histograms (frequencies) or probabilities.

Q can be a distribution object. Then, histogram will be created out of P.

Arguments:

• method - distance method
• P-observed - frequencies, probabilities or actual data (when Q is a distribution)
• Q-expected - frequencies, probabilities or distribution object (when P is a data)

Options:

• :probabilities? - should P/Q be converted to a probabilities, default: true.
• :epsilon - small number which replaces 0.0 when division or logarithm is used`
• :bins - number of bins or bins estimation method, see histogram.

The list of methods: :intersection, :czekanowski, :motyka, :kulczynski, :ruzicka, :inner-product, :harmonic-mean, :cosine, :jaccard, :dice, :fidelity, :squared-chord

See more: Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions by Sung-Hyuk Cha

skewness clj

(skewness vs)

(skewness vs typ)

Calculate skewness from sequence.

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

skewness-test clj

(skewness-test xs)

(skewness-test xs params)

(skewness-test xs skew {:keys [sides type] :or {sides :two-sided type :g1}})

Normality test for skewness.

span clj

(span vs)

Width of the sample, maximum value minus minimum value

spearman-correlation clj

(spearman-correlation [vs1 vs2])

(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 mu)

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.

t-test-one-sample clj

(t-test-one-sample xs)

(t-test-one-sample xs m)

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)

t-test-two-samples clj

(t-test-two-samples xs ys)

(t-test-two-samples xs
                    ys
                    {:keys [paired? equal-variances?]
                     :or {paired? false equal-variances? false}
                     :as params})

Two samples Student's t-test

• alpha - significance level (default: 0.05)
• sides - one of: :two-sided (default), :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)

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.

trim-lower clj

(trim-lower vs)

(trim-lower vs quantile)

(trim-lower vs quantile estimation-strategy)

Trim data below given quanitle, default: 0.2.

trim-upper clj

(trim-upper vs)

(trim-upper vs quantile)

(trim-upper vs quantile estimation-strategy)

Trim data above given quanitle, default: 0.2.

tschuprows-t clj

(tschuprows-t contingency-table)

(tschuprows-t group1 group2)

Tschuprows T effect size for discrete data

ttest-one-sample clj deprecated

ttest-two-samples clj deprecated

variance clj

(variance vs)

(variance vs mu)

Calculate variance of vs.

See population-variance.

variation clj

(variation vs)

Coefficient of variation CV = stddev / mean

weighted-kappa clj

(weighted-kappa contingency-table)

(weighted-kappa contingency-table weights)

Cohen's weighted kappa for indexed contingency table

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.

wmean clj deprecated

(wmean vs)

(wmean vs weights)

Weighted mean

wmedian clj

(wmedian vs ws)

(wmedian vs ws method)

Weighted median.

Calculation is done using interpolation. There are three methods:

• :linear - linear interpolation, default
• :step - step interpolation
• :average - average of ties

Based on spatstat.geom::weighted.quantile from R.

wmw-odds clj

(wmw-odds [group1 group2])

(wmw-odds group1 group2)

Wilcoxon-Mann-Whitney odds

wquantile clj

(wquantile vs ws q)

(wquantile vs ws q method)

Weighted quantile.

Calculation is done using interpolation. There are three methods:

• :linear - linear interpolation, default
• :step - step interpolation
• :average - average of ties

Based on spatstat.geom::weighted.quantile from R.

wquantiles clj

(wquantiles vs ws)

(wquantiles vs ws qs)

(wquantiles vs ws qs method)

Weighted quantiles.

Calculation is done using interpolation. There are three methods:

• :linear - linear interpolation, default
• :step - step interpolation
• :average - average of ties

Based on spatstat.geom::weighted.quantile from R.

wstddev clj

(wstddev vs freqs)

Calculate weighted (unbiased) standard deviation of vs

wvariance clj

(wvariance vs freqs)

Calculate weighted (unbiased) variance of vs.

yeo-johnson-transformation clj

(yeo-johnson-transformation xs)

(yeo-johnson-transformation xs lambda)

(yeo-johnson-transformation xs lambda alpha)

Yeo-Johnson transformation

Arguments:

• lambda - power parameter (default: 0.0)
• alpha - shift parameter (optional)

z-test-one-sample clj

(z-test-one-sample xs)

(z-test-one-sample xs m)

One sample z-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)

z-test-two-samples clj

(z-test-two-samples xs ys)

(z-test-two-samples xs
                    ys
                    {:keys [paired? equal-variances?]
                     :or {paired? false equal-variances? false}
                     :as params})

Two samples z-test

• alpha - significance level (default: 0.05)
• sides - one of: :two-sided (default), :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)