# Statistical functions for masked arrays (scipy.stats.mstats)ΒΆ

This module contains a large number of statistical functions that can be used with masked arrays.

Most of these functions are similar to those in scipy.stats but might have small differences in the API or in the algorithm used. Since this is a relatively new package, some API changes are still possible.

 argstoarray(*args) Constructs a 2D array from a sequence of sequences. Sequences are filled betai(a, b, x) Returns the incomplete beta function. chisquare(f_obs[, f_exp]) Calculates a one-way chi square test. count_tied_groups(x[, use_missing]) Counts the number of tied values in x, and returns a dictionary (nb of ties: nb of groups). describe(a[, axis]) Computes several descriptive statistics of the passed array. f_oneway(*args) Performs a 1-way ANOVA, returning an F-value and probability given f_value_wilks_lambda(ER, EF, dfnum, dfden, a, b) Calculation of Wilks lambda F-statistic for multivarite data, per find_repeats(arr) Find repeats in arr and return a tuple (repeats, repeat_count). friedmanchisquare(*args) Friedman Chi-Square is a non-parametric, one-way within-subjects ANOVA. gmean(a[, axis]) Compute the geometric mean along the specified axis. hmean(a[, axis]) Calculates the harmonic mean along the specified axis. kendalltau(x, y[, use_ties, use_missing]) Computes Kendall’s rank correlation tau on two variables x and y. kendalltau_seasonal(x) Computes a multivariate extension Kendall’s rank correlation tau, designed kruskalwallis(*args) Compute the Kruskal-Wallis H-test for independent samples kruskalwallis(*args) Compute the Kruskal-Wallis H-test for independent samples ks_twosamp(data1, data2[, alternative]) Computes the Kolmogorov-Smirnov test on two samples. ks_twosamp(data1, data2[, alternative]) Computes the Kolmogorov-Smirnov test on two samples. kurtosis(a[, axis, fisher, bias]) Computes the kurtosis (Fisher or Pearson) of a dataset. kurtosistest(a[, axis]) Tests whether a dataset has normal kurtosis linregress(*args) Calculate a regression line mannwhitneyu(x, y[, use_continuity]) Computes the Mann-Whitney on samples x and y. plotting_positions(data[, alpha, beta]) Returns the plotting positions (or empirical percentile points) for the data. mode(a[, axis]) Returns an array of the modal (most common) value in the passed array. moment(a[, moment, axis]) Calculates the nth moment about the mean for a sample. mquantiles(a, 5[, prob, alphap, betap, ...]) Computes empirical quantiles for a data array. msign(x) Returns the sign of x, or 0 if x is masked. normaltest(a[, axis]) Tests whether a sample differs from a normal distribution obrientransform(*args) Computes a transform on input data (any number of columns). pearsonr(x, y) Calculates a Pearson correlation coefficient and the p-value for testing plotting_positions(data[, alpha, beta]) Returns the plotting positions (or empirical percentile points) for the data. pointbiserialr(x, y) Calculates a point biserial correlation coefficient and the associated p-value. rankdata(data[, axis, use_missing]) Returns the rank (also known as order statistics) of each data point along the given axis. samplestd(data[, axis]) Returns a biased estimate of the standard deviation of the data, as the square root of the average squared deviations from the mean. samplevar(data[, axis]) Returns a biased estimate of the variance of the data, as the average of the squared deviations from the mean. scoreatpercentile(data, per[, limit, ...]) Calculate the score at the given ‘per’ percentile of the sequence a. sem(a[, axis]) Calculates the standard error of the mean (or standard error of measurement) of the values in the passed array. signaltonoise(data[, axis]) Calculates the signal-to-noise ratio, as the ratio of the mean over standard deviation along the given axis. skew(a[, axis, bias]) Computes the skewness of a data set. skewtest(a[, axis]) Tests whether the skew is different from the normal distribution. spearmanr(x, y[, use_ties]) Calculates a Spearman rank-order correlation coefficient and the p-value std(a[, axis]) Returns the estimated population standard deviation of the values in stderr(a[, axis]) stderr is deprecated! theilslopes(y[, x, alpha]) Computes the Theil slope over the dataset (x,y), as the median of all slopes threshold(a[, threshmin, threshmax, newval]) Clip array to a given value. tmax(a, upperlimit[, axis, inclusive]) Compute the trimmed maximum tmean(a[, limits, inclusive]) Compute the trimmed mean tmin(a[, lowerlimit, axis, inclusive]) Compute the trimmed minimum trim(a[, limits, inclusive, relative, axis]) Trims an array by masking the data outside some given limits. trima(a[, limits, inclusive]) Trims an array by masking the data outside some given limits. trimboth(data[, proportiontocut, inclusive, ...]) Trims the data by masking the int(proportiontocut*n) smallest and int(proportiontocut*n) largest values of data along the given axis, where n is the number of unmasked values before trimming. trimmed_stde(a[, limits, inclusive, axis]) Returns the standard error of the trimmed mean of the data along the given axis. trimr(a[, limits, inclusive, axis]) Trims an array by masking some proportion of the data on each end. trimtail(data[, proportiontocut, tail, ...]) Trims the data by masking int(trim*n) values from ONE tail of the tsem(a[, limits, inclusive]) Compute the trimmed standard error of the mean ttest_onesamp(a, popmean) Calculates the T-test for the mean of ONE group of scores a. ttest_ind(a, b[, axis]) Calculates the T-test for the means of TWO INDEPENDENT samples of scores. ttest_onesamp(a, popmean) Calculates the T-test for the mean of ONE group of scores a. ttest_rel(a, b[, axis]) Calculates the T-test on TWO RELATED samples of scores, a and b. tvar(a[, limits, inclusive]) Compute the trimmed variance var(a[, axis]) Returns the estimated population variance of the values in the passed variation(a[, axis]) Computes the coefficient of variation, the ratio of the biased standard deviation to the mean. winsorize(a[, limits, inclusive, inplace, axis]) Returns a Winsorized version of the input array. z(a, score) z is deprecated! zmap(scores, compare[, axis]) Returns an array of z-scores the shape of scores (e.g., [x,y]), compared to array passed to compare (e.g., [time,x,y]). zs(a) zs is deprecated!

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