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 group of sequences. |
| betai(a, b, x) | Returns the incomplete beta function. |
| chisquare(f_obs[, f_exp, ddof, axis]) | Calculates a one-way chi square test. |
| count_tied_groups(x[, use_missing]) | Counts the number of tied values. |
| 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 any number of groups. |
| f_value_wilks_lambda(ER, EF, dfnum, dfden, a, b) | Calculation of Wilks lambda F-statistic for multivariate data, per Maxwell & Delaney p.657. |
| 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. |
| 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 Kendall’s rank correlation tau, for seasonal data. |
| 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 statistic |
| plotting_positions(data[, alpha, beta]) | Returns 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[, prob, alphap, betap, axis, limit]) | 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 non-correlation. |
| plotting_positions(data[, alpha, beta]) | Returns 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. |
| scoreatpercentile(data, per[, limit, ...]) | Calculate the score at the given ‘per’ percentile of the sequence a. |
| sem(a[, axis, ddof]) | Calculates the standard error of the mean (or standard error of measurement) of the values in the input 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 to test for non-correlation. |
| theilslopes(y[, x, alpha]) | Computes the Theil slope as the median of all slopes between paired values. |
| 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 smallest and largest data values. |
| trimmed_stde(a[, limits, inclusive, axis]) | Returns the standard error of the trimmed mean 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 values from one tail. |
| tsem(a[, limits, inclusive]) | Compute the trimmed standard error of the mean. |
| ttest_onesamp(a, popmean[, axis]) | Calculates the T-test for the mean of ONE group of scores. |
| ttest_ind(a, b[, axis]) | Calculates the T-test for the means of TWO INDEPENDENT samples of scores. |
| ttest_onesamp(a, popmean[, axis]) | Calculates the T-test for the mean of ONE group of scores. |
| 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 |
| 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. |
| zmap(scores, compare[, axis, ddof]) | Calculates the relative z-scores. |
| zscore(a[, axis, ddof]) | Calculates the z score of each value in the sample, relative to the sample mean and standard deviation. |