U Kv f&@sDdZddlZddlZdddZdddZGdddZdd d ZdS)zqAnova k-sample comparison without and with trimming Created on Sun Jun 09 23:51:34 2013 Author: Josef Perktold NcCsvt|}|dkr|}d}|j|}t||}||}||krLtdtdg|j}t||||<|t|S)a Slices off a proportion of items from both ends of an array. Slices off the passed proportion of items from both ends of the passed array (i.e., with `proportiontocut` = 0.1, slices leftmost 10% **and** rightmost 10% of scores). You must pre-sort the array if you want 'proper' trimming. Slices off less if proportion results in a non-integer slice index (i.e., conservatively slices off `proportiontocut`). Parameters ---------- a : array_like Data to trim. proportiontocut : float or int Proportion of data to trim at each end. axis : int or None Axis along which the observations are trimmed. The default is to trim along axis=0. If axis is None then the array will be flattened before trimming. Returns ------- out : array-like Trimmed version of array `a`. Examples -------- >>> from scipy import stats >>> a = np.arange(20) >>> b = stats.trimboth(a, 0.1) >>> b.shape (16,) NrProportion too big.) npasarrayZravelshapeint ValueErrorslicendimtuple)aproportiontocutaxisnobslowercutuppercutslrD/tmp/pip-unpacked-wheel-2v6byqio/statsmodels/stats/robust_compare.pytrimboths$   rcCs$tt||||d}tj||dS)a Return mean of array after trimming observations from both tails. If `proportiontocut` = 0.1, slices off 'leftmost' and 'rightmost' 10% of scores. Slices off LESS if proportion results in a non-integer slice index (i.e., conservatively slices off `proportiontocut` ). Parameters ---------- a : array_like Input array proportiontocut : float Fraction to cut off at each tail of the sorted observations. axis : int or None Axis along which the trimmed means are computed. The default is axis=0. If axis is None then the trimmed mean will be computed for the flattened array. Returns ------- trim_mean : ndarray Mean of trimmed array. r )rrsortmean)r r r Znewarrr trim_meanCsrc@seZdZdZdddZeddZedd Zed d Zed d Z eddZ eddZ eddZ dddZ ddZdS) TrimmedMeana class for trimmed and winsorized one sample statistics axis is None, i.e. ravelling, is not supported Parameters ---------- data : array-like The data, observations to analyze. fraction : float in (0, 0.5) The fraction of observations to trim at each tail. The number of observations trimmed at each tail is ``int(fraction * nobs)`` is_sorted : boolean Indicator if data is already sorted. By default the data is sorted along ``axis``. axis : int The axis of reduce operations. By default axis=0, that is observations are along the zero dimension, i.e. rows if 2-dim. FrcCst||_||_||_|jj||_}t|||_}|||_ }||krZt d|d||_ t dg|jj |_t |j|j |j|<t|j|_|stj|j|d|_n|j|_tj|j||d|_tj|j|d|d|_dS)Nrr)rrdatar fractionrrrrrr nobs_reducedrr rr r data_sortedZtake lowerbound upperbound)selfrr is_sortedr rrrrrr__init__vs"  zTrimmedMean.__init__cCs |j|jS)z/numpy array of trimmed and sorted data )rrr"rrr data_trimmedszTrimmedMean.data_trimmedcCs0t|j|j}t|j|j}t|j||S)zwinsorized data )r expand_dimsr r r!Zclipr)r"ZlbZubrrrdata_winsorizedszTrimmedMean.data_winsorizedcCst|jt|j|jS)zmean of trimmed data )rrrr rr r%rrr mean_trimmedszTrimmedMean.mean_trimmedcCst|j|jS)z mean of winsorized data )rrr(r r%rrrmean_winsorizedszTrimmedMean.mean_winsorizedcCstj|jd|jdS)z$variance of winsorized data r)Zddofr )rvarr(r r%rrrvar_winsorizedszTrimmedMean.var_winsorizedcCs,t|j|j}|t|j|j9}|S)z'standard error of trimmed mean )rsqrtr,rr)r"serrrstd_mean_trimmedszTrimmedMean.std_mean_trimmedcCs.t|j|j}||jd|jd9}|S)z*standard error of winsorized mean r)rr-r,rr)r"std_rrrstd_mean_winsorizedszTrimmedMean.std_mean_winsorizedtrimmed two-sidedc Cspddlmm}|jd}|dkr2|j}|j}n|dkrH|j}|j}ntd|j |d||||d}||fS)aF One sample t-test for trimmed or Winsorized mean Parameters ---------- value : float Value of the mean under the Null hypothesis transform : {'trimmed', 'winsorized'} Specified whether the mean test is based on trimmed or winsorized data. alternative : {'two-sided', 'larger', 'smaller'} Notes ----- p-value is based on the approximate t-distribution of the test statistic. The approximation is valid if the underlying distribution is symmetric. rNrr2Z winsorizedz/transform can only be 'trimmed' or 'winsorized') alternativeZdiff) Zstatsmodels.stats.weightstatsstatsZ weightstatsrr)r/r*r1rZ_tstat_generic) r"value transformr4ZsmwsZdfZmean_r0resrrr ttest_means  zTrimmedMean.ttest_meancCs t|j|d|jd}|j|_|S)zcreate a TrimmedMean instance with a new trimming fraction This reuses the sorted array from the current instance. T)r#r )rrr r)r"fractmrrrreset_fractions  zTrimmedMean.reset_fractionN)Fr)rr2r3)__name__ __module__ __qualname____doc__r$propertyr&r(r)r*r,r/r1r9r<rrrrr`s(         $rmedianabs皙?cCst|}|dkrtj}n:|dkr,dd}n(|dkr>dd}nt|rL|}ntd|dkr|||ttj||d |}nv|d kr||ttj||d |}nN|d krt|||d }||t||}n"t |t j r|||}ntd |S) aTransform data for variance comparison for Levene type tests Parameters ---------- data : array_like Observations for the data. center : "median", "mean", "trimmed" or float Statistic used for centering observations. If a float, then this value is used to center. Default is median. transform : 'abs', 'square', 'identity' or a callable The transform for the centered data. trim_frac : float in [0, 0.5) Fraction of observations that are trimmed on each side of the sorted observations. This is only used if center is `trimmed`. axis : int Axis along which the data are transformed when centering. Returns ------- res : ndarray transformed data in the same shape as the original data. rCZsquarecSs||SNrxrrrz!scale_transform..identitycSs|SrErrFrrrrHrIz&transform should be abs, square or exprBrrr2z(center should be median, mean or trimmed) rrrCcallablerr'rBrr isinstancenumbersNumber)rcenterr7Z trim_fracr rGZtfuncr8rrrscale_transforms*      rP)r)r)rBrCrDr)r@rMZnumpyrrrrrPrrrrs 3