7.10.7.1. statsmodels.stats.weightstats.DescrStatsW¶

class statsmodels.stats.weightstats.DescrStatsW(data, weights=None, ddof=0)[source]¶

descriptive statistics and tests with weights for case weights

Assumes that the data is 1d or 2d with (nobs, nvars) observations in rows, variables in columns, and that the same weight applies to each column.

If degrees of freedom correction is used, then weights should add up to the number of observations. ttest also assumes that the sum of weights corresponds to the sample size.

This is essentially the same as replicating each observations by its weight, if the weights are integers, often called case or frequency weights.

Parameters:

data : array_like, 1-D or 2-D

dataset

weights : None or 1-D ndarray

weights for each observation, with same length as zero axis of data

ddof : int

default ddof=0, degrees of freedom correction used for second moments, var, std, cov, corrcoef. However, statistical tests are independent of ddof, based on the standard formulas.

Examples

Note: I don’t know the seed for the following, so the numbers will differ

>>> x1_2d = 1.0 + np.random.randn(20, 3)
>>> w1 = np.random.randint(1,4, 20)
>>> d1 = DescrStatsW(x1_2d, weights=w1)
>>> d1.mean
array([ 1.42739844,  1.23174284,  1.083753  ])
>>> d1.var
array([ 0.94855633,  0.52074626,  1.12309325])
>>> d1.std_mean
array([ 0.14682676,  0.10878944,  0.15976497])

>>> tstat, pval, df = d1.ttest_mean(0)
>>> tstat; pval; df
array([  9.72165021,  11.32226471,   6.78342055])
array([  1.58414212e-12,   1.26536887e-14,   2.37623126e-08])
44.0

>>> tstat, pval, df = d1.ttest_mean([0, 1, 1])
>>> tstat; pval; df
array([ 9.72165021,  2.13019609,  0.52422632])
array([  1.58414212e-12,   3.87842808e-02,   6.02752170e-01])
44.0

#if weiqhts are integers, then asrepeats can be used

>>> x1r = d1.asrepeats()
>>> x1r.shape
...
>>> stats.ttest_1samp(x1r, [0, 1, 1])
...

__init__(data, weights=None, ddof=0)[source]¶

Methods

`__init__`(data[, weights, ddof])
`asrepeats`()	get array that has repeats given by floor(weights)
`corrcoef`()	weighted correlation with default ddof
`cov`()	weighted covariance of data if data is 2 dimensional
`demeaned`()	data with weighted mean subtracted
`get_compare`(other[, weights])	return an instance of CompareMeans with self and other
`mean`()	weighted mean of data
`nobs`()	alias for number of observations/cases, equal to sum of weights
`std`()	standard deviation with default degrees of freedom correction
`std_ddof`([ddof])	standard deviation of data with given ddof
`std_mean`()	standard deviation of weighted mean
`sum`()	weighted sum of data
`sum_weights`()
`sumsquares`()	weighted sum of squares of demeaned data
`tconfint_mean`([alpha, alternative])	two-sided confidence interval for weighted mean of data
`ttest_mean`([value, alternative])	ttest of Null hypothesis that mean is equal to value.
`ttost_mean`(low, upp)	test of (non-)equivalence of one sample
`var`()	variance with default degrees of freedom correction
`var_ddof`([ddof])	variance of data given ddof
`zconfint_mean`([alpha, alternative])	two-sided confidence interval for weighted mean of data
`ztest_mean`([value, alternative])	z-test of Null hypothesis that mean is equal to value.
`ztost_mean`(low, upp)	test of (non-)equivalence of one sample, based on z-test