arviz_stats.kde#
- arviz_stats.kde(data, dim=None, group='posterior', var_names=None, filter_vars=None, coords=None, circular=False, **kwargs)[source]#
Compute the marginal kernel density estimates (KDE).
See the EABM chapter on Visualization of Random Variables with ArviZ for more details.
- Parameters:
- dataarray_like,
xarray.DataArray,xarray.Dataset,xarray.DataTree,DataArrayGroupBy,DatasetGroupBy, or idata-like Input data. It will have different pre-processing applied to it depending on its type:
array-like: call array layer within
arviz-stats.xarray object: apply dimension aware function to all relevant subsets
others: passed to
arviz_base.convert_to_datasetthen treated asxarray.Dataset. This option is discouraged due to needing this conversion which is completely automated and will be needed again in future executions or similar functions.It is recommended to first perform the conversion manually and then call
arviz_stats.kde. This allows controlling the conversion step and inspecting its results.
- dimsequence of
hashable, optional Dimensions to be reduced when computing the KDE. Default
rcParams["data.sample_dims"].- group
hashable, default “posterior” Group on which to compute the KDE
- var_names
strorlistofstr, optional Names of the variables for which the KDE should be computed.
- filter_vars{
None, “like”, “regex”}, defaultNone - coords
dict, optional Dictionary of dimension/index names to coordinate values defining a subset of the data for which to perform the computation.
- circularbool, default
False - **kwargs
any, optional Forwarded to the array or dataarray interface for KDE.
- dataarray_like,
- Returns:
ndarray,xarray.DataArray,xarray.Dataset,xarray.DataTreeRequested KDE of the provided input. The xarray objects will have a
kde_dimdimension and aplot_axisdimension with coordinates “x”, and “y”.
See also
arviz_stats.ecdf,arviz_stats.histogram,arviz_stats.qdsAlternative visual summaries for marginal distributions
arviz_plots.plot_dist
Examples
Calculate the KDE of a Normal random variable:
In [1]: import arviz_stats as azs ...: import numpy as np ...: data = np.random.default_rng().normal(size=2000) ...: azs.kde(data) ...: Out[1]: (array([-4.29998821, -4.28488109, -4.26977397, -4.25466685, -4.23955973, -4.22445261, -4.20934549, -4.19423837, -4.17913124, -4.16402412, -4.148917 , -4.13380988, -4.11870276, -4.10359564, -4.08848852, -4.0733814 , -4.05827427, -4.04316715, -4.02806003, -4.01295291, -3.99784579, -3.98273867, -3.96763155, -3.95252443, -3.9374173 , -3.92231018, -3.90720306, -3.89209594, -3.87698882, -3.8618817 , -3.84677458, -3.83166746, -3.81656033, -3.80145321, -3.78634609, -3.77123897, -3.75613185, -3.74102473, -3.72591761, -3.71081049, -3.69570336, -3.68059624, -3.66548912, -3.650382 , -3.63527488, -3.62016776, -3.60506064, -3.58995352, -3.57484639, -3.55973927, -3.54463215, -3.52952503, -3.51441791, -3.49931079, -3.48420367, -3.46909655, -3.45398943, -3.4388823 , -3.42377518, -3.40866806, -3.39356094, -3.37845382, -3.3633467 , -3.34823958, -3.33313246, -3.31802533, -3.30291821, -3.28781109, -3.27270397, -3.25759685, -3.24248973, -3.22738261, -3.21227549, -3.19716836, -3.18206124, -3.16695412, -3.151847 , -3.13673988, -3.12163276, -3.10652564, -3.09141852, -3.07631139, -3.06120427, -3.04609715, -3.03099003, -3.01588291, -3.00077579, -2.98566867, -2.97056155, -2.95545442, -2.9403473 , -2.92524018, -2.91013306, -2.89502594, -2.87991882, -2.8648117 , -2.84970458, -2.83459745, -2.81949033, -2.80438321, -2.78927609, -2.77416897, -2.75906185, -2.74395473, -2.72884761, -2.71374049, -2.69863336, -2.68352624, -2.66841912, -2.653312 , -2.63820488, -2.62309776, -2.60799064, -2.59288352, -2.57777639, -2.56266927, -2.54756215, -2.53245503, -2.51734791, -2.50224079, -2.48713367, -2.47202655, -2.45691942, -2.4418123 , -2.42670518, -2.41159806, -2.39649094, -2.38138382, -2.3662767 , -2.35116958, -2.33606245, -2.32095533, -2.30584821, -2.29074109, -2.27563397, -2.26052685, -2.24541973, -2.23031261, -2.21520548, -2.20009836, -2.18499124, -2.16988412, -2.154777 , -2.13966988, -2.12456276, -2.10945564, -2.09434851, -2.07924139, -2.06413427, -2.04902715, -2.03392003, -2.01881291, -2.00370579, -1.98859867, -1.97349154, -1.95838442, -1.9432773 , -1.92817018, -1.91306306, -1.89795594, -1.88284882, -1.8677417 , -1.85263458, -1.83752745, -1.82242033, -1.80731321, -1.79220609, -1.77709897, -1.76199185, -1.74688473, -1.73177761, -1.71667048, -1.70156336, -1.68645624, -1.67134912, -1.656242 , -1.64113488, -1.62602776, -1.61092064, -1.59581351, -1.58070639, -1.56559927, -1.55049215, -1.53538503, -1.52027791, -1.50517079, -1.49006367, -1.47495654, -1.45984942, -1.4447423 , -1.42963518, -1.41452806, -1.39942094, -1.38431382, -1.3692067 , -1.35409957, -1.33899245, -1.32388533, -1.30877821, -1.29367109, -1.27856397, -1.26345685, -1.24834973, -1.2332426 , -1.21813548, -1.20302836, -1.18792124, -1.17281412, -1.157707 , -1.14259988, -1.12749276, -1.11238564, -1.09727851, -1.08217139, -1.06706427, -1.05195715, -1.03685003, -1.02174291, -1.00663579, -0.99152867, -0.97642154, -0.96131442, -0.9462073 , -0.93110018, -0.91599306, -0.90088594, -0.88577882, -0.8706717 , -0.85556457, -0.84045745, -0.82535033, -0.81024321, -0.79513609, -0.78002897, -0.76492185, -0.74981473, -0.7347076 , -0.71960048, -0.70449336, -0.68938624, -0.67427912, -0.659172 , -0.64406488, -0.62895776, -0.61385063, -0.59874351, -0.58363639, -0.56852927, -0.55342215, -0.53831503, -0.52320791, -0.50810079, -0.49299366, -0.47788654, -0.46277942, -0.4476723 , -0.43256518, -0.41745806, -0.40235094, -0.38724382, -0.37213669, -0.35702957, -0.34192245, -0.32681533, -0.31170821, -0.29660109, -0.28149397, -0.26638685, -0.25127973, -0.2361726 , -0.22106548, -0.20595836, -0.19085124, -0.17574412, -0.160637 , -0.14552988, -0.13042276, -0.11531563, -0.10020851, -0.08510139, -0.06999427, -0.05488715, -0.03978003, -0.02467291, -0.00956579, 0.00554134, 0.02064846, 0.03575558, 0.0508627 , 0.06596982, 0.08107694, 0.09618406, 0.11129118, 0.12639831, 0.14150543, 0.15661255, 0.17171967, 0.18682679, 0.20193391, 0.21704103, 0.23214815, 0.24725528, 0.2623624 , 0.27746952, 0.29257664, 0.30768376, 0.32279088, 0.337898 , 0.35300512, 0.36811225, 0.38321937, 0.39832649, 0.41343361, 0.42854073, 0.44364785, 0.45875497, 0.47386209, 0.48896921, 0.50407634, 0.51918346, 0.53429058, 0.5493977 , 0.56450482, 0.57961194, 0.59471906, 0.60982618, 0.62493331, 0.64004043, 0.65514755, 0.67025467, 0.68536179, 0.70046891, 0.71557603, 0.73068315, 0.74579028, 0.7608974 , 0.77600452, 0.79111164, 0.80621876, 0.82132588, 0.836433 , 0.85154012, 0.86664725, 0.88175437, 0.89686149, 0.91196861, 0.92707573, 0.94218285, 0.95728997, 0.97239709, 0.98750422, 1.00261134, 1.01771846, 1.03282558, 1.0479327 , 1.06303982, 1.07814694, 1.09325406, 1.10836119, 1.12346831, 1.13857543, 1.15368255, 1.16878967, 1.18389679, 1.19900391, 1.21411103, 1.22921816, 1.24432528, 1.2594324 , 1.27453952, 1.28964664, 1.30475376, 1.31986088, 1.334968 , 1.35007512, 1.36518225, 1.38028937, 1.39539649, 1.41050361, 1.42561073, 1.44071785, 1.45582497, 1.47093209, 1.48603922, 1.50114634, 1.51625346, 1.53136058, 1.5464677 , 1.56157482, 1.57668194, 1.59178906, 1.60689619, 1.62200331, 1.63711043, 1.65221755, 1.66732467, 1.68243179, 1.69753891, 1.71264603, 1.72775316, 1.74286028, 1.7579674 , 1.77307452, 1.78818164, 1.80328876, 1.81839588, 1.833503 , 1.84861013, 1.86371725, 1.87882437, 1.89393149, 1.90903861, 1.92414573, 1.93925285, 1.95435997, 1.9694671 , 1.98457422, 1.99968134, 2.01478846, 2.02989558, 2.0450027 , 2.06010982, 2.07521694, 2.09032406, 2.10543119, 2.12053831, 2.13564543, 2.15075255, 2.16585967, 2.18096679, 2.19607391, 2.21118103, 2.22628816, 2.24139528, 2.2565024 , 2.27160952, 2.28671664, 2.30182376, 2.31693088, 2.332038 , 2.34714513, 2.36225225, 2.37735937, 2.39246649, 2.40757361, 2.42268073, 2.43778785, 2.45289497, 2.4680021 , 2.48310922, 2.49821634, 2.51332346, 2.52843058, 2.5435377 , 2.55864482, 2.57375194, 2.58885907, 2.60396619, 2.61907331, 2.63418043, 2.64928755, 2.66439467, 2.67950179, 2.69460891, 2.70971604, 2.72482316, 2.73993028, 2.7550374 , 2.77014452, 2.78525164, 2.80035876, 2.81546588, 2.83057301, 2.84568013, 2.86078725, 2.87589437, 2.89100149, 2.90610861, 2.92121573, 2.93632285, 2.95142997, 2.9665371 , 2.98164422, 2.99675134, 3.01185846, 3.02696558, 3.0420727 , 3.05717982, 3.07228694, 3.08739407, 3.10250119, 3.11760831, 3.13271543, 3.14782255, 3.16292967, 3.17803679, 3.19314391, 3.20825104, 3.22335816, 3.23846528, 3.2535724 , 3.26867952, 3.28378664, 3.29889376, 3.31400088, 3.32910801, 3.34421513, 3.35932225, 3.37442937, 3.38953649, 3.40464361, 3.41975073]), array([1.95348357e-03, 1.94280916e-03, 1.92163501e-03, 1.89030570e-03, 1.84932638e-03, 1.79934924e-03, 1.74115624e-03, 1.67563878e-03, 1.60377501e-03, 1.52660563e-03, 1.44520899e-03, 1.36067632e-03, 1.27408785e-03, 1.18649060e-03, 1.09887831e-03, 1.01217424e-03, 9.27216884e-04, 8.44749135e-04, 7.65410743e-04, 6.89734140e-04, 6.18143438e-04, 5.50956326e-04, 4.88388527e-04, 4.30803781e-04, 3.77833542e-04, 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Calculate the KDE for specific variables:
In [2]: import arviz_base as azb ...: dt = azb.load_arviz_data("centered_eight") ...: azs.kde(dt, var_names=["mu", "theta"]) ...: Out[2]: <xarray.DataTree 'posterior'> Group: /posterior Dimensions: (plot_axis: 2, kde_dim: 512, school: 8) Coordinates: * plot_axis (plot_axis) <U1 8B 'x' 'y' bw_mu float64 8B 0.3569 * school (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon' bw_theta (school) float64 64B 0.5168 0.5102 0.5383 ... 0.4794 0.5383 Dimensions without coordinates: kde_dim Data variables: mu (plot_axis, kde_dim) float64 8kB -7.485 -7.435 ... 0.001112 theta (plot_axis, school, kde_dim) float64 66kB -14.87 ... 0.0007317
Calculate the KDE also over the school dimension (for variables where present):
In [3]: azs.kde(dt, dim=["chain", "draw", "school"]) Out[3]: <xarray.DataTree 'posterior'> Group: /posterior Dimensions: (plot_axis: 2, kde_dim: 512) Coordinates: * plot_axis (plot_axis) <U1 8B 'x' 'y' bw_mu float64 8B 0.3569 bw_theta float64 8B 0.3822 bw_tau float64 8B 0.2737 Dimensions without coordinates: kde_dim Data variables: mu (plot_axis, kde_dim) float64 8kB -7.485 -7.435 ... 0.001112 theta (plot_axis, kde_dim) float64 8kB -29.63 -29.49 ... 0.0001257 tau (plot_axis, kde_dim) float64 8kB 0.9156 0.9539 ... 0.002818