>hٲSrSSKJr SSKJrJr SSKrSSKr SSK J r SSK J r SSKJr SSKJr SS KJr SS KJrJrJr SS KJr SS KJr S SKJrJrJrJ r J!r! /SQr"Sr#S,Sjr$S-Sjr%S.Sjr&Sr'S0S0SSS 4Sjr(S/Sjr)S.Sjr*S/Sjr+S0Sjr,\"\RZ"S10SS0D65S2Sj5r.\"\RZ"S10SS0D65S3S j5r/\"\ RZ"SS!055S4S"j5r0\"\ RZ"SS#055S4S$j5r1\"\SS#0-5S5S%j5r2\"\!SS#0-5S.S&j5r3\"\SS'0-5S6S(j5r4S7S)jr5S*r6S8S+jr7g)9zPartial Regression plot and residual plots to find misspecification Author: Josef Perktold License: BSD-3 Created: 2011-01-23 update 2011-06-05 : start to convert example to usable functions 2011-10-27 : docstrings )Appender)lrangelzipN)dmatrix)GEE)GLM)utils)lowess)GLSOLSWLS)wls_prediction_std)maybe_unwrap_results)_plot_added_variable_doc_plot_ceres_residuals_doc_plot_influence_doc_plot_leverage_resid2_doc_plot_partial_residuals_doc)plot_fitplot_regress_exogplot_partregress plot_ccprrplot_partregress_gridplot_ccpr_grid add_lowess abline_plotinfluence_plotplot_leverage_resid2added_variable_residspartial_resids ceres_residsplot_added_variableplot_partial_residualsplot_ceres_residualsc@SURS--UR- $)N@r)df_modelnobs)resultss wC:\Users\julio\OneDrive\Documentos\Trabajo\Ideas Frescas\venv\Lib\site-packages\statsmodels/graphics/regressionplots.py_high_leverager,,s! !!A% &w|| 33c UR5URnUR5URn[XE4SU0UD6nUR USS2S4USS2S4SSS9 UR $)a Add Lowess line to a plot. Parameters ---------- ax : AxesSubplot The Axes to which to add the plot lines_idx : int This is the line on the existing plot to which you want to add a smoothed lowess line. frac : float The fraction of the points to use when doing the lowess fit. lowess_kwargs Additional keyword arguments are passes to lowess. Returns ------- Figure The figure that holds the instance. fracNrrrg?)lw) get_lines_y_xr plotfigure)ax lines_idxr/ lowess_kwargsy0x0lress r+rr1sr*  " % %B  " % %B " 5t 5} 5DGGDAJQT CCG0 99r-Tc [R"U5upc[R"XR5upq[ U5nURR nURR SS2U4n [R"U 5n XnXn URXSURRS9 UbURXU SSS9 SU-n UR"XRU S4SS S .UD6 US La&[U5upnURXU XS S SS9 URU 5 URU5 UR!URR5 UR#SS S9 U$)a Plot fit against one regressor. This creates one graph with the scatterplot of observed values compared to fitted values. Parameters ---------- results : Results A result instance with resid, model.endog and model.exog as attributes. exog_idx : {int, str} Name or index of regressor in exog matrix. y_true : array_like. optional If this is not None, then the array is added to the plot. ax : AxesSubplot, optional If given, this subplot is used to plot in instead of a new figure being created. vlines : bool, optional If this not True, then the uncertainty (pointwise prediction intervals) of the fit is not plotted. **kwargs The keyword arguments are passed to the plot command for the fitted values points. Returns ------- Figure If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. Examples -------- Load the Statewide Crime data set and perform linear regression with `poverty` and `hs_grad` as variables and `murder` as the response >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> data = sm.datasets.statecrime.load_pandas().data >>> murder = data['murder'] >>> X = data[['poverty', 'hs_grad']] >>> X["constant"] = 1 >>> y = murder >>> model = sm.OLS(y, X) >>> results = model.fit() Create a plot just for the variable 'Poverty.' Note that vertical bars representing uncertainty are plotted since vlines is true >>> fig, ax = plt.subplots() >>> fig = sm.graphics.plot_fit(results, 0, ax=ax) >>> ax.set_ylabel("Murder Rate") >>> ax.set_xlabel("Poverty Level") >>> ax.set_title("Linear Regression") >>> plt.show() .. plot:: plots/graphics_plot_fit_ex.py Nbo)labelzb-z True valueszFitted values versus %sDr0fitted)colorr?Trkffffff? linewidthrBalphabest)loc numpoints)r create_mpl_axmaybe_name_or_idxmodelrendogexognpargsortr5 endog_names fittedvaluesrvlines set_title set_xlabel set_ylabellegend)r*exog_idxy_truer7rTkwargsfig exog_nameyx1 x1_argsorttitle_iv_liv_us r+rrMsj~!!"%GC11(MMJI"7+G  A   AxK (BBJ A BGGB4w}}88G9  :&MB % 1EGGB$$Z0#&S&$& ~*73  ":&(8A2  'LLMM)MM'--++,II&AI& Jr-c [R"U5n[R"XR5up1[ U5nURR nURR SS2U4n[U5upgnURSSS5n U RXPRRSSSUS9 U RXPRSS S S S 9 U RXWUSS SS9 U RSSS9 U RU5 U RU5 U R!SS9 URSSS5n U RXPR"S5 U R%SSS9 U RSU-SS9 U RU5 U RS5 URSSS5n [&R("URR R*S[,5n SX'URR SS2U 4n SSKJn [3URR4R6U "XSURR4R8S9U SU S9nU RSSS9 URSSS 5n [;XU S!9nU RS"SS9 UR=S#U-SS9 UR?5 URASS$9 U$)%aPlot regression results against one regressor. This plots four graphs in a 2 by 2 figure: 'endog versus exog', 'residuals versus exog', 'fitted versus exog' and 'fitted plus residual versus exog' Parameters ---------- results : result instance A result instance with resid, model.endog and model.exog as attributes. exog_idx : int or str Name or index of regressor in exog matrix. fig : Figure, optional If given, this figure is simply returned. Otherwise a new figure is created. Returns ------- Figure The value of `fig` if provided. Otherwise a new instance. Examples -------- Load the Statewide Crime data set and build a model with regressors including the rate of high school graduation (hs_grad), population in urban areas (urban), households below poverty line (poverty), and single person households (single). Outcome variable is the murder rate (murder). Build a 2 by 2 figure based on poverty showing fitted versus actual murder rate, residuals versus the poverty rate, partial regression plot of poverty, and CCPR plot for poverty rate. >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import statsmodels.formula.api as smf >>> fig = plt.figure(figsize=(8, 6)) >>> crime_data = sm.datasets.statecrime.load_pandas() >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single', ... data=crime_data.data).fit() >>> sm.graphics.plot_regress_exog(results, 'poverty', fig=fig) >>> plt.show() .. plot:: plots/graphics_regression_regress_exog.py Nrobg?)rBrGr?r@r0rA?)rBr?rGrCrDrEzY and Fitted vs. XlargefontsizerH)rIrblack)r^rBzResiduals versus %sresidF)Series)nameindex) obs_labelsr7zPartial regression plotr7z CCPR PlotzRegression Plots for %stop)!r create_mpl_figrLrMrrRrOr add_subplotr5rNrSrTrUrVrWrXrnaxhlinerPonesshapeboolpandasrprdata orig_endog row_labelsrsuptitle tight_layoutsubplots_adjust) r*rYr\r]y_namer_prstdrcrdr7 exog_noti exog_othersrps r+rrs^   s #C11(MMJI"7+G]] & &F   AxK (B*73E Aq !BGGB ##S3fGMGGB$$cH IIbBI?LL%L8MM)MM&II&I Aq !BGGB s#JJ'J"LL&2WLEMM)MM' Aq !B **003T:II--$$Q \2K 7==--88!"(/ (:(:(E(EG&5R ACLL*WL= Aq !B G" -CLLwL/LL*Y6LIC Jr-c[X5R5n[X5R5n[URUR5R5nXSU44$)a]Partial regression. regress endog on exog_i conditional on exog_others uses OLS Parameters ---------- endog : array_like exog : array_like exog_others : array_like Returns ------- res1c : OLS results instance (res1a, res1b) : tuple of OLS results instances results from regression of endog on exog_others and of exog_i on exog_others )r fitrn)rNexog_irres1ares1bres1cs r+_partial_regressionrsT,  # ' ' )E  $ ( ( *E  U[[ ) - - /E %.  r-Fc 4[R"U5up[U[5(a [ US-X9S9n[U[5(a [ X#U S9n O3[U[ 5(aSR U5n [ XU S9n OUn Sn [U [R5(aU RS:XaSn O2[U [R5(aU R(aSn [U[5(a [ US-X9S9nU (a[R"U5n[R"U5nUR"XS40U D6 [X5R!5n[U[R5(aSO UR"n[U[R5(aS OUR$R&SnO[X 5R!5n[X5R!5nUR(nUR(nUR*R,nUR*R,nUR"UUS40U D6 [UU5R!5n[/S[R"UR05SS US 9n US :XaSnUR3S U-5 UR5S U-5 UR6"S0UD6 USLaeUb UR8nO>[;US 5(a UR8nO WR*R<R>nUc[A[CU55nUSLa[CU5[CU5:wa [ES5eURG[ISSS95 [RJ"[A[CU55U[MWR(WR(5S/[CU5-S4SU0UD6nU(aU WR(WR(44$U $)a2 Plot partial regression for a single regressor. Parameters ---------- endog : {ndarray, str} The endogenous or response variable. If string is given, you can use a arbitrary translations as with a formula. exog_i : {ndarray, str} The exogenous, explanatory variable. If string is given, you can use a arbitrary translations as with a formula. exog_others : {ndarray, list[str]} Any other exogenous, explanatory variables. If a list of strings is given, each item is a term in formula. You can use a arbitrary translations as with a formula. The effect of these variables will be removed by OLS regression. data : {DataFrame, dict} Some kind of data structure with names if the other variables are given as strings. title_kwargs : dict Keyword arguments to pass on for the title. The key to control the fonts is fontdict. obs_labels : {bool, array_like} Whether or not to annotate the plot points with their observation labels. If obs_labels is a boolean, the point labels will try to do the right thing. First it will try to use the index of data, then fall back to the index of exog_i. Alternatively, you may give an array-like object corresponding to the observation numbers. label_kwargs : dict Keyword arguments that control annotate for the observation labels. ax : AxesSubplot, optional If given, this subplot is used to plot in instead of a new figure being created. ret_coords : bool If True will return the coordinates of the points in the plot. You can use this to add your own annotations. eval_env : int Patsy eval environment if user functions and formulas are used in defining endog or exog. **kwargs The keyword arguments passed to plot for the points. Returns ------- fig : Figure If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. coords : list, optional If ret_coords is True, return a tuple of arrays (x_coords, y_coords). See Also -------- plot_partregress_grid : Plot partial regression for a set of regressors. Notes ----- The slope of the fitted line is the that of `exog_i` in the full multiple regression. The individual points can be used to assess the influence of points on the estimated coefficient. Examples -------- Load the Statewide Crime data set and plot partial regression of the rate of high school graduation (hs_grad) on the murder rate(murder). The effects of the percent of the population living in urban areas (urban), below the poverty line (poverty) , and in a single person household (single) are removed by OLS regression. >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> crime_data = sm.datasets.statecrime.load_pandas() >>> sm.graphics.plot_partregress(endog='murder', exog_i='hs_grad', ... exog_others=['urban', 'poverty', 'single'], ... data=crime_data.data, obs_labels=False) >>> plt.show() .. plot:: plots/graphics_regression_partregress.py More detailed examples can be found in the Regression Plots notebook on the examples page. z-1)eval_env+FrTrgxr^rC)rBr7z e(%s | X)rrz*obs_labels does not match length of exog_icenterbottom)havarx-larger7)Partial Regression Plot)'r rK isinstancestrrlistjoinrPndarraysizepd DataFrameemptyasarrayr5r rrq design_info column_namesrnrMrRrparamsrVrWrUrrhasattrrrrlen ValueErrorupdatedict annotate_axesr)rNrrr title_kwargsrs label_kwargsr7 ret_coordsrr[r\RHS RHS_isemtpy fitted_linex_axis_endog_namey_axis_endog_name res_yaxis res_xaxis xaxis_resid yaxis_resids r+rr/sRn!!"%GC% d>+s##k(; K & &hh{#c(3K#rzz""sxx{ C & &399 &#$@ 5!F# s-f-%(,,. #-fbjj#A#ACv{{#-eRZZ#@#@CeFWFWFdFdefFgO'') $((* oo oo %OO77%OO77  [#88+{3779 aK$6$67:#" MCCMM+ 112MM+ 112LL;l;T  J VW % %J"--88J  F ,J z?c&k )IJ JDH:;  J!8*!%iooy!G"(C O!;Y1KM1$01 Y__ioo666 r-c ~SSKn[R"U5n[R"XR5upQUR URR URRS9nURRnURSn[U5S-S-n U [U5:XaSOSn UbUupU S:aSSS00n [R"URR5n [U5Hup[U5nUR!U5 UR#USS2U4XS 9nUR%XU S-5n['XdR USS2U4XS9UUW S S 9 UR)S 5 M UR+S SS9 UR-5 UR/SS9 U$)a Plot partial regression for a set of regressors. Parameters ---------- results : Results instance A regression model results instance. exog_idx : {None, list[int], list[str]} The indices or column names of the exog used in the plot, default is all. grid : {None, tuple[int]} If grid is given, then it is used for the arrangement of the subplots. The format of grid is (nrows, ncols). If grid is None, then ncol is one, if there are only 2 subplots, and the number of columns is two otherwise. fig : Figure, optional If given, this figure is simply returned. Otherwise a new figure is created. Returns ------- Figure If `fig` is None, the created figure. Otherwise `fig` itself. See Also -------- plot_partregress : Plot partial regression for a single regressor. plot_ccpr : Plot CCPR against one regressor Notes ----- A subplot is created for each explanatory variable given by exog_idx. The partial regression plot shows the relationship between the response and the given explanatory variable after removing the effect of all other explanatory variables in exog. References ---------- See http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/partregr.htm Examples -------- Using the state crime dataset separately plot the effect of the each variable on the on the outcome, murder rate while accounting for the effect of all other variables in the model visualized with a grid of partial regression plots. >>> from statsmodels.graphics.regressionplots import plot_partregress_grid >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import statsmodels.formula.api as smf >>> fig = plt.figure(figsize=(8, 6)) >>> crime_data = sm.datasets.statecrime.load_pandas() >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single', ... data=crime_data.data).fit() >>> plot_partregress_grid(results, fig=fig) >>> plt.show() .. plot:: plots/graphics_regression_partregress_grid.py rN)rqrrffontdictrlsmall)columnsF)r7rrsrrjrkffffff?rv)r~r rxrLrMrprNrRrOr|rrParray exog_names enumeraterpoprryrrUrrr)r*rYgridr\r~r]r^rOk_varsnrowsncolsr other_namesiidxothersrr7s r+rrs|   s #C11(MMJI  gmm)) 0I0I JA ==  D ZZ]F]Q 1 $E#h-'AQE   qy"Z$9: ((7==334KH% 3&&tAvI/:/B'D __U1q5 1MM$q#v,/:/?*A$,$) + R&LL*WL=C Jr-c$[R"U5up2[R"XR5upA[ U5nURR SS2U4nXPR U-nURXVUR-S5 SSK J n [Xg"U55R5nUR n [U 0[US9D6nURS5 UR!SXA4-5 UR#SU-5 U$) a Plot CCPR against one regressor. Generates a component and component-plus-residual (CCPR) plot. Parameters ---------- results : result instance A regression results instance. exog_idx : {int, str} Exogenous, explanatory variable. If string is given, it should be the variable name that you want to use, and you can use arbitrary translations as with a formula. ax : AxesSubplot, optional If given, it is used to plot in instead of a new figure being created. Returns ------- Figure If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- plot_ccpr_grid : Creates CCPR plot for multiple regressors in a plot grid. Notes ----- The CCPR plot provides a way to judge the effect of one regressor on the response variable by taking into account the effects of the other independent variables. The partial residuals plot is defined as Residuals + B_i*X_i versus X_i. The component adds the B_i*X_i versus X_i to show where the fitted line would lie. Care should be taken if X_i is highly correlated with any of the other independent variables. If this is the case, the variance evident in the plot will be an underestimate of the true variance. References ---------- http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ccpr.htm Examples -------- Using the state crime dataset plot the effect of the rate of single households ('single') on the murder rate while accounting for high school graduation rate ('hs_grad'), percentage of people in an urban area, and rate of poverty ('poverty'). >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import statsmodels.formula.api as smf >>> crime_data = sm.datasets.statecrime.load_pandas() >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single', ... data=crime_data.data).fit() >>> sm.graphics.plot_ccpr(results, 'single') >>> plt.show() .. plot:: plots/graphics_regression_ccpr.py Nrgr) add_constantruz*Component and component plus residual plotzResidual + %s*beta_%dz%s)r rKrLrMrrOrr5rnstatsmodels.tools.toolsrr rrrrUrWrV) r*rYr7r\r]r_x1betarmodrs r+rr<s|!!"%GC11(MMJI"7+G   AxK (B x( (FGGB&,4 fl2& ' + + -C ZZF v - -CLL=>MM)Y,AABMM$"# Jr-cp[R"U5n[R"XR5upAUbUupVOJ[ U5S:a.[ [ R"[ U5S- 55nSnO [ U5nSnSn[U5HkupURRSS2U 4R5S:XaSnM8URXVUS-U- 5n [X U S9nU RS5 Mm URSS S 9 UR5 UR!S S 9 U$) a Generate CCPR plots against a set of regressors, plot in a grid. Generates a grid of component and component-plus-residual (CCPR) plots. Parameters ---------- results : result instance A results instance with exog and params. exog_idx : None or list of int The indices or column names of the exog used in the plot. grid : None or tuple of int (nrows, ncols) If grid is given, then it is used for the arrangement of the subplots. If grid is None, then ncol is one, if there are only 2 subplots, and the number of columns is two otherwise. fig : Figure, optional If given, this figure is simply returned. Otherwise a new figure is created. Returns ------- Figure If `ax` is None, the created figure. Otherwise the figure to which `ax` is connected. See Also -------- plot_ccpr : Creates CCPR plot for a single regressor. Notes ----- Partial residual plots are formed as:: Res + Betahat(i)*Xi versus Xi and CCPR adds:: Betahat(i)*Xi versus Xi References ---------- See http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/ccpr.htm Examples -------- Using the state crime dataset separately plot the effect of the each variable on the on the outcome, murder rate while accounting for the effect of all other variables in the model. >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> import statsmodels.formula.api as smf >>> fig = plt.figure(figsize=(8, 8)) >>> crime_data = sm.datasets.statecrime.load_pandas() >>> results = smf.ols('murder ~ hs_grad + urban + poverty + single', ... data=crime_data.data).fit() >>> sm.graphics.plot_ccpr_grid(results, fig=fig) >>> plt.show() .. plot:: plots/graphics_regression_ccpr_grid.py Nrfr'rr)rYr7rz&Component-Component Plus Residual Plotrjrkrrv)r rxrLrMrintrPceilrrOvarryrrUrrr) r*rYrr\r]rr seen_constantrrr7s r+rrs~   s #C11(MMJI  u x=1 H b 012EEMEEMH% ==  af % ) ) +q 0M  __U1Q3}+< ="5 R&LL9GLLC Jr-c F^^UbUR5nOSn[R"U5upU(aiURummUcVURR SS2S4R 5URR SS2S4R5/nO$TbTc [S5eUcUR5nUST-T-UST-T-/n URU5 SSK J n "UU4SjSU 5n U "Xy40UD6n URU 5 URRSU R5U lURRS U R5U lU(aUR%U5 U(aUR'U5 U$) a@ Plot a line given an intercept and slope. Parameters ---------- intercept : float The intercept of the line. slope : float The slope of the line. horiz : float or array_like Data for horizontal lines on the y-axis. vert : array_like Data for verterical lines on the x-axis. model_results : statsmodels results instance Any object that has a two-value `params` attribute. Assumed that it is (intercept, slope). ax : axes, optional Matplotlib axes instance. **kwargs Options passed to matplotlib.pyplot.plt. Returns ------- Figure The figure given by `ax.figure` or a new instance. Examples -------- >>> import numpy as np >>> import statsmodels.api as sm >>> np.random.seed(12345) >>> X = sm.add_constant(np.random.normal(0, 20, size=30)) >>> y = np.dot(X, [25, 3.5]) + np.random.normal(0, 30, size=30) >>> mod = sm.OLS(y,X).fit() >>> fig = sm.graphics.abline_plot(model_results=mod) >>> ax = fig.axes[0] >>> ax.scatter(X[:,1], y) >>> ax.margins(.1) >>> import matplotlib.pyplot as plt >>> plt.show() .. plot:: plots/graphics_regression_abline.py Nrz-specify slope and intercepty or model_resultsr)Line2DcD>^\rSrSrU4SjrU4SjrUU4SjrSrU=r$)abline_plot..ABLine2Di4cB>[TU]"U0UD6 SUlSUlgN)super__init__id_xlim_callbackid_ylim_callback)selfargsr[ __class__s r+r&abline_plot..ABLine2D.__init__5s% G d -f -$(D !$(D !r-c>URnUR(a%URRUR5 UR(a%URRUR5 [ TU]5 gr)axesr callbacks disconnectrrremove)rr7rs r+r$abline_plot..ABLine2D.remove:sXB$$ ''(=(=>$$ ''(=(=> GN r-cB>URS5 UR5nUVs/sH o3ULdM UPM nnUSnUR5nUST -T-UST -T-/nURXg5 URR R 5 gs snf)NFrr)set_autoscale_on get_childrenget_xlimset_datar6canvasdraw) rr7childrenchildablinesablinerr^ interceptslopes r+update_datalim,abline_plot..ABLine2D.update_datalimBs    &(H*2D(tmu(GDQZF A1 )1Q4%<)+CDA OOA ! II   ! ! # Es BB)rr) __name__ __module__ __qualname____firstlineno__rrr__static_attributes__ __classcell__)rrrs@r+ABLine2Dr4s )   $ $r-r xlim_changed ylim_changed)rr rKrrMrOminmaxrset_xlimmatplotlib.linesradd_linerconnectrrrhlinevline) rrhorizvert model_resultsr7r[rr\data_yrrlines `` r+rrsy\ ~ KKM !!"%GC(// 5 9$$))!Q$/335$$))!Q$/3357A%%*;LM M 9 Ad5j"AaDJy$8 9FKKN($$6$2 A ( (DKKLL00ATATUDLL00ATATUD     Jr-extra_params_doczresults: object Results for a fitted regression model. influence: instance The instance of Influence for model.c Un [R"U5upUR5RS5(aU RSn OVUR5RS5(a$[ R "U RS5n O[SU-5e[ R"U 5nUS-S- nXR5- U-U- S-n Uc U RnU c#SnU(a U Rn O%U Rn O[ R"U 5n SnSS KJn UR"R%S US- - UR&5n[ R "U 5U:nU[)U5:n[ R*"UU5nUR-XXS 9 UR.R0R2nUc[5[7U 55n[R8"[ R:"U5SU[=X5[=U S- S -*U S- S -5S U5nSSS.nUR>"U40UD6 UR@"S0UD6 URB"S0UD6 U $)NcoordffzCriterion %s not understoodrf@zStudentized Residuals Residuals)stats?)srGrirrm)rlrB)Leverage)zInfluence Plot)"r rKlower startswithcooks_distancerPabsdffitsrptprhat_matrix_diagresid_studentized_externalresid_studentizedrscipyrtppfdf_residr, logical_orscatterrMrrrrrwhererrWrVrU)r* influenceexternalrG criterionr plot_alphar7leveragernr[inflr\psize old_range new_rangeylabelrcutoff large_residlarge_leverage large_pointslabelsfonts r+_influence_plotr;Ys D!!"%GC##E**##A&   % %e , ,t{{1~&6BCC u Ia$I YY[ I -i 7$ >E'' }( 33E**E 5! WW[[E!GW%5%5 6F&&-&(Kw 77N==n=LJJx%J:]]   * *F ~E #   RXXl3A6!(2!E!Gb=.57R-@) !B W -DMM&!D!MM%%LL*T* Jr-z@results : Results Results for a fitted regression model.c FUR5n[X4XX4XVS.UD6n U $)N)r,rGr-rr.r7) get_influencer;) r*r,rGr-rr.r7r[r0ress r+rrs=  "D ' B($-%/ B:@ BC Jr-zqresults: object Results for a fitted regression model influence: instance instance of Influence for modelc SSKJnJn [R"U5upsUnUR n U"UR 5n UR"U S-U S40UD6 URS5 URS5 URS5 U [U5:n URSUS- - 5n [R"U 5U :n URR R"nUc[%['UR(55n[R*"[R,"X55Sn[R."X[1U S-U 5S /['UR(5-S US S S 9nUR3SS5 U$)Nr)normzscorerfrgzNormalized residuals**2rz)Leverage vs. Normalized residuals squaredrrrjrr)r7rrg333333?) scipy.statsr@rAr rKr!rnr5rVrWrUr,r&rPrrMrrrrr)r*r(rrmargins)r*r+rGr7r[r@rAr\r0r/rnr7r5r6r9rrs r+_plot_leverage_resid2rDsF)!!"%GC D##H 4:: EGGE1Hh.v.MM+,MM*LL<=w 77N XXbqj !F&&-&(K ]]   * *F ~GLL)* HHR]]>? @ CE   UD8,D$Xc',,&77 "xH >BJJtT Jr-z:results : object Results for a fitted regression modelc >UR5n[X4XS.UD6$)N)rGr7)r=rD)r*rGr7r[r0s r+rrs'  "D  Me Mf MMr-cTURn[R"U5upu[XUUUS9upUR XSSS9 UR SSS9 [ U[5(aUn OURUn URU SS 9 URURS -SS 9 U$) N) resid_typeuse_glm_weights fit_kwargsrg333333?rGzAdded variable plotrjrkrz residuals) rMr rKr r5rUrrrrVrWrR) r* focus_exogrGrHrIr7rMr\ endog_residfocus_exog_residxnames r+r#r#s MME!!"%GC'w2<7F2<>"K GG 3cG:LL&L9*c""  ,MM%bM!MM%##l2M< Jr-cURn[R"X5up[X5nURRSS2U4n[R "U5uprUR XeSSS9 URSSS9 [U[5(aUnOURUnURUSS9 URS SS9 U$) NrgrJrKzPartial residuals plotrjrkrLrMComponent plus residual) rMr rLr!rOrKr5rUrrrrVrW) r*rNr7rM focus_colprfocus_exog_valsr\rQs r+r$r$s MME!33JFJ  ,Bmm((I6O!!"%GCGGOCG0LL)GL<*c""  ,MM%bM!MM+"M5 Jr-zHresults : Results Results instance of a fitted regression model.c<URn[R"X5up[XUUS9nURSS2U4n[R "U5upUR XSSS9 URSSS9 URUSS 9 URS SS 9 U $) N)r/ cond_meansrgrJrKzCERES residuals plotrjrkrLrMrS) rMr rLr"rOrKr5rUrVrW) r*rNr/rXr7rMrTpresidrVr\s r+r%r%s MME!33JFJ 'D%/1FjjI.O!!"%GCGGOSG4LL''L:MM*2M&MM+"M5 Jr-cTURn[U[[[45(d"[ SUR R-5e[R"X5up[[UR55n[U5nURU5 [U5nUcURSS2U4nXR!S5-n["R$R'US5upn ["R("U S:5n U SS2U 4nURSS2U4n ["R*"[U 5UR,S45n[UR,S5H nUSS2U4n[/XUSS9nUUSS2U4'M" ["R0"URSS2U4U4SS9nUR nUR35nU"UR4U40UD6nUR75nUR4UR8- n[U[[45(a2UUR:R<R?UR85-nUR,SU:a0U["R@"USS2US24URUS5- nU$) a Calculate the CERES residuals (Conditional Expectation Partial Residuals) for a fitted model. Parameters ---------- results : model results instance The fitted model for which the CERES residuals are calculated. focus_exog : int The column of results.model.exog used as the 'focus variable'. frac : float, optional Lowess smoothing parameter for estimating the conditional means. Not used if `cond_means` is provided. cond_means : array_like, optional If provided, the columns of this array are the conditional means E[exog | focus exog], where exog ranges over some or all of the columns of exog other than focus exog. If this is an empty nx0 array, the conditional means are treated as being zero. If None, the conditional means are estimated. Returns ------- An array containing the CERES residuals. Notes ----- If `cond_means` is not provided, it is obtained by smoothing each column of exog (except the focus column) against the focus column. Currently only supports GLM, GEE, and OLS models. z$ceres residuals not available for %sNrgư>rF)r/ return_sorted)axis)!rMrrrr rrrr rLrangerrrrrOmeanrPlinalgsvd flatnonzerorr|r concatenate_get_init_kwdsrNrrSfamilylinkderivdot)r*rNr/rXrMrTix_nfnnfpexogurvtiifcoljr:cfnew_exogklass init_kwargs new_model new_resultrYs r+r"r"*sMD MME ec3_ - -?1123 3"33JFJ #gnn% &E KE IIi e*C  1e8$ A99==*b ^^AH %!R%zz!Y,'XXs4y%++a.9: u{{1~&Aq!tBt5AB!Jq!t '~~uzz!U(3Z@qIH OOE&&(Kekk8;{;IJ[[:22 2F%#s$$%,,##))**A*ABB~~a3"&&!ST'*J,=,=cd,CDD Mr-c URnURUR5- n[U[[ 45(a2X2R RRUR5-nO8[U[[[45(aO[S[U5-5e[U5[LaUR R#U5nOUnUR$UUR&SS2U4-nXS-$)a Returns partial residuals for a fitted model with respect to a 'focus predictor'. Parameters ---------- results : results instance A fitted regression model. focus col : int The column index of model.exog with respect to which the partial residuals are calculated. Returns ------- An array of partial residuals. References ---------- RD Cook and R Croos-Dabrera (1998). Partial residual plots in generalized linear models. Journal of the American Statistical Association, 93:442. z+Partial residuals for '%s' not implemented.N)rMrNpredictrrrrdrerfrSr r r rtyperrrrrrO)r*rNrMrnrT focus_vals r+r!r!s8 MME KK'//+ +E%#s$$ ""(()=)=>> ECc? + + F;'( ( J3$$**:6  y)EJJq)|,DDI  r-cURn[U[[[45(d"[ SUR R-5eURnURn[R"X5upUSS2U4n Uc [U[[45(aSnOSn[URS5n [U 5n U RU5 USS2U 4n UR U n UR n UR#5nU "X{40UD6nSU 0nUbUR%U5 UR&"S 0UD6n[)USS5(d [ S 5e[)UU5nS SKJs Jn [U[[45(amU(afUR2R5UR65n[9US 5(aUUR:-nUR=XU5R'5nOUR X5R'5nUR>nUU4$![*a [ S U-5ef=f)a Residualize the endog variable and a 'focus' exog variable in a regression model with respect to the other exog variables. Parameters ---------- results : regression results instance A fitted model including the focus exog and all other predictors of interest. focus_exog : {int, str} The column of results.model.exog or a variable name that is to be residualized against the other predictors. resid_type : str The type of residuals to use for the dependent variable. If None, uses `resid_deviance` for GLM/GEE and `resid` otherwise. use_glm_weights : bool Only used if the model is a GLM or GEE. If True, the residuals for the focus predictor are computed using WLS, with the weights obtained from the IRLS calculations for fitting the GLM. If False, unweighted regression is used. fit_kwargs : dict, optional Keyword arguments to be passed to fit when refitting the model. Returns ------- endog_resid : array_like The residuals for the original exog focus_exog_resid : array_like The residuals for the focus predictor Notes ----- The 'focus variable' residuals are always obtained using linear regression. Currently only GLM, GEE, and OLS models are supported. z8model type %s not supported for added variable residualsNresid_deviancernr start_params convergedTz>fit did not converge when calculating added variable residualsz '%s' residual type not availabler data_weights) rMrrrr rrrrOrNr rLr]r|rrrrcrrgetattrAttributeError#statsmodels.regression.linear_model regression linear_modelrdweightsrSrr~r rn)r*rNrGrHrIrMrOrNrTrVrm reduced_exogr|rrr[rtrrurOlmr lm_resultsrPs r+r r sR MME ec3_ - -S1123 3 ::D KKE!33JFJ1i<(O ec3Z ( ()J J tzz!} B bBFF92;L>>"%L OOE  ! ! #Fe4V4I L )D J&&J :{D 1 1YZZJj*5 54%#s$$,,&&w';';< 5. ) ) 2 22GVVO7CGGI VVO:>>@ !'' ( (( J;jHIIJs $ H00I )rg?)NNTr)NNN)NNNNNNr)T皙?cooks0?NNN)TrrrrN)rN)NTNN)Q?NN)rN)NTN)8__doc__statsmodels.compat.pandasrstatsmodels.compat.pythonrrnumpyrPr~rpatsyr3statsmodels.genmod.generalized_estimating_equationsr+statsmodels.genmod.generalized_linear_modelrstatsmodels.graphicsr *statsmodels.nonparametric.smoothers_lowessr rr r r &statsmodels.sandbox.regression.predstdrrr_regressionplots_docrrrrr__all__r,rrrrrrrrrformatr;rrDrr#r$r%r"r!r rr-r+rsh /2C;&===E8 =4 8]@bJ!:7;"$BdNcLPf\~>B'+hV  $ $ IG(H IJ >ACG)-; J ;|  $ $ KI(J KLAH/3L # * *>,? @A  A : # * *D,E FGNGN "D&E EF9=BFF6 %D)E EFF2 #!'" "#EI  #.Wr.`;?;?])r-