![]() ![]() concat (, axis = 1 ) # model values model_fitted_y = model_fit. fit () # create dataframe from X, y for easier plot handling dataframe = pd. Model_fit : a model after regressing y on X. Y: A numpy array or pandas series/dataframe of the target variable of the linear regression model X: A numpy array or pandas dataframe of the features to use in building the linear regression model plot ( x, y, label = label, lw = 1, ls = '-', color = 'red' ) def diagnostic_plots ( X, y, model_fit = None ): """įunction to reproduce the 4 base plots of an OLS model in R. Helper function for plotting cook's distance lines Wrapping it all in a function 1ĭef graph ( formula, x_range, label = None ): """ Below, I provide the code for the function to reproduce the plots in Python. This was something I had initially set out to do myself but did not find much success. Lastly, there will be readers who after seeing this post will want to reproduce these plots in a systematic way. Furthermore, I showed various ways to interpret them using a sample dataset. In this post I set out to reproduce, using Python, the diagnostic plots found in the R programming language. If the point is removed, we would re-run this analysis again and determine how much the model improved. I would argue that removing the point on the far right of the plot should improve the model. In practice, there may be cases where we may want to remove points with a Cook’s distance of less than 0.5, especially if there are only a few observations compared to the rest of the data. In this plot, we do not have any leverage points that meet this criteria. Thanks to Cook’s Distance, we only need to find leverage points that have a distance greater than 0.5. annotate ( i, xy = ( model_leverage, model_norm_residuals )) įortunately, this arguably one of the easiest plots to interpret. argsort ( model_cooks ), 0 ) for i in leverage_top_3 : plot_lm_4. set_ylabel ( 'Standardized Residuals' ) # annotations leverage_top_3 = np. set_title ( 'Residuals vs Leverage' ) plot_lm_4. set_xlim ( 0, max ( model_leverage ) + 0.01 ) plot_lm_4. columns, data = dataframe, lowess = True, scatter_kws = ) plot_lm_4. hat_matrix_diag # cook's distance, from statsmodels internals model_cooks = model_fit. abs ( model_residuals ) # leverage, from statsmodels internals model_leverage = model_fit. abs ( model_norm_residuals )) # absolute residuals model_abs_resid = np. resid_studentized_internal # absolute squared normalized residuals model_norm_residuals_abs_sqrt = np. resid # normalized residuals model_norm_residuals = model_fit. fittedvalues # model residuals model_residuals = model_fit. # model values model_fitted_y = model_fit. Let’s look at an example in R, and its corresponding output, using the Boston housing data. These 4 plots examine a few different assumptions about the model and the data:ġ) The data can be fit by a line (this includes any transformations made to the predictors, e.g., or )Ģ) Errors are normally distributed with mean zeroģ) Errors have constant variance, i.e., homoscedasticity Each of these plots will focus on the residuals - or errors - of a model, which is mathematical jargon for the difference between the actual value and the predicted value, i.e. We will be looking at four main plots in this post and describe how each of them can be used to diagnose issues in an OLS model. In short, diagnostic plots help us determine visually how our model is fitting the data and if any of the basic assumptions of an OLS model are being violated. This post will leverage a lot of that work and at the end will wrap it all in a function that anyone can cut and paste into their code to reproduce these plots regardless of the dataset. So, I did what most people in my situation would do - I turned to Google for help.Īfter trying different queries, I eventually found this excellent resource that was helpful in recreating these plots in a programmatic way. From using R, I had familiarized myself with debugging and tweaking OLS models with the built-in diagnostic plots, but after switching to Python I didn’t know how to get the original plots from R that I had turned to time and time again. Making the switch to Python after having used R for several years, I noticed there was a lack of good base plots for evaluating ordinary least squares (OLS) regression models in Python. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |