The answer from @Michael B works well, but requires “recreating” the table. The table itself is actually directly available from the summary().tables attribute. Each table in this attribute (which is a list of tables) is a SimpleTable, which has methods for outputting different formats. We can then read any of those formats back as a … Read more
From my understanding “central credible region” is not any different from how confidence intervals are calculated; all you need is the inverse of cdf function at alpha/2 and 1-alpha/2; in scipy this is called ppf ( percentage point function ); so as for Gaussian posterior distribution: >>> from scipy.stats import norm >>> alpha = .05 … Read more
The difference between the Pandas and Statsmodels version lie in the mean subtraction and normalization / variance division: autocorr does nothing more than passing subseries of the original series to np.corrcoef. Inside this method, the sample mean and sample variance of these subseries are used to determine the correlation coefficient acf, in contrary, uses the … Read more
You can use the params property of a fitted model to get the coefficients. For example, the following code: import statsmodels.api as sm import numpy as np np.random.seed(1) X = sm.add_constant(np.arange(100)) y = np.dot(X, [1,2]) + np.random.normal(size=100) result = sm.OLS(y, X).fit() print(result.params) will print you a numpy array [ 0.89516052 2.00334187] – estimates of intercept … Read more
[EDIT] Since this keeps on getting downvoted: I know that median absolute deviation is a more commonly-used statistic, but the questioner asked for mean absolute deviation, and here’s how to do it: from numpy import mean, absolute def mad(data, axis=None): return mean(absolute(data – mean(data, axis)), axis)
If X is your dataframe, try using the .astype method to convert to float when running the model: est = sm.OLS(y, X.astype(float)).fit()
Applying the answer given here I used dir() to print all the attributes of the results object. After that I searched for the one that contained the std err value and it turned out to be: print results.bse (Not sure what the b stands for in bse, but I guess the se stands for “standard … Read more
Try this: X = sm.add_constant(X) sm.OLS(y,X) as in the documentation: An intercept is not included by default and should be added by the user statsmodels.tools.tools.add_constant
For test data you can try to use the following. predictions = result.get_prediction(out_of_sample_df) predictions.summary_frame(alpha=0.05) I found the summary_frame() method buried here and you can find the get_prediction() method here. You can change the significance level of the confidence interval and prediction interval by modifying the “alpha” parameter. I am posting this here because this was … Read more