statsmodels

I'm not sure why I'm getting slightly different results for a simple OLS, depending on whether I go through panda's experimental rpy interface to do the regression in R or whether I use statsmodels in Python. import pandas from rpy2.robjects import r from functools import partial loadcsv = partial(pandas.DataFrame.from_csv, index_col="seqn", parse_dates=False) demoq = loadcsv("csv/DEMO.csv") rxq = loadcsv("csv/quest/RXQ_RX.csv") num_rx = {} for seqn, num in rxq.rxd295.iteritems(): try: val = int(num) except ValueError: val = 0 num_rx[seqn] = val series = pandas.Series(num_rx, name="num_rx")

I need to serialise scikit-learn/statsmodels models such that all the dependencies (code + data) are packaged in an artefact and this artefact can be used to initialise the model and make predictions. Using the pickle module is not an option because this will only take care of the data dependency (the code will not be packaged). So, I have been conducting experiments with Dill . To make my question more precise, the following is an example where I build a model and persist it. from sklearn import datasets from sklearn import svm from sklearn.preprocessing import Normalizer import dill digits =

I want to have a coefficient and Newey-West standard error associated with it. I am looking for Python library (ideally, but any working solutions is fine) that can do what the following R code is doing: library(sandwich) library(lmtest) a <- matrix(c(1,3,5,7,4,5,6,4,7,8,9)) b <- matrix(c(3,5,6,2,4,6,7,8,7,8,9)) temp.lm = lm(a ~ b) temp.summ <- summary(temp.lm) temp.summ$coefficients <- unclass(coeftest(temp.lm, vcov. = NeweyWest)) print (temp.summ$coefficients) Result: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.0576208 2.5230532 0.8155281 0.4358205 b 0.5594796 0.4071834 1.3740235 0

It seems scipy once provided a function mad to calculate the mean absolute deviation for a set of numbers: http://projects.scipy.org/scipy/browser/trunk/scipy/stats/models/utils.py?rev=3473 However, I can not find it anywhere in current versions of scipy. Of course it is possible to just copy the old code from repository but I prefer to use scipy's version. Where can I find it, or has it been replaced or removed? The current version of statsmodels has mad in statsmodels.robust : >>> import numpy as np >>> from statsmodels import robust >>> a = np.matrix( [ ... [ 80, 76, 77, 78, 79, 81, 76, 77,

Short version : I was using the scikit LinearRegression on some data, but I'm used to p-values so put the data into the statsmodels OLS, and although the R^2 is about the same the variable coefficients are all different by large amounts. This concerns me since the most likely problem is that I've made an error somewhere and now I don't feel confident in either output (since likely I have made one model incorrectly but don't know which one). Longer version : Because I don't know where the issue is, I don't know exactly which details to include, and including everything is probably too much. I

I am using 32 bit cmd, 64 bit windows, python 2.7 when I type the command pip install statsmodels I get the following error for some module of scipy... Failed building wheel for Scipy Failed cleaning build dir for scipy be_good_do_good install numpy pip install numpy If you face installation issues for numpy, get the pre-built windows installers from http://www.lfd.uci.edu/~gohlke/pythonlibs/ for your python version (python version is different from windows version). numpy 32-bit: numpy-1.11.1+mkl-cp27-cp27m-win32.whl numpy 64-bit: numpy-1.11.1+mkl-cp27-cp27m-win_amd64.whl Later you require VC