statsmodels

(Sorry to ask but http://statsmodels.sourceforge.net/ is currently down and I can't access the docs) I'm doing a linear regression using statsmodels , basically: import statsmodels.api as sm model = sm.OLS(y,x) results = model.fit() I know that I can print out the full set of results with: print results.summary() which outputs something like: OLS Regression Results ============================================================================== Dep. Variable: y R-squared: 0.952 Model: OLS Adj. R-squared: 0.951 Method: Least Squares F-statistic: 972.9 Date: Mon, 20 Jul 2015 Prob (F-statistic): 5

I am trying to use the Python statsmodels linear mixed effects model to fit a model that has two random intercepts, e.g. two groups. I cannot figure out how to initialize the model so that I can do this. Here's the example. I have data that looks like the following (taken from here ): subject gender scenario attitude frequency F1 F 1 pol 213.3 F1 F 1 inf 204.5 F1 F 2 pol 285.1 F1 F 2 inf 259.7 F1 F 3 pol 203.9 F1 F 3 inf 286.9 F1 F 4 pol 250.8 F1 F 4 inf 276.8 I want to make a linear mixed effects model with two random effects -- one for the subject group and one for the scenario group. I am

I tried to calculate logical regression. I have the data as csv file. it looks like node_id,second_major,gender,major_index,year,dorm,high_school,student_fac 0,0,2,257,2007,111,2849,1 1,0,2,271,2005,0,51195,2 2,0,2,269,2007,0,21462,1 3,269,1,245,2008,111,2597,1 .......................... This is my coding. import pandas as pd import statsmodels.api as sm import pylab as pl import numpy as np df = pd.read_csv("Reed98.csv") print df.describe() dummy_ranks = pd.get_dummies(df['second_major'], prefix='second_major') cols_to_keep = ['second_major', 'dorm', 'high_school'] data = df[cols_to_keep]

Here is the sample ADF test in python to check for Cointegration between two pairs. However the final result gives only the numeric value for co-integration. How to get the historical results of Co-integration. Taken from http://www.leinenbock.com/adf-test-in-python/ import numpy as np import statsmodels.api as stat import statsmodels.tsa.stattools as ts x = np.random.normal(0,1, 1000) y = np.random.normal(0,1, 1000) def cointegration_test(y, x): result = stat.OLS(y, x).fit() return ts.adfuller(result.resid) I assume you want to test for expanding cointegration? Note that you should use sm.tsa

I am trying to use python to compute multiple linear regression and multiple correlation between a response array and a set of arrays of predictors. I saw the very simple example to compute multiple linear regression, which is easy. But how to compute multiple correlation with statsmodels? or with anything else, as an alternative. I guess i could use rpy and R, but i'd prefer to stay in python if possible. edit [clarification]: Considering a situation like the one described here: http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704-EP713_MultivariableMethods/ I would like to compute also

I tried forecasting with holt-winters model as shown below but I keep getting a prediction that is not consistent with what I expect. I also showed a visualization of the plot Train = Airline[:130] Test = Airline[129:] from statsmodels.tsa.holtwinters import Holt y_hat_avg = Test.copy() fit1 = Holt(np.asarray(Train['Passengers'])).fit() y_hat_avg['Holt_Winter'] = fit1.predict(start=1,end=15) plt.figure(figsize=(16,8)) plt.plot(Train.index, Train['Passengers'], label='Train') plt.plot(Test.index,Test['Passengers'], label='Test') plt.plot(y_hat_avg.index,y_hat_avg['Holt_Winter'], label='Holt

According to this question How to get constant term in AR Model with statsmodels and Python? . I'm now trying to use the ARMA model to fit the data but again I couldn't find a way to interpret the model's result. Here what I have done according to ARMA out-of-sample prediction with statsmodels and ARMAResults.predict API document . # Parameter INPUT_DATA_POINT = 200 P = 5 Q = 0 # Read Data data = [] f = open('stock_all.csv', 'r') for line in f: data.append(float(line.split(',')[5])) f.close() # Fit ARMA-model using the first piece of data result = arma_model(data[:INPUT_DATA_POINT], P, Q) #

I am trying calculate a regression output using python library but I am unabl;e to get the intercept value when I use the library: import statsmodels.api as sm It prints all the regression analysis except the intercept. but when I use: from pandas.stats.api import ols My code for pandas: Regression = ols(y= Sorted_Data3['net_realization_rate'],x = Sorted_Data3[['Cohort_2','Cohort_3']]) print Regression I get the the intercept with a warning that this librabry will be deprecated in the future so I am trying to use Statsmodels. the warning that I get while using pandas.stats.api: Warning (from