statsmodels

I have tried with both the (pandas)pd.ols and the (statsmodels)sm.ols to get a regression scatter plot with the regression line , I can get the scatter plot but I can't seem to get the parameters to get the regression line to plot. It is probably obvious that I am doing some cut and paste coding here :-( (using this as a guide: http://nbviewer.ipython.org/github/weecology/progbio/blob/master/ipynbs/statistics.ipynb My data is in a pandas DataFrame and the x column is merged2[:-1].lastqu and the y data column is merged2[:-1].Units My code is now as follows: to get the regression: def fit_line2

I'm fairly new to python and pandas (from using SAS as my workhorse analytical platform), so I apologize in advance if this has already been asked / answered. (I've searched through the documentation as well as this site searching for answer and haven't been able to find something yet.) I've got a dataframe (called resp) containing respondent level survey data. I want to perform some basic descriptive statistics on one of the fields (called anninc [short for annual income]). resp["anninc"].describe() Which gives me the basic stats: count 76310.000000 mean 43455.874862 std 33154.848314 min 0

If I understand the OLS model correctly, this should never be the case? trades['const']=1 Y = trades['ret']+trades['comms'] #X = trades[['potential', 'pVal', 'startVal', 'const']] X = trades[['potential', 'pVal', 'startVal']] from statsmodels.regression.linear_model import OLS ols=OLS(Y, X) res=ols.fit() res.summary() If I turn the const on, I get a rsquared of 0.22 and with it off, I get 0.43. How is that even possible? Josef see the answer here Statsmodels: Calculate fitted values and R squared Rsquared follows a different definition depending on whether there is a constant in the model or

I have a correlation plot for two variables, the predictor variable (temperature) on the x-axis, and the response variable (density) on the y-axis. My best fit least squares regression line is a 2nd order polynomial. I would like to also plot confidence and prediction intervals. The method described in this answer seems perfect. However, my dataset (n=2340) has repeated entries for many (x,y) pairs. My resulting plot looks like this: Here is my relevant code (slightly modified from linked answer above): import numpy as np import pandas as pd import matplotlib.pyplot as plt from statsmodels

I am running a regression as follows ( df is a pandas dataframe): import statsmodels.api as sm est = sm.OLS(df['p'], df[['e', 'varA', 'meanM', 'varM', 'covAM']]).fit() est.summary() Which gave me, among others, an R-squared of 0.942 . So then I wanted to plot the original y-values and the fitted values. For this, I sorted the original values: orig = df['p'].values fitted = est.fittedvalues.values args = np.argsort(orig) import matplotlib.pyplot as plt plt.plot(orig[args], 'bo') plt.plot(orig[args]-resid[args], 'ro') plt.show() This, however, gave me a graph where the values were completely off

I got good use out of pandas' MovingOLS class (source here ) within the deprecated stats/ols module. Unfortunately, it was gutted completely with pandas 0.20. The question of how to run rolling OLS regression in an efficient manner has been asked several times ( here , for instance), but phrased a little broadly and left without a great answer, in my view. Here are my questions: How can I best mimic the basic framework of pandas' MovingOLS ? The most attractive feature of this class was the ability to view multiple methods/attributes as separate time series--i.e. coefficients, r-squared, t

I have tried looking through multiple statistics modules for Python but can't seem to find any that support one-way ANOVA post hoc tests. one way ANOVA can be used like from scipy import stats f_value, p_value = stats.f_oneway(data1, data2, data3, data4, ...) This is one way ANOVA and it returns F value and P value. There is significant difference If the P value is below your setting. The Tukey-kramer HSD test can be used like from statsmodels.stats.multicomp import pairwise_tukeyhsd print pairwise_tukeyhsd(Data, Group) This is multicomparison. The output is like Multiple Comparison of Means -