statsmodels

I am following along with a statsmodels tutorial An OLS model is fitted with formula = 'S ~ C(E) + C(M) + X' lm = ols(formula, salary_table).fit() print lm.summary() Predicted values are provided through: lm.predict({'X' : [12], 'M' : [1], 'E' : [2]}) The result is returned as a single value array. Is there a method to also return confidence intervals for the predicted value (prediction intervals) in statsmodels? Thanks. We've been meaning to make this easier to get to. You should be able to use from statsmodels.sandbox.regression.predstd import wls_prediction_std prstd, iv_l, iv_u = wls

I am trying to write a code to generate a series of arima model and compare different models.The code is as follow. p=0 q=0 d=0 pdq=[] aic=[] for p in range(6): for d in range(2): for q in range(4): arima_mod=sm.tsa.ARIMA(df,(p,d,q)).fit(transparams=True) x=arima_mod.aic x1= p,d,q print (x1,x) aic.append(x) pdq.append(x1) keys = pdq values = aic d = dict(zip(keys, values)) print (d) minaic=min(d, key=d.get) for i in range(3): p=minaic[0] d=minaic[1] q=minaic[2] print (p,d,q) Where 'df' is the time series data.And the output is as follow, (0, 0, 0) 1712.55522759 (0, 0, 1) 1693.436483044094 (0,

It's a useful and common practice to append predicted values and residuals from running a regression onto a dataframe as distinct columns. I'm new to pandas, and I'm having trouble performing this very simple operation. I know I'm missing something obvious. There was a very similar question asked about a year-and-a-half ago, but it wasn't really answered. The dataframe currently looks something like this: y x1 x2 880.37 3.17 23 716.20 4.76 26 974.79 4.17 73 322.80 8.70 72 1054.25 11.45 16 And all I'm wanting is to return a dataframe that has the predicted value and residual from y = x1 + x2

I want to visually explore the relationship between two variables. The functional form of the relationship is not visible in dense scatter plots like this: How can I add a lowess smooth to the scatter plot in Python? Or do you have any other suggestions to visually explore non-linear relationships? I tried the following but it didn't work properly (drawing on an example from Michiel de Hoon ): import numpy as np from statsmodels.nonparametric.smoothers_lowess import lowess x = np.arange(0,10,0.01) ytrue = np.exp(-x/5.0) + 2*np.sin(x/3.0) # add random errors with a normal distribution y = ytrue