Calculate and plot Statsmodels OLS and WLS confidence intervals

ci.py

from statsmodels.stats.outliers_influence import summary_table
from statsmodels.sandbox.regression.predstd import wls_prediction_std

# carry out yr fit

# ols cinv
st, data, ss2 = summary_table(ols_fit, alpha=0.05)

fittedvalues = data[:,2]
predict_mean_se  = data[:,3]
predict_mean_ci_low, predict_mean_ci_upp = data[:,4:6].T
predict_ci_low, predict_ci_upp = data[:,6:8].T

# wls cinv
prstd, iv_l, iv_u = wls_prediction_std(wls_fit)

# plot OLS
cil, = plt.plot(x,predict_ci_low, 'r--', lw=1, alpha=0.5)
ciu, = plt.plot(x,predict_ci_upp, 'r--', lw=1, alpha=0.5)
mcil, = plt.plot(x,predict_mean_ci_low, 'b--', lw=1, alpha=0.5)
mciu, = plt.plot(x,predict_mean_ci_upp, 'b--', lw=1, alpha=0.5)

# plot WLS
wciu, = ax.plot(x, iv_u, 'r--', lw=1, alpha=0.5)
wcil, = ax.plot(x, iv_l, 'r--', lw=1, alpha=0.5)