According to the following resources, t = sp.stats.t.ppf(0.95, n - m) was corrected to t = sp.stats.t.ppf(0.975, n - m) to reflect a two-sided 95% t-statistic (or one-sided 97.5% t-statistic). stats.t.ppf() accepts the lower tail probability.
Smoothing parameter jmp 9 graph builder code#
This post has been updated with revised code compatible with Python 3.You can select a more advanced technique called residual bootstrapping by uncommenting the second option plot_ci_bootstrap(). The primary confidence interval code ( plot_ci_manual()) is adapted from another source producing a plot similar to the OP. It works fine in Jupyter using %maplotlib inline. I believe that since the legend is outside the figure, it does not show up in matplotblib's popup window. Plt.savefig("filename.png", bbox_extra_artists=(legend,), bbox_inches="tight") Handles, labels = ax.get_legend_handles_labels()ĪnyArtist = plt.Line2D((0, 1), (0, 0), color="#b9cfe7") # create custom artists Plt.title("Fit Plot for Weight", fontsize="14", fontweight="bold") # Figure Modifications -Īx.get_xaxis().set_tick_params(direction="out")Īx.get_yaxis().set_tick_params(direction="out") The following modifications are optional, originally implemented to mimic the OP's desired result. Plot_ci_manual(t, s_err, n, x, x2, y2, ax=ax) Markeredgewidth=1, markeredgecolor="b", markerfacecolor="None"Īx.plot(x, y_model, "-", color="0.1", linewidth=1.5, alpha=0.5, label="Fit") S_err = np.sqrt(np.sum(resid**2) / dof) # standard deviation of the error actual data from predicted valuesĬhi2 = np.sum((resid / y_model)**2) # chi-squared estimates error in dataĬhi2_red = chi2 / dof # reduced chi-squared measures goodness of fit T = stats.t.ppf(0.975, n - m) # t-statistic used for CI and PI bands N = weights.size # number of observations Y_model = equation(p, x) # model using the fit parameters NOTE: parameters here are coefficients P, cov = np.polyfit(x, y, 1, cov=True) # parameters and covariance from of the fit of 1-D polynom.
Smoothing parameter jmp 9 graph builder Pc#
Pc = sp.polyfit(xs, ys + resamp_resid, 1)Īx.plot(xs, sp.polyval(pc, xs), "b-", linewidth=2, alpha=3.0 / float(nboot))Ĭode # Computations. "Visualizing Confidence Intervals", Various Consequences. Upper and Lower bounds (high and low) (optional) Note: sensitive to outliers The density of overlapping lines indicates improved confidence.
It plots `nboot` number of straight lines and outlines the shape of a band. The bootstrap approach iteratively resampling residuals. """Return an axes of confidence bands using a bootstrap approach. """Return an axes of confidence bands using a simple approach. Two detailed options to plot confidence intervals: def plot_ci_manual(t, s_err, n, x, x2, y2, ax=None): I tried to closely emulate your screenshot.
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