www.gusucode.com > stats 源码程序 matlab案例代码 > stats/PlotsToUnderstandTermsEffectsExample.m
%% Plots to Understand Terms Effects % This example shows how to understand the effect of each term in a % regression model using a variety of available plots. %% % Load the sample data. % Copyright 2015 The MathWorks, Inc. load carsmall %% % Create a model of mileage from some predictors in the |carsmall| data. ds = dataset(Weight,MPG,Cylinders); ds.Cylinders = ordinal(ds.Cylinders); mdl = fitlm(ds,'MPG ~ Cylinders*Weight + Weight^2'); %% % Create an added variable plot with |Weight^2| as the added variable. plotAdded(mdl,'Weight^2') %% % This plot shows the results of fitting both |Weight^2| and |MPG| to the terms % other than |Weight^2| . The reason to use |plotAdded| is to understand what % additional improvement in the model you get by adding |Weight^2| . The % coefficient of a line fit to these points is the coefficient of |Weight^2| % in the full model. The |Weight^2| predictor is just over the edge of % significance ( |pValue| < 0.05) as you can see in the coefficients table % display. You can see that in the plot as well. The confidence bounds look % like they could not contain a horizontal line (constant y), so a % zero-slope model is not consistent with the data. %% % Create an added variable plot for the model as a whole. plotAdded(mdl) %% % The model as a whole is very significant, so the bounds don't come close % to containing a horizontal line. The slope of the line is the slope of a % fit to the predictors projected onto their best-fitting direction, or in % other words, the norm of the coefficient vector.