www.gusucode.com > stats 源码程序 matlab案例代码 > stats/ExamineResidualsOfAPoissonModelExample.m
%% Examine Residuals of a Poisson Model % This example shows how to use residual plots to help you discover errors, % outliers, or correlations in the model. %% % Randomly generate the sample data from Poisson distribution. % Copyright 2015 The MathWorks, Inc. rng default; % For reproducibility X = randn(100,5); mu = exp(X(:,[1 4 5])*[2;1;.5]); y = poissrnd(mu); %% % The data construction has two out of five predictors not affecting the % response, and no intercept term. %% % Fit a Poisson model. mdl = fitglm(X,y,'linear','Distribution','poisson'); %% % Examine the residuals. plotResiduals(mdl) %% % While most residuals cluster near 0, there are several near -18 and +18. % Examine a different residuals plot. plotResiduals(mdl,'fitted') %% % The large residuals don't seem to have much to do with the sizes of the % fitted values. %% % Create a probability plot. plotResiduals(mdl,'probability') %% % Now it is clear. The residuals do not follow a normal distribution. % Instead, they have fatter tails, much as an underlying Poisson % distribution.