这部分代码其实比较简单。 %% RFnTree=100;nLeaf=5;RFModel=TreeBagger(nTree,TrainVARI,TrainYield,...'Method','regression','OOBPredictorImportance','on','MinLeafSize',nLeaf);[RFPredictYield,RFPredictConfidenceInterval]=predict(RFModel,TestVARI); 1. 2. 3. 4. 5. 6. 7. 其中,n...
plot(height,leg_length,'k+',height,preresult,'r'); % 95%置信区间 plot(xdata,Y2+DELTA2,'b--'); plot(xdata,Y2-DELTA2,'b--'); legend('Data','Linear Fit','95% Confidence Interval'); xlabel('身高'); ylabel('腿长'); hold off; 4. 残差图 % 女子身高和腿长数据 height = [14...
patch([x';flipud(x')],[y_up';flipud(y_floor')],'r','FaceA',.1,'EdgeA',0); xlabel('Observed values(mg/kg)');ylabel('Predicted values(mg/kg)') % legend('Calibration set','Validation set','Regression line','1:1 line','Confidence interval(α=0.05)'); set(gca,'Xlim',[min...
Compute the regression coefficients for a linear model with an interaction term. X = [ones(size(x1)) x1 x2 x1.*x2]; b = regress(y,X)% Removes NaN data b =4×160.7104 -0.0102 -0.1882 0.0000 Plot the data and the model.
% legend('Calibration set','Validation set','Regression line','1:1 line','Confidence interval(α=0.05)'); set(gca,'Xlim',[min(x),max(x)]); set(gca,'Ylim',[min(x),max(x)]); set(gca,'FontSize',17,'Fontname', 'Times New Roman'); ...
nlparci - Confidence intervals for parameters. Regression Plots. addedvarplot - Created added-variable plot for stepwise regression. nlintool - Interactive graphical tool for prediction in nonlinear models. polytool - Interactive graph for prediction of fitted polynomials. ...
bootstrap confidence intervals for the coefficients of the linear regression model. Create the bootstrap samples from the residuals. Use normal approximated intervals with bootstrapped bias and standard error by specifying'Type','normal'. You cannot use the default confidence interval type in this ...
(tr)%figure, plottrainstate(tr)%figure, ploterrhist(e)%figure, plotregression(t,y)%figure, plotfit(net,x,t)% Deployment% See the help for each generation function for more information.if(false)% Generate MATLAB function for neural network for application% deployment in MATLAB scripts or ...
ANNnet.plotFcns={'plotperform','plottrainstate','ploterrhist','plotregression','plotfit'};[ANNnet,tr]=train(ANNnet,x,t);y=ANNnet(x);e=gsubtract(t,y);performance=perform(ANNnet,t,y);%Recalculate Training,Validation and Test Performance ...
A regression model for the predictor variablesXand the response variableyhas the form y=f(X) +ε, wherefis a function ofXandεis a random noise term. Curve(default) —plotSlicepredicts confidence bounds for the fitted responsesf(X). ...