Demos of the graphics.
The goal of this document is to present the graphics available in this toolbox.
In the script below, we consider a model with 3 inputs and 3 outputs. Then we plot Y versus X.
m=1000; x1=distfun_unifrnd(0,1,m,1); x2=distfun_unifrnd(0,1,m,1); x3=distfun_unifrnd(0,1,m,1); y1=2*x1.*x2+x3; y2=-3*x1+x2.^2-2*x3; y3=sin(x1)-3*x2+3*x3; x=[x1,x2,x3]; y=[y1,y2,y3]; xlabels=["X1","X2","X3"]; ylabels=["Y1","Y2","Y3"]; scf(); plotmatrix(x,y,"xlabels",xlabels,"ylabels",ylabels); | ![]() | ![]() |
The previous script produces the following output.
In the script below, we consider a model with 3 inputs and 3 outputs. Then we plot X versus X, with histograms on the diagonal.
m=1000; x1=distfun_unifrnd(0,1,m,1); x2=distfun_unifrnd(0,1,m,1); x3=distfun_unifrnd(0,1,m,1); y1=2*x1.*x2+x3; y2=-3*x1+x2.^2-2*x3; y3=sin(x1)-3*x2+3*x3; y=[y1,y2,y3]; ylabels=["Y1","Y2","Y3"]; scf(); plotmatrix(y,"xlabels",ylabels); | ![]() | ![]() |
The previous script produces the following output.
In the script below, we create a sample of 10000 random numbers from the chi-square distribution with 3 degrees of freedom. Then we plot the histogram.
The previous script produces the following output.
In the script below, we create two samples of 100 random numbers from the Poisson distribution. Then we create the QQ-plot.
x = distfun_poissrnd(10,100,1); y = distfun_poissrnd(5,100,1); scf(); qqplot(x,y); xtitle("n=100","Quantile Poiss(10)","Quantile Poiss(5)"); | ![]() | ![]() |
The previous script produces the following output.