How to generate scatter plot similar to attached scatter plot?

Question

Devendra 2023-8-17

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2009677-how-to-generate-scatter-plot-similar-to-attached-scatter-plot

评论： dpb 2023-8-19

I want to generate scatter plot over laid by the equation of fit and R^2 value. Also the x and y axis lables as attached pdf plot. I am attaching the input data and plot and request you all to kindly suggest me how to do it in matlab. I would appreciate your kind suggestions.

Devendra

1 个评论
显示 -1更早的评论隐藏 -1更早的评论

dpb 2023-8-17

See <example> that shows a quadratic and its equation; simply follow those steps to fit your trendline after using scatter. Remember to set hold on before drawing the trendline to add to the same axis instead of clearing it.

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

KSSV 2023-8-18

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2009677-how-to-generate-scatter-plot-similar-to-attached-scatter-plot#answer_1289167

编辑：KSSV 2023-8-18

在 MATLAB Online 中打开

T = readtable('https://in.mathworks.com/matlabcentral/answers/uploaded_files/1458992/Test.csv') ;

plotregression(T.(1),T.(2))

If you don't have ML toolbox, you may follow:

T = readtable('https://in.mathworks.com/matlabcentral/answers/uploaded_files/1458992/Test.csv') ;

% plotregression(T.(1),T.(2))

x = T.(1) ;

y = T.(2) ;

R = regression(x',y') ;

p = polyfit(x,y,1) ;

figure(2)

scatter(x,y,[],'k','filled')

hold on

plot(y,polyval(p,y),'--b')

str{1} = sprintf('y=%0.2fx+%0.2f\n',p(1),p(2)) ;

str{2} = sprintf('R = %0.2f\n',R) ;

text(2.8,10.5,str)

4 个评论
显示 2更早的评论隐藏 2更早的评论

Devendra 2023-8-19

Please also suggest me how to compute mean square error(MSE) and root mean square error(RMSE) in your scatter plot. I just want to get these values without overlaying over the plot.

Devendra

dpb 2023-8-19

在 MATLAB Online 中打开

<regression> is documented as "Not recommended"; why such an animal was ever introduced is another Q? The better (and recommended) function would be fitlm which will produce all the desired results dirctly.

Nota Bene: The "R" value returned by regression is an unusual choice; it is NOT the normally-reported R^2 ("R-squared") which is the fraction of the total variance explained by the regression but the square root of R^2. Since the data in this case is mostly a blob rather than showing a linear relationship, the value of ~0.5 is pretty misleading; R-sq for these data is 0.22. That makes the lack of correlation much more apparent.

T = readtable('https://in.mathworks.com/matlabcentral/answers/uploaded_files/1458992/Test.csv');

T.Properties.VariableNames={'x','y'}; % make more convenient names

% IF going to use regression, at least use its results instead

% recalculating again later -- m,b are polyfit() coefficients

[R,m,b] = regression(T.x.',T.y.')

R = 0.4712

m = 0.9168

b = 0.6520

scatter(T.x,T.y,[],'k','filled')

hold on

p=[m b]; % slope, intercept for polyval()

plot(T.y,polyval(p,T.y),'--b')

str = sprintf('y=%0.2fx+%0.2f\nR^2 = %0.2f',p(1),p(2),R^2) ;

text(2.8,10.5,str,'interpreter','Tex')

Now use recommended fitlim...

T.Properties.VariableNames={'X','Y'};   % make uppercase for pretty
f=fitlm(T,'Y ~ X')
f = 
Linear regression model:
    Y ~ 1 + X

Estimated Coefficients:
                   Estimate      SE       tStat       pValue  
                   ________    ______    _______    __________

    (Intercept)    0.65202     1.0723    0.60808        0.5442
    X               0.9168     0.1511     6.0677    1.3449e-08


Number of observations: 131, Error degrees of freedom: 129
Root Mean Squared Error: 2.43
R-squared: 0.222,  Adjusted R-Squared: 0.216
F-statistic vs. constant model: 36.8, p-value = 1.34e-08