Find standardized coefficient in linear regression?

How can I get the standardized coefficient (beta) in linear regression? (e.g. the following code):

[overal,s,r] = xlsread('overal.csv');

alpha = overal(:,1);
beta= overal(:,2);
max_slope = overal(:,3);
age= overal(:,4);
Td=overal(:,5);
Diffstress=overal(:,6);

X1=[beta,max_slope,age,Td,Diffstress];
myFit1= LinearModel.stepwise(X1,alpha,'PEnter',0.09)

Answer 1

[Sandeep]

Hi Faezeh Manesh,

To obtain the standardized coefficients (beta) in linear regression, you can use the Coefficients property of the fitted LinearModel object.

You can refer the below code to understand how Coefficients can be used,

[overal,~,~] = xlsread('overal.csv');

alpha = overal(:,1);
beta = overal(:,2);
max_slope = overal(:,3);
age = overal(:,4);
Td = overal(:,5);
Diffstress = overal(:,6);

X1 = [beta, max_slope, age, Td, Diffstress];
myFit1 = LinearModel.stepwise(X1, alpha, 'PEnter', 0.09);

% Get the standardized coefficients
beta_hat = myFit1.Coefficients.Estimate(2:end); % exclude the intercept term
x_means = mean(X1, 1);
x_sds = std(X1, [], 1);
y_sd = std(alpha);

beta = beta_hat .* (x_sds ./ y_sd);

The estimated coefficients are obtained from the Estimate property of Coefficients returned by the LinearModel object. The x_means, x_sds, and y_sd variables are used to compute the standardized coefficients.

[Faezeh Manesh]

Thanks @Sandeep. I used the code that you provided, however, the beta values that are calculated are as follows:

beta =

-0.3294 -16.3266 -19.6832 -1.4097 -0.4325

-0.1212 -6.0053 -7.2400 -0.5185 -0.1591

It is a 2*5 matrix and the coefficients are not standardized ( Standardized values need to be between 0 and 1). Could you please help me modify this.