how to calculate the logistic regression of two vectors in matlab

To calculate the logistic regression of two vectors in Matlab, you can use the glmfit function, which fits a logistic regression model for binary classification problems. Here's an example code snippet:

main.m
% example input data
x1 = [1; 2; 3; 4; 5]; % independent variable 1
x2 = [0; 1; 1; 0; 1]; % independent variable 2
y = [0; 0; 1; 0; 1]; % target variable (binary)

% combine inputs into design matrix
X = [ones(size(x1)), x1, x2];

% fit logistic regression model
[beta, dev, stats] = glmfit(X, y, 'binomial');

% display model coefficients
disp(['Intercept: ', num2str(beta(1))]);
disp(['Coeff for x1: ', num2str(beta(2))]);
disp(['Coeff for x2: ', num2str(beta(3))]);

% calculate predicted probabilities
p = glmval(beta, X, 'logit');

% classify inputs based on predicted probabilities
class = round(p);
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In this example, we have two independent variables (x1 and x2) and a target variable y. We first combine the inputs into a design matrix X, which includes a column of ones for the intercept term. We then fit a logistic regression model using glmfit, which returns the model coefficients beta, the deviance dev, and additional statistics in a structure stats.

We can then use the glmval function to calculate the predicted probabilities of our inputs based on the model coefficients. Finally, we can classify our inputs into binary outputs based on a threshold of 0.5 (or using round(p) as in this example).