Binary Logistic Regression - beginner

Question

Amina Ag 2021-5-27

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/841735-binary-logistic-regression-beginner

评论： the cyclist 2021-5-29

Hello,

Begginer question regarding logstic regressions in matlab. I am trying to run a binary logistic regression in Matlab but cannot seem to find the code to be able to do so. I am finding it for multinomial but not for binary. Could someone please lead me in the right direction?

I have a table of more than 60 variables and 200 000 rows that I want to run.

Thank you for your time and help!

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

the cyclist 2021-5-27

1
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/841735-binary-logistic-regression-beginner#answer_710945

If you have the Statistics and Machine Learning Toolbox, you can use the fitglm function to fit a binomial logistic regression. See the first example on that page.

8 个评论
显示 6更早的评论隐藏 6更早的评论

the cyclist 2021-5-28

在 MATLAB Online 中打开

fitglm will fit many different types of models. That is why it is "generalized".

Did you look at the first example on the documentation page, as I suggested? That model is a binary logistic regression, exactly as you describe.

People often get confused by the terminology "linear". Linear refers to the fact that fitting equation will be linear in the coefficients. The curve itself is not (necessarily) linear.

Here is a simplified version of that first example, showing a logistic regression for Weight vs. Smoker, and the fit. (I could also have included many more variables, and interaction terms, but I wanted something simple to calculate and plot.)

load hospital

dsa = hospital(:,[4 5]);

modelspec = 'Smoker ~ Weight';

mdl = fitglm(dsa,modelspec,'Distribution','binomial')

mdl =

Generalized linear regression model: logit(Smoker) ~ 1 + Weight Distribution = Binomial Estimated Coefficients: Estimate SE tStat pValue ________ _________ _______ ________ (Intercept) -3.3983 1.3198 -2.5749 0.010027 Weight 0.017541 0.0082503 2.1261 0.033495 100 observations, 98 error degrees of freedom Dispersion: 1 Chi^2-statistic vs. constant model: 4.68, p-value = 0.0304

w = (50:250)';

figure

hold on

scatter(dsa.Weight,dsa.Smoker)

plot(w,predict(mdl,w))

Amina Ag 2021-5-29

That worked perfectly and I found the guilty variable but I am not quite sure why it is not working? The varibale is the log of assets. If I want to include this variable how can i transform it so that the model can read it?

I also have 5 categorical variables that I want to include. Is it possible to include these without making them dummyvar? I know STATA reads it as an categorical automatically and it is not necessary to make a dummy, would it be possible to do the same in matlab?

the cyclist 2021-5-29

If the variable is the log of assets, I'll wager that what has happened is that one of your asset values is unexpectedly negative. The log of a negative number is complex.

Take a look at the Name-Value Pair Arguments section of the fitglm documentation. There is a CategoricalVars input that allows you to specify which of your explanatory variables is categorical. Alternatively, you could convert the variable to categorical before entering it into the model.

请先登录，再进行评论。