Logistic Regression not display all data like R

Hello ! I'm using version 2016a and trying to code my previous code from R to Matlab. When I do a Logistic Regression on R, it shows me all coeficients for each variable, like this image:

But when I do it from Matlab (same Data Set, same 'formula'), the results are missing:

modelo = 'LSTATUS ~ METODO + URI + URL + USERAGENT + BROWSER + COOKIE';
mdl = fitglm(FiltroA, modelo, 'Distribution','binomial');
mdl

The Data Set (FiltroA) was imported as a Table

Each field in R represent a category. For example, USERAGENT1 = Windows, USERAGENT2= Linux, etc. Someone know how to use a logistic regression on Matlab to show all categories?

Answer 1

[Brendan Hamm]

Your issue is that in R these variables are interpreted as categorical whereas in MATLAB they are being interpreted as continuous predictor variables. You can either specify them as categorical variables in fitglm:

modelo = 'LSTATUS ~ METODO + URI + URL + USERAGENT + BROWSER + COOKIE';
mdl = fitglm(FiltroA, modelo, 'Distribution','binomial','CategoricalVars',1:6);

or convert each of them to a categorical variable ahead of time.

FiltroA.METODO = categorical(FiltroA.METODO);
% Repeat for other variables

This can all be accomplished with a call to varfun:

Filtro = varfun(@categorical,FiltroA(:,1:end-1)) % Assuming LSTATUS is the last variable in table
Filtro.Properties.VariableNames = FiltroA.Properties.VariableNames(1:end-1);
FiltroA(:,1:end-1) = Filtro;

[Leonardo Silva]

Brendan, the 1st option, using 'CategoricalVars' saved my day. I'll try the other two options after and try to optimize it. TYVM ! :)