NaN values in fitglm

Hello, I have 2 models, whose coefficients matrix has been designed. I would like to use these matrices and the response variable to obtain the values for the predictors in my models. I have used fitglm for the same. My code is as follows:

Some background: I have 6 conditions in my response variable, (so Y has 6 values), the values to which I am trying to model with my predictors - model 1 has 3 predictors and model 2 has 6.

In model 2, the first 3 predictors are same as model 1, and the next 3 are interactions between the first 3. I have included the interactions as distinct terms.

 for type = 1:2
        if type == 1

            %ADDITIVE MODEL
            %bar graph specifications
            r = [2 0 0;1 1 0;1 0 1;0 2 0;0 1 1;0 0 2];

        elseif type == 2

            %MIXED MODEL - DIFF EMO ONLY
            %bar graph specifications
            r = [2 0 0 0 0 0;1 1 0 1 0 0;1 0 1 0 1 0;0 2 0 0 0 0;0 1 1 0 0 1 ;0 0 2 0 0 0 ];
        end

        %Solving the linear model
        estimate = [];
        SE = [];
        tstat = [];
        pValue = [];
        
        adj_R_sq = [];
        aic = [];
        
        for i = 1:length(data_final)
            
            %Getting the response for one condition
            y = data_final(i,:)';
            mdl = fitglm(r,y,'Intercept',false);
            
            %saving the needed variables
            estimate = cat(1,estimate,mdl.Coefficients.Estimate');
            SE = cat(1,SE,mdl.Coefficients.SE');
            tstat = cat(1,tstat,mdl.Coefficients.tStat');
            pValue = cat(1,pValue,mdl.Coefficients.pValue');
            
            % Getting R-squared
            R_squared = mdl.Rsquared.Ordinary;
            % Get the number of predictors and observations
            num_predictors = mdl.NumPredictors;
            num_observations = mdl.NumObservations;
            % Calculate adjusted R-squared
            adjusted_R_squared = 1 - ((1 - R_squared) * (num_observations - 1) / (num_observations - num_predictors - 1));
            adj_R_sq = cat(1,adj_R_sq,adjusted_R_squared);

            %Saving AIC 
            aic = cat(1,aic,mdl.ModelCriterion.AIC);
                
        end

My first model works well, but my second model gives estimates, but the other values are all NaN. Please help me figure out if:

1. I am going about the right way for my question
2. If so, if this NaN issue can be sorted.

Thanks for any help in advance.

Answer 1

[Debadipto]

Hello @Sanjana,

The NaN values you're encountering could be due to a conflict where one function is being inadvertently masked by another. To address this, I recommend resetting your MATLAB path to its default settings. Here's how you can do it:

1. First, safeguard your current search path with a backup. Run this command to find out where 'pathdef.m' is located:

>> which -all pathdef

If you've customized your 'pathdef.m' with additional paths, make sure to copy this file to a secure location outside of your MATLAB path. This precaution is optional but important if you have made specific changes to your path settings.

2. Next, execute the following commands in the MATLAB Command Window. These will reset your search path to its original state and refresh the toolbox cache:

>> restoredefaultpath

>> rehash toolboxcache

3. Once you've completed the above steps, try using MATLAB again to check if the NaN issue has been resolved. If everything is working correctly, you can then save the updated search path with this command:

>> savepath

Hope you should be able to eliminate any function conflicts that might be causing the NaN value anomaly.

[Sanjana H]

Hello, thank you for your response. Even after trying these steps, the issue still persists. Is there anything else that might be causing this?