Fitlm error with predictor and response variables

I am trying to run a regression model on 83 funds and their returns. I have started by dividing them by 100 then subtracting the risk free rate. I am using fama french 6 factor model. I have attached photos for context, R1 is my returns, ff5 is my factor model, Rf is my risk free rate. Any help in understanding why I am getting the error regardign predictor and resposne variables would be apprecaited, when using the fama french 6 model factor how do I integreate this into my analysis. Thanks

[Ive J]

Post your script (or at least the part you have issue with) in a proper way (not image).

The error is self explanatory, the length of independent and dependent variables must be the same.

[John Good]

R1=xlsread(Input_file,Input_sheet,'B2:NG84');

>> R1=R1'; % [N,T]

>> size(R1)

>> R1=R1/100;

I then manually imported the fama french model and risk free returns

>> ff5=ff5'

>> ff5=ff5/100

>> Rf=rf1m/100;

>> rf1m=rf1m';

>> [T,N]=size(R1);

>> r=R1-Rf.*ones(1,N);

>> rhv=ff5;

>> for i=1:N;

lhv=r(:,i);

mdl=fitlm(rhv,lhv);

end;

[Ive J]

So, as I mentioned earlier rhv is 6X83 while lhv is 320X1. You need to clearly define your input variables; did you mean this?

lhv = r(i, :)'; % dim of 83X1
mdl = fitlm(rhv', lhv); % avoid too many unnecessary vector/matrix transpose

[John Good]

The equation seems to have messed up as there a lot of NaN values where they shouldnt be. My coefficients show as the photo attached

[John Good]

any ideas Ive Ive?

[Ive J]

Sorry I haven't received any MATLAB notification on this post.

Please share your data properly (not as image). Show you code also and the output you get from MATLAB. For instance,

x = rand(10, 2); % test predictor
y = rand(10, 1); % test reponse

mdl = fitlm(x, y)

Linear regression model:
    y ~ 1 + x1 + x2

Estimated Coefficients:
                   Estimate       SE        tStat      pValue 
                   _________    _______    ________    _______

    (Intercept)       0.4717    0.25349      1.8609    0.10508
    x1             -0.087938    0.33145    -0.26532    0.79841
    x2              0.010793    0.31584    0.034172    0.97369


Number of observations: 10, Error degrees of freedom: 7
Root Mean Squared Error: 0.27
R-squared: 0.0112,  Adjusted R-Squared: -0.271
F-statistic vs. constant model: 0.0397, p-value = 0.961

Your image simply shows your design matrix is rank deficient (test stats are NaN or Inf), so either your data is problematic or the way you process them. That's why it's crucial to show all details/information you can when you ask a question.