Jeff Miller asked . 2022-07-15

Error when using fitrm - Fit Repeated Measures Model for RM ANOVA

I am trying to perform repeated measures ANOVA using the fitrm function, but keep getting the following error:

      *Error using eig*
      *Input matrix contains NaN or Inf.*

I've created a minimal working example based on the Matlab documentation for fitrm, but with some of my own variables (my actual list of measurements is much longer). I am trying to determine if the measurements from the 4 participants can be considered as statistically equivalent, while preserving the relationship between the data points (so all L1 data was collected at location 1 for all participants, L2 was collected at location 2, etc).

Here is my minimal example code (including data):

    % Data collected from 4 participants in 3 locations. 
    % Each row is a participant, each column is a location
    measurements = [  112.7258   92.2979   82.5488;
      123.7502  113.2852   84.8053;
      106.8964   93.9526   70.9359;
      107.3634   85.2672   65.7928]

    P_Name = ['A' 'B' 'C' 'D']' % participant name

    %Create a table, Data comes first, Variable names second
    t = table(P_Name,measurements(:,1),measurements(:,2),measurements(:,3),'VariableNames',{'Participant','L1','L2','L3'})

    % Create a location variable for the measurements
    Location = table([1 2 3]','VariableNames',{'Location'})

    % Fit repeated measures model
    rm = fitrm(t,'L1-L3~Participant','WithinModel',Location)

Any help is greatly received.

fitrm , anova, statistics , repeated measures , AI, Data Science, and Statistics , Statistics and M

Expert Answer

Neeta Dsouza answered . 2024-12-21 00:47:28

This seems like a slightly nonstandard problem to me so I am not sure, but here is one way you might approach it.

measurements = [...];
AllCombos = allcomb(1:3,1:4);  % From MATLAB file exchange
[a, b, c] = anovan(measurements(1:end),AllCombos,'Model','full');

anovan basically fits an additive model where each score is decomposed into a sum of a constant, a participant effect, a location effect, and a residual. The Sum Sq. column of the anovan output gives you a breakdown of the sums of squares for the participant, location, and residual terms.

Note that the error is zero. This is because you have only 1 score per participant/location combination, so there is no way to estimate a true random component associated with each participant/location combination score. In this sort of situation, the residual is often used as a guestimate of random error (e.g., to compute an F test).

You might be able to use the sums of square to answer your question (as I understand it). You could compute a table like this for each of your measurement arrays, and then probably convert the SS within each array to a % of the total for that array. (This would certainly make sense if your measurement arrays had different units.)

Then, you could compare across arrays: What % of the variation is explained by participants? What % is explained by location? What % is explained by the random or idiosyncratic participant/location combination effects? It seems like this would give you some meaningful basis on which to compare the different measurement arrays.

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