Regression with tall array (Using datastore, CSV) - Error

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K.P. 2021-7-12

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https://ww2.mathworks.cn/matlabcentral/answers/876723-regression-with-tall-array-using-datastore-csv-error

编辑： K.P. 2021-8-1

采纳的回答： Ive J

Hi

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K.P. 2021-7-12

在 MATLAB Online 中打开

x is a 1000x500 (tall) table. This are the first entries:

6	12	12	15	13	12	30	71	6
4	4	0	0	1	10	2	6	1
0	0	0	0	0	2	0	0	0
0	4	0	0	0	0	0	4	0
3	5	2	0	0	10	0	3	0
26	10	3	0	2	15	7	24	1
85	5	4	0	0	29	0	6	0
0	1	0	0	2	1	0	0	0
0	3	0	0	0	9	0	4	0
18	11	2	0	1	6	0	3	0
1	0	0	0	2	4	0	0	0
0	0	0	0	0	0	0	0	0
0	10	0	0	0	0	0	0	0
0	1	1	0	3	0	0	3	0
16	3	0	0	0	3	2	36	1

y is a 1000x1 (tall) table and the first entries are:

dpb 2021-7-12

在 MATLAB Online 中打开

I just tried to see if it was tall arrays and fitglm

>> X=[1:1000].'; X=tall(X);
>> Y=randn(size(X));                % this is interesting sidelight on the way...
Error using randn
Size inputs must be numeric. 
>> size(X)
ans =
  1×2 tall double row vector
        1000           1
>> Y=randn(1000,1); Y=tall(Y);      % OK, have to brute-force it
>> fitglm(X,Y,'Distribution',"normal")
Iteration [1]:	  0% completed
Iteration [1]:	 50% completed
Iteration [1]:	 100% completed
Iteration [2]:	  0% completed
Iteration [2]:	 50% completed
Iteration [2]:	 100% completed
Iteration [3]:	  0% completed
Iteration [3]:	 100% completed
ans = 
Compact generalized linear regression model:
    y ~ 1 + x1
    Distribution = Normal
Estimated Coefficients:
                    Estimate         SE         tStat      pValue 
                   __________    __________    ________    _______
    (Intercept)     0.0015036      0.064429    0.023338    0.98139
    x1             1.6177e-05    0.00011151     0.14507    0.88468
1000 observations, 998 error degrees of freedom
Estimated Dispersion: 1.04
F-statistic vs. constant model: 0.021, p-value = 0.885
>> 

So, fitglm will accept tall arrays; the syntax must be else where it would seem...

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Answer 1

Ive J 2021-7-13

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https://ww2.mathworks.cn/matlabcentral/answers/876723-regression-with-tall-array-using-datastore-csv-error#answer_745528

编辑：Ive J 2021-7-13

在 MATLAB Online 中打开

Well, your data is tall table, and that's what MATLAB complains about: since your first argument is a table, MATLAB thinks y is modelspec. You have two options:

% 1-feed fitglm with matrix
mdl = fitglm(x{:, :}, y{:, :}, 'Link', 'logit', 'Distribution', 'binomial');
% 2-OR: merge x and y as a table
data = [x, y]; % last column is the dependent variable by default
mdl = fitglm(data, 'Link', 'logit', 'Distribution', 'binomial');