Error in fitlm function

I am using

reg = fitlm(X_train, Y_train);

But it gives following errors;

Error in fitlm (line 122)

model = LinearModel.fit(X,varargin{:});

I request you to kindly have a look on attached matlab code and input file and suggest me how to fix it.

Devender

test()

df = 100×20 table
    Var1    POS value    MAX sum    AVG sum    SOS     POS    EOS     Base     Duration    First half    Second half    Growth rate    Senescence rate    Peaks    total_cane    act_y_ha    act_per_ha    field_yield    total_FS       variety   
    ____    _________    _______    _______    ____    ___    ___    ______    ________    __________    ___________    ___________    _______________    _____    __________    ________    __________    ___________    ________    _____________

      0      3.1771      149.35     92.117       79    334    729    2.9793      650          255            395           2.6824          -1.5491          2         8719         1026        118.7          11.57        1008.8     {'NCO334'   }
      1      3.9655      180.34     122.08       19    229    729    3.8176      710          210            500           3.6809           -2.633          3        23962         1653        188.9          11.43          2739     {'CO740'    }
      2      3.3081      156.67     103.54       99    334    729    3.0564      630          235            395           2.8323           -1.408          1        28828         1386          159          11.47        3307.5     {'NCO334'   }
      3      3.4482      139.52     85.993       39    229    759    3.2743      720          190            530           2.5075          -2.7583          1         6108          364         31.7           8.72         532.6     {'CO740'    }
      4      3.8306      176.85      119.2       19    294    729    3.6499      710          275            435           3.5651          -2.2468          2        44705         1739        208.8             12        5365.5     {'NCO334'   }
      5      3.4701      145.87     111.34       99    319    699    3.2575      600          220            380           3.0761          -1.0609          1         4604         2093        233.1          11.14         512.9     {'Mex54-245'}
      6       3.733      196.27     145.09       39    294    789    3.5574      750          255            495           3.2482          -3.4094          3        38435         2159        267.6          12.39        4762.6     {'NCO334'   }
      7      3.0975      167.82     123.67     -236    114    454    2.8192      690          350            340           2.5781          -2.4782          2         7567         1182        117.6           9.95         752.9     {'Mex54-245'}
      8      3.2312      150.91     119.68      129    334    729    2.9267      600          205            395           1.8209          -1.4896          1         5698         1266          143          11.29         643.3     {'B41227'   }
      9      3.6544      188.29     123.56       79    294    759    3.4433      680          215            465           3.3051           -2.348          3        21706         1632        203.4          12.47        2705.7     {'NCO334'   }
     10      3.8049       167.1     119.96      129    319    729    3.5039      600          190            410           2.1563          -2.0176          3         5500         1618        189.9          11.74         645.7     {'Mex54-245'}
     11      3.7917      162.24     113.38       99    324    699    3.6036      600          225            375           3.4075          -1.1251          2        28625         2157        248.2          11.51        3293.8     {'Mex54-245'}
     12      2.6391      109.44     92.363      129    334    669    2.3069      540          205            335          0.48525         -0.70277          1         6729         1373        144.7          10.54         709.1     {'C86/56'   }
     13       3.525      162.31     123.12      129    229    759    3.2314      630          100            530           1.9618          -1.8762          2        12643         1642        191.3          11.65        1472.7     {'NCO334'   }
     14      3.3622      168.53     130.99      129    334    759    3.0431      630          205            425            1.839          -1.7134          3        12237         1654        199.5          12.07        1476.5     {'B52298'   }
     15      4.3618      184.75     134.22       19    294    759    4.1654      740          275            465           3.3812          -3.3908          2        28287         1911        222.7          11.65        3295.8     {'B52298'   }

X = 100×13 table
    POS value    MAX sum    AVG sum    SOS     POS    EOS     Base     Duration    First half    Second half    Growth rate    Senescence rate    Peaks
    _________    _______    _______    ____    ___    ___    ______    ________    __________    ___________    ___________    _______________    _____

     3.1771      149.35     92.117       79    334    729    2.9793      650          255            395           2.6824          -1.5491          2  
     3.9655      180.34     122.08       19    229    729    3.8176      710          210            500           3.6809           -2.633          3  
     3.3081      156.67     103.54       99    334    729    3.0564      630          235            395           2.8323           -1.408          1  
     3.4482      139.52     85.993       39    229    759    3.2743      720          190            530           2.5075          -2.7583          1  
     3.8306      176.85      119.2       19    294    729    3.6499      710          275            435           3.5651          -2.2468          2  
     3.4701      145.87     111.34       99    319    699    3.2575      600          220            380           3.0761          -1.0609          1  
      3.733      196.27     145.09       39    294    789    3.5574      750          255            495           3.2482          -3.4094          3  
     3.0975      167.82     123.67     -236    114    454    2.8192      690          350            340           2.5781          -2.4782          2  
     3.2312      150.91     119.68      129    334    729    2.9267      600          205            395           1.8209          -1.4896          1  
     3.6544      188.29     123.56       79    294    759    3.4433      680          215            465           3.3051           -2.348          3  
     3.8049       167.1     119.96      129    319    729    3.5039      600          190            410           2.1563          -2.0176          3  
     3.7917      162.24     113.38       99    324    699    3.6036      600          225            375           3.4075          -1.1251          2  
     2.6391      109.44     92.363      129    334    669    2.3069      540          205            335          0.48525         -0.70277          1  
      3.525      162.31     123.12      129    229    759    3.2314      630          100            530           1.9618          -1.8762          2  
     3.3622      168.53     130.99      129    334    759    3.0431      630          205            425            1.839          -1.7134          3  
     4.3618      184.75     134.22       19    294    759    4.1654      740          275            465           3.3812          -3.3908          2  

Y = 100×1

1026 1653 1386 364 1739 2093 2159 1182 1266 1632

Error using classreg.regr.TermsRegression.createFormula
The terms matrix must have one column for each variable in the dataset or table. You may not specify the response as a separate vector with a dataset or table.

Error in LinearModel.createFormula (line 1076)
formula = classreg.regr.TermsRegression.createFormula(supplied,modelDef, ...

Error in LinearModel.fit (line 1058)
model.Formula = LinearModel.createFormula(supplied,modelDef,X, ...

Error in fitlm (line 122)
model = LinearModel.fit(X,varargin{:});

Error in test (line 27)
reg = fitlm(X_train, Y_train);

Answer 1

[Walter Roberson]

% Load data
df = readtable('input_file.csv','VariableNamingRule','preserve');
dependent_vars = {'act_y_ha', 'act_per_ha'};

for jj = 1:length(dependent_vars)
var = dependent_vars{jj};

v =  {'POS value', 'MAX sum', 'AVG sum', 'SOS', 'POS', 'EOS', ...
    'Base', 'Duration', 'First half', 'Second half', 'Growth rate', ...
    'Senescence rate', 'Peaks'};
     v_to_use = v(ismember(v,df.Properties.VariableNames));
     new_T  = df(:,v_to_use);
          
     X = new_T;
     Y = df{:, var};
        
         % Split data
        cv = cvpartition(size(X, 1), 'HoldOut', 0.2);
        idxTrain = training(cv);
        idxTest = test(cv);

        X_train = X{idxTrain, :};
        X_test = X{idxTest, :};
        Y_train = Y(idxTrain);
        Y_test = Y(idxTest);
        reg = fitlm(X_train, Y_train, 'linear');
               
        % Prediction
        Y_pred = predict(reg, X_test);
               
        % Evaluation
        r2_reg = 1 - sum((Y_test - Y_pred).^2)/sum((Y_test - mean(Y_test)).^2);
        rmse = sqrt(mean((Y_test - Y_pred).^2));
        disp([ char(var), ' ', num2str(r2_reg,2), ' ', num2str(rmse,2)]);
     end

Warning: Regression design matrix is rank deficient to within machine precision.

act_y_ha 0.66 2.3e+02

Warning: Regression design matrix is rank deficient to within machine precision.

act_per_ha 0.6 32