Predict response given new input data | GLME
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I am trying to fit a model between a dependent variable (Y) and two predictor variables (X, Z) using GLME.
First I divided the data into training (80%) and validation (20%). I've fitted a GLME model to the training data:
X = Xtr % Validation portion of data (442x1);
Z = Ztr % Validation portion of data (442x1);
Y = Ytr % Validation portion of data (442x1);
trTable = table(Y,X,Z);
mdl_glme = fitglme(trTable,'Y ~ X + (1 | Z) + (-1 + X | Z)')'
Now I want to test this model on new (validation) data. This is where I run into problems. If I try:
X = Xval % Validation portion of data (110x1);
Z = Zval % Validation portion of data (110x1);
Y = Yval % Validation portion of data (110x1);
valTable = table(Y,X,Z);
ypred = predict(mdl_glme,valTable)
I get the following error:
Error using categorical (line 434)
Unable to create default category names. Specify category names using the CATEGORYNAMES
input argument.
Error in nominal (line 152)
b = b@categorical(a,args{:},'Ordinal',false);
Error in classreg.regr.LinearLikeMixedModel/makeInteractionVar (line 806)
G = nominal(ds.(interactionVars{1}));
Error in classreg.regr.LinearLikeMixedModel/extractGroupingInfo (line 205)
model.makeInteractionVar(ds,interactionVars);
Error in GeneralizedLinearMixedModel/predict (line 539)
ginfo = extractGroupingInfo(model,ds);
Though if I inspect trTable and valTable the only difference I can see is the number of rows:
442×3 table (trTable)
Y X Z
_________ _________ ________
1.2107 1.2011 0.098847
110×3 table (valTable)
Y X Z
_________ _________ ______
1.6064 1.6024 1.6024
I would be very grateful for any feedback and/or advice.
Regards J
2 comentarios
the cyclist
el 11 de Oct. de 2018
Can you upload your dataset, or a minimal subset that exhibits the problem?
(Sometimes trying to find the minimal subset that causes the problem will actually help identify the cause.)
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