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When specifying a formula explicitly (i.e. with components separately defined), the only way to tell inlabru it's a linear predictor is currently to use response ~ ., and to use include/exclude to specify which components to include or exclude, either in multilikelihood models with like() or in generate()/predict() calls, to avoid attempts to evaluate undefined components.
Would be useful to add a method that parses the formula and detects simple nameA + nameB predictors, and also other more complex linear predictors, such as nameA - nameB and nameA + nameB.latent ( with allow_latent=TRUE and/or allow_combine=TRUE). More complex expressions like nameA + Amatrix %*% nameB.latent may also be possible to detect.
The verbose output should also state the result (and ideally the reasoning) of this detection.
The text was updated successfully, but these errors were encountered:
In addition, a linear = TRUE/FALSE option should be added, to help in more general cases like sum(Amatrix %*% nameA.latent), and also to aid debugging.
When specifying a formula explicitly (i.e. with components separately defined), the only way to tell inlabru it's a linear predictor is currently to use
response ~ .
, and to useinclude
/exclude
to specify which components to include or exclude, either in multilikelihood models withlike()
or ingenerate()
/predict()
calls, to avoid attempts to evaluate undefined components.Would be useful to add a method that parses the formula and detects simple
nameA + nameB
predictors, and also other more complex linear predictors, such asnameA - nameB
andnameA + nameB.latent
( withallow_latent=TRUE
and/orallow_combine=TRUE
). More complex expressions likenameA + Amatrix %*% nameB.latent
may also be possible to detect.The verbose output should also state the result (and ideally the reasoning) of this detection.
The text was updated successfully, but these errors were encountered: