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edited Nov 4 at 16:39

user_20201213

Update: This new R package gam.hp seems to be the solution I have been looking for: It contains basically one function (gam.hp()) that takes GAMs and GAMMs constructed with the mgcv package and calculatesdecomposes either Adjusted R² or explained Deviance from the model into relative contributions per each predictor inof the model formula. It also calculates which fraction of the explained deviance or R² is uniquely attributable to a certain predictor and the publication reference below discusses in how far that it useful to consider. Computation can take a while for complexer models. Further details see here:

Reference:

Lai J, Tang J, Li T, Zhang A, Mao L (2024) Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package. Plant Diversity, 46, 542-546. [Link]

Update: This new R package gam.hp seems to be the solution I have been looking for: It contains basically one function (gam.hp()) that takes GAMs and GAMMs constructed with the mgcv package and calculates either Adjusted R² or explained Deviance per each predictor in the model formula. It also calculates which fraction of the explained deviance or R² is uniquely attributable to a certain predictor and the publication reference below discusses in how far that it useful to consider. Computation can take a while for complexer models. Further details see here:

Reference:

Lai J, Tang J, Li T, Zhang A, Mao L (2024) Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package. Plant Diversity, 46, 542-546. [Link]

Update: This new R package gam.hp seems to be the solution I have been looking for: It contains basically one function (gam.hp()) that takes GAMs and GAMMs constructed with the mgcv package and decomposes either Adjusted R² or explained Deviance from the model into relative contributions per each predictor of the model formula. It also calculates which fraction of the explained deviance or R² is uniquely attributable to a certain predictor and the publication reference below discusses in how far that it useful to consider. Computation can take a while for complexer models. Further details see here:

Reference:

Lai J, Tang J, Li T, Zhang A, Mao L (2024) Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package. Plant Diversity, 46, 542-546. [Link]

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created Nov 4 at 15:27

user_20201213

Update: This new R package gam.hp seems to be the solution I have been looking for: It contains basically one function (gam.hp()) that takes GAMs and GAMMs constructed with the mgcv package and calculates either Adjusted R² or explained Deviance per each predictor in the model formula. It also calculates which fraction of the explained deviance or R² is uniquely attributable to a certain predictor and the publication reference below discusses in how far that it useful to consider. Computation can take a while for complexer models. Further details see here:

Reference:

Lai J, Tang J, Li T, Zhang A, Mao L (2024) Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package. Plant Diversity, 46, 542-546. [Link]