Hi,
I use a mboost model to predict my dependent variable on new data. I get the
following warning message:
In bs(mf[[i]], knots = args$knots[[i]]$knots, degree = args$degree, :
some 'x' values beyond boundary knots may cause ill-conditioned bases
The new predicted values are partly negative although the variable in the
training data ranges from 3 to 8 on a numeric scale. In order to restrict the
predicted values to the value range from 3 to 8 I limit the feature space of
the prediction data on the minima and maxima of the training data for every
predictor variable before applying the model on the new data.
As baselearner in mboost I use splines ("bbs"):
mod <- mboost(MF ~ bbs(predictor1) + bbs(predictor2) + bbs(...), data = train)
I wonder why there are negative values when applying the model on new data,
because both, training and prediction data have the same value ranges in the
predictor variables.
Did somebody get the same warning message? Can someone help me please?
TIM
------------------------------------------
Tim Häring
Bavarian State Institute of Forestry
Department of Forest Ecology
Hans-Carl-von-Carlowitz-Platz 1
D-85354 Freising
E-Mail: [email protected]
http://www.lwf.bayern.de
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