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: tim.haer...@lwf.bayern.de http://www.lwf.bayern.de ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.