marjoleinF
commented
6 years ago It's due to winsorizing and normalizing. Variable mp_data$a_nI is almost all zeros, after winsorizing it's all zeros. Then it's divided by its own standard deviation and consequently NaN. Setting winsfrac=0 or normalize=FALSE yields no error. This should at least issue an informative warning though, thanks for reporting the error (and spelling mistake)!
On Thu, May 10, 2018 at 4:47 AM, Max Kuhn notifications@github.com wrote:
The data set has a fair amount of correlated predictors. No idea what would cause the error.
library(QSARdata)> library(pre)> library(sessioninfo)> > data(MeltingPoint)> > mp_data <- MP_Descriptors> mp_data$melt_point <- MP_Outcome> > set.seed(36624)> mod <- pre(melt_point ~ ., data = mp_data, verbose = TRUE)A rule ensemble for prediction of a continuous response will be created. A total of 500 trees and 3790 rules were generated initially. A total of 39 generated rules were perfectly collinear with earlier rules and removed from the initial ensemble. ($duplicates.removed and $complements.removed show which, if any). An initial ensemble consisting of 3751 rules was succesfully created. Error in elnet(x, is.sparse, ix, jx, y, weights, offset, type.gaussian, : NA/NaN/Inf in foreign function call (arg 5)> > session_info() ─ Session info ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── setting value version R version 3.5.0 (2018-04-23) os macOS High Sierra 10.13.4 system x86_64, darwin15.6.0 ui RStudio language (EN) collate en_US.UTF-8 tz America/New_York date 2018-05-09
─ Packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── package version date source clisymbols 1.2.0 2017-05-21 CRAN (R 3.5.0) codetools 0.2-15 2016-10-05 CRAN (R 3.5.0) earth 4.6.2 2018-03-21 CRAN (R 3.5.0) foreach 1.4.4 2017-12-12 CRAN (R 3.5.0) Formula 1.2-2 2017-07-10 CRAN (R 3.5.0) glmnet 2.0-16 2018-04-02 CRAN (R 3.5.0) inum 1.0-0 2017-12-12 CRAN (R 3.5.0) iterators 1.0.9 2017-12-12 CRAN (R 3.5.0) lattice 0.20-35 2017-03-25 CRAN (R 3.5.0) libcoin 1.0-1 2017-12-13 CRAN (R 3.5.0) magrittr 1.5 2014-11-22 CRAN (R 3.5.0) Matrix 1.2-14 2018-04-09 CRAN (R 3.5.0) mvtnorm 1.0-7 2018-01-26 CRAN (R 3.5.0) partykit 1.2-1 2018-04-20 CRAN (R 3.5.0) plotmo 3.3.6 2018-03-21 CRAN (R 3.5.0) plotrix 3.7 2017-12-07 CRAN (R 3.5.0) pre 0.5.0 2018-05-07 CRAN (R 3.5.0) QSARdata 1.3 2013-07-16 CRAN (R 3.5.0) rpart 4.1-13 2018-02-23 CRAN (R 3.5.0) sessioninfo 1.0.0 2017-06-21 CRAN (R 3.5.0) stringi 1.2.2 2018-05-02 CRAN (R 3.5.0) stringr 1.3.0 2018-02-19 CRAN (R 3.5.0) survival 2.42-3 2018-04-16 CRAN (R 3.5.0) TeachingDemos 2.10 2016-02-12 CRAN (R 3.5.0) withr 2.1.2 2018-03-15 CRAN (R 3.5.0) yaml 2.1.19 2018-05-01 CRAN (R 3.5.0)> traceback()4: elnet(x, is.sparse, ix, jx, y, weights, offset, type.gaussian, alpha, nobs, nvars, jd, vp, cl, ne, nx, nlam, flmin, ulam, thresh, isd, intr, vnames, maxit)3: glmnet(x, y, weights = weights, offset = offset, lambda = lambda, ...)2: cv.glmnet(x, y, nfolds = nfolds, weights = weights, family = family, parallel = par.final, standardize = standardize, ...)1: pre(melt_point ~ ., data = mp_data, verbose = TRUE)
Side note: "succesfully" is misspelled. After years of ridiculously poor spelling, I have finally found someone else's typo
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The data set has a fair amount of correlated predictors. No idea what would cause the error.
Side note: "succesfully" is misspelled. After years of ridiculously poor spelling, I have finally found someone else's typo