* using log directory ‘/data/localhost/ripley/R/packages/tests-MKL/FDboost.Rcheck’
* using R Under development (unstable) (2026-10-28 r90786)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2)
    GNU Fortran (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2)
* running under: Fedora Linux 44 (Server Edition)
* using session charset: UTF-8
* current time: 2026-10-29 12:27:58 UTC
* using option ‘--no-stop-on-test-error’
* checking for file ‘FDboost/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘FDboost’ version ‘1.1-4’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘FDboost’ can be installed ... [13s/28s] OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [26s/61s] OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking sizes of PDF files under ‘inst/doc’ ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... [26s/76s] OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘factorize_test_irregular.R’
  Running ‘factorize_test_regular.R’
  Running ‘general_tests.R’ [11s/14s]
 [17s/24s] ERROR
Running the tests in ‘tests/general_tests.R’ failed.
Complete output:
  > 
  > 
  > library(FDboost)
  Loading required package: mboost
  Loading required package: parallel
  Loading required package: stabs
  This is FDboost 1.1-4. 
  > library(gamboostLSS)
  Loading required package: gamlss.dist
  
  Attaching package: 'gamlss.dist'
  
  The following object is masked from 'package:mboost':
  
      Family
  
  
  Attaching package: 'gamboostLSS'
  
  The following object is masked from 'package:stats':
  
      model.weights
  
  > 
  > # print(sessionInfo())
  > 
  > # simulated data ----------------------------------------------------------
  > 
  > 
  > if(require(refund)){
  +   
  +   old_mc_cores <- getOption("mc.cores")
  +   options(mc.cores = 1L)
  +   on.exit(options(mc.cores = old_mc_cores), add = TRUE)
  +   
  +   ## simulate a small data set 
  +   print("simulate data")
  +   set.seed(230)
  +   pffr_data <- suppressWarnings(pffrSim(n = 25, nxgrid = 21, nygrid = 19))
  +   pffr_data$X1 <- scale(pffr_data$X1, scale = FALSE)
  +   
  +   dat <- as.list(pffr_data)
  +   dat$tvals <- attr(pffr_data, "yindex")
  +   dat$svals <- attr(pffr_data, "xindex")
  +   
  +   dat$Y_scalar <- dat$Y[ , 10]
  +   
  +   dat$Y_long <- c(dat$Y)
  +   dat$tvals_long <- rep(dat$tvals, each = nrow(dat$Y))
  +   dat$id_long <- rep(seq_len(nrow(dat$Y)), ncol(dat$Y))
  +   
  +   # second functional covariate
  +   dat$s2 <- seq(0, 1, l = 15)
  +   dat$X2 <- I(matrix(rnorm(25 * 15), nrow = 25))
  +   dat$X2 <- scale(dat$X2, scale = FALSE)
  +   
  + 
  +   # model fit ---------------------------------------------------------------
  + 
  +   print("model fit")
  +   
  +   ## response matrix for response observed on one common grid 
  +   m <- FDboost(Y ~ 1 + bhist(X1, svals, tvals, knots = 10, df = 6) 
  +                + bsignal(X1, svals, knots = 6, df = 3)
  +                + bbsc(xsmoo, knots = 6, df = 3) 
  +                + bolsc(xte1, df = 3)
  +                + brandomc(xte2, df = 3), 
  +                timeformula = ~ bbs(tvals, knots = 9, df = 2, differences = 1), 
  +                control = boost_control(mstop = 10), data = dat)
  +   
  +   ## response in long format
  +   ml <- FDboost(Y_long ~ 1 + bhist(X1, svals, tvals_long, knots = 6, df = 12) 
  +                 + bsignal(X1, svals, knots = 6, df = 4)
  +                 + bbsc(xsmoo, knots = 6, df = 4) 
  +                 + bolsc(xte1, df = 4)
  +                 + brandomc(xte2, df = 4), 
  +                 timeformula = ~ bbs(tvals_long, knots = 8, df = 3, differences = 1), 
  +                 id = ~ id_long, 
  +                 offset_control = o_control(k_min = 10), 
  +                 control = boost_control(mstop = 10), data = dat)
  +   
  +   ## scalar response 
  +   ms <- FDboost(Y_scalar ~ 1 + bsignal(X1, svals, knots = 6, df = 2)
  +                 + bbs(xsmoo, knots = 6, df = 2, differences = 1) 
  +                 + bols(xte1, df = 2) 
  +                 + bols(xte2, df = 2)
  +                 + bols(xfactor, df = 2), 
  +                 timeformula = NULL, 
  +                 control = boost_control(mstop = 50), data = dat)
  +   
  +   ## scalar response and interaction effect between two functional variables
  +   ms_funint <- FDboost(Y_scalar ~ 1 + 
  +                          bsignal(X1, svals, knots = 9, df = 3) %X% bsignal(X2, s2, knots = 9, df = 3), 
  +                 timeformula = NULL, 
  +                 control = boost_control(mstop = 50), data = dat)
  +   
  +   ## GAMLSS with functional response 
  +   mlss <- FDboostLSS(Y ~ 1 + bsignal(X1, svals, knots = 6, df = 3)               
  +                      + bbsc(xsmoo, knots = 6, df = 3) 
  +                      + bolsc(xte1, df = 3), 
  +                      timeformula = ~ bbs(tvals, knots = 9, df = 3, differences = 1), 
  +                      control = boost_control(mstop = 20), data = dat, 
  +                      method = "noncyclic")
  +   
  +   ## GAMLSS with scalar response 
  +   mslss <- FDboostLSS(Y_scalar ~ 1 + bsignal(X1, svals, knots = 6, df = 3)
  +                       + bbs(xsmoo, knots = 6, df = 3, differences = 1), 
  +                       timeformula = NULL, 
  +                       control = boost_control(mstop = 50), data = dat, 
  +                       method = "noncyclic")
  +   
  +   ## response matrix with factor + continuous time variable
  +   
  +   # linear array model implemented only for matrices
  +   # => tvals and factor for dimensions have to be flattened
  +   dat2D <- with(dat, expand.grid(
  +     tvals = tvals, 
  +     xfactor = factor(2:3)
  +   ))
  +   dat2D <- as.list(dat2D[order(dat2D$xfactor), ])
  +   dat2D$Y <- cbind(
  +     dat$Y[dat$xfactor == "2", ],
  +     dat$Y[dat$xfactor == "3", ]
  +     )
  +   dat2D$xsmoo <- dat$xsmoo[dat$xfactor == "2"] 
  +     
  +   m2D <- FDboost(Y ~ bbsc(xsmoo), 
  +                  timeformula = ~ bols(xfactor) %X% bbs(tvals),
  +                  control = boost_control(mstop = 20), 
  +                  data = dat2D)
  +   
  +   
  +   # test some methods and utility functions  --------------------------------
  +   
  +   ## test plot()
  +   print("plot effects")
  +   par(mfrow = c(1,1))
  +   plot(m, ask = FALSE)
  +   plot(ml, ask = FALSE)
  +   plot(ms, ask = FALSE)
  +   plot(mlss$mu, ask = FALSE) 
  +   plot(mlss$sigma, ask = FALSE)
  +   
  +   ## test applyFolds()
  +   print("run applyFolds")
  +   set.seed(123)
  +   applyFolds(m, folds = cv(rep(1, length(unique(m$id))), B = 2), grid = 0:5)
  +   #applyFolds(ml, folds = cv(rep(1, length(unique(ml$id))), B = 2), grid = 0:5)
  +   #applyFolds(ms, folds = cv(rep(1, length(unique(ms$id))), B = 2), grid = 0:5)
  + 
  +   ## test cvrisk()
  +   print("run cvrisk")
  +   set.seed(123)
  +   cvrisk(m, folds = cvLong(id = m$id, weights = model.weights(m), B = 2), grid = 0:5)
  +   cvrisk(ml, folds = cvLong(id = ml$id, weights = model.weights(ml), B = 2), grid = 0:5)
  +   cvrisk(ms, folds = cvLong(id = ms$id, weights = model.weights(ms), B = 2), grid = 0:5)
  +   cvrisk(ms_funint, folds = cvLong(id = ms$id, weights = model.weights(ms), B = 2), grid = 0:5)
  +   
  +   cvrisk(mlss, folds = cv(model.weights(mlss[[1]]), B = 2),
  +          grid = 1:5, trace = FALSE)
  +   cvrisk(mslss, folds = cv(model.weights(mslss[[1]]), B = 2),
  +          grid = 1:5, trace = FALSE)
  +   
  +   cvrisk(m2D, folds = cv(model.weights(m2D), B = 2),
  +          grid = 1:5)
  +   
  +   ## test stabsel (use very small number of folds, B = 10, to speed up testing)
  +   print("run stabsel")
  +   stabsel(m, cutoff=0.8, PFER = 0.1*length(m$baselearner), sampling.type = "SS", eval = TRUE, B = 3)
  +   stabsel(m, cutoff=0.8, PFER = 0.1*length(m$baselearner), sampling.type = "SS", eval = TRUE, B = 3, 
  +           refitSmoothOffset = FALSE)
  +   
  +   ## FIXME: this stabsel() should also work with refitSmoothOffset = TRUE 
  +   stabsel(ml, cutoff=0.8, PFER = 0.1*length(ml$baselearner), sampling.type = "SS", eval = TRUE, B = 3, 
  +           refitSmoothOffset = FALSE)
  +   stabsel(ms, cutoff=0.8, PFER = 0.1*length(ms$baselearner), sampling.type = "SS", eval = TRUE, B = 3)
  +   ## FIXME: this is broken again 
  +   ## fixed in gamboostLSS package on github with commit 4989474 
  +   ##stabsel(mlss, cutoff=0.8, PFER = 0.1*length(mlss$mu$baselearner), sampling.type = "SS", eval = TRUE, B = 3)
  +   ##stabsel(mslss, cutoff=0.8, PFER = 0.1*length(mslss$mu$baselearner), sampling.type = "SS", eval = TRUE, B = 3)
  +   
  +   
  +   ## test predict with newdata
  +   print("predict with new data")
  +   pred <- predict(m, newdata = dat)
  +   ## for this predict() with newdata, you need a data.frame where the irregular time of y fits with X
  +   ## pred <- predict(ml, newdata = dat)
  +   pred <- predict(ms, newdata = dat)
  +   pred <- predict(ms_funint, newdata = dat)
  +   pred <- predict(mlss, newdata = dat)
  +   pred <- predict(mslss, newdata = dat)
  +   
  + }
  Loading required package: refund
  [1] "simulate data"
  [1] "model fit"
  Use a smooth offset.
  Use a smooth offset for irregular data.
  No smooth offsets over time are used, just global scalar offsets.
  No integration weights are used to compute the loss for the functional response.
  [1] "plot effects"
  [1] "run applyFolds"
  ..[1] "run cvrisk"
  The smooth offset is fixed over all folds.
  For brandomc, the transformation matrix Z is fixed over all folds.
  For bolsc, the transformation matrix Z is fixed over all folds.
  For bbsc, the transformation matrix Z is fixed over all folds.
  The smooth offset is fixed over all folds.
  For brandomc, the transformation matrix Z is fixed over all folds.
  For bolsc, the transformation matrix Z is fixed over all folds.
  For bbsc, the transformation matrix Z is fixed over all folds.
  For bbsc, the transformation matrix Z is fixed over all folds.
  [1] "run stabsel"
  Use applyFolds() to recompute the smooth offset in each fold.
  The smooth offset is fixed over all folds.
  For brandomc, the transformation matrix Z is fixed over all folds.
  For bolsc, the transformation matrix Z is fixed over all folds.
  For bbsc, the transformation matrix Z is fixed over all folds.
  The smooth offset is fixed over all folds.
  For brandomc, the transformation matrix Z is fixed over all folds.
  For bolsc, the transformation matrix Z is fixed over all folds.
  For bbsc, the transformation matrix Z is fixed over all folds.
  [1] "predict with new data"
  There were 46 warnings (use warnings() to see them)
  > 
  > 
  > 
  > # sof: fuel data ----------------------------------------------------------
  > 
  > print("run checks with fuel data")
  [1] "run checks with fuel data"
  > 
  > ## prediction with functional variable as numeric matrix, see Issue #17
  > data(fuelSubset)
  > fuel <- fuelSubset[c('heatan', 'h2o', 'UVVIS', 'uvvis.lambda')]
  > str(fuel$UVVIS) # numeric matrix
   num [1:129, 1:134] 0.145 -1.584 -0.814 -1.311 -1.373 ...
  > 
  > sof <- FDboost(heatan ~ bsignal(UVVIS, uvvis.lambda, knots = 20, df = 4) + 
  +                  bbs(h2o, df = 4),
  +                timeformula = ~bols(1), data = fuel)
  UVVIS is not centered per column, inducing a non-centered effect.
  Warning message:
  In .qr.rank.def.warn(r) :
    matrix is structurally rank deficient; using augmented matrix with additional 2 row(s) of zeros
  > 
  > # Predict with newdata
  > pred <- predict(sof, newdata = fuel)
  > 
  > # with interaction term
  > sof_int <- FDboost(heatan ~ bsignal(UVVIS, uvvis.lambda, knots = 9, df = 9) + 
  +                  bsignal(NIR, nir.lambda, knots = 9, df = 9) +
  +                  bsignal(UVVIS, uvvis.lambda, knots = 9, df = 3) %X% bsignal(NIR, nir.lambda, knots = 9, df = 3),
  +                timeformula = ~bols(1), data = fuelSubset)
  UVVIS is not centered per column, inducing a non-centered effect.
  NIR is not centered per column, inducing a non-centered effect.
  UVVIS is not centered per column, inducing a non-centered effect.
  NIR is not centered per column, inducing a non-centered effect.
  Error in chol.default(A) : the leading minor of order 154 is not positive
  Calls: FDboost ... backsolve -> as.matrix -> chol -> chol -> chol.default
  Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking re-building of vignette outputs ... [38s/78s] OK
* checking PDF version of manual ... [6s/13s] OK
* checking HTML version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE

Status: 1 ERROR
See
  ‘/data/localhost/ripley/R/packages/tests-MKL/FDboost.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 6:19.03, 162.95 + 13.97