Running examples in ‘FDboost-Ex.R’ failed
The error most likely occurred in:

> ### Name: FDboost
> ### Title: Model-based Gradient Boosting for Functional Response
> ### Aliases: FDboost
> ### Keywords: models nonlinear regression smooth
> 
> ### ** Examples
> 
> ######## Example for function-on-scalar-regression 
> data("viscosity", package = "FDboost") 
> ## set time-interval that should be modeled
> interval <- "101"
> 
> ## model time until "interval" and take log() of viscosity
> end <- which(viscosity$timeAll == as.numeric(interval))
> viscosity$vis <- log(viscosity$visAll[,1:end])
> viscosity$time <- viscosity$timeAll[1:end]
> # with(viscosity, funplot(time, vis, pch = 16, cex = 0.2))
> 
> ## fit median regression model with 100 boosting iterations,
> ## step-length 0.4 and smooth time-specific offset
> ## the factors are coded such that the effects are zero for each timepoint t
> ## no integration weights are used!
> mod1 <- FDboost(vis ~ 1 + bolsc(T_C, df = 2) + bolsc(T_A, df = 2),
+                timeformula = ~ bbs(time, df = 4),
+                numInt = "equal", family = QuantReg(),
+                offset = NULL, offset_control = o_control(k_min = 9),
+                data = viscosity, control=boost_control(mstop = 100, nu = 0.4))
Use a smooth offset.
> 
> ## Not run: 
> ##D   #### find optimal mstop over 5-fold bootstrap, small number of folds for example
> ##D   #### do the resampling on the level of curves
> ##D   
> ##D   ## possibility 1: smooth offset and transformation matrices are refitted 
> ##D   set.seed(123)
> ##D   appl1 <- applyFolds(mod1, folds = cv(rep(1, length(unique(mod1$id))), B = 5), 
> ##D                       grid = 1:500)
> ##D   ## plot(appl1)
> ##D   mstop(appl1)
> ##D   mod1[mstop(appl1)]
> ##D   
> ##D   ## possibility 2: smooth offset is refitted, 
> ##D   ## computes oob-risk and the estimated coefficients on the folds
> ##D   set.seed(123)
> ##D   val1 <- validateFDboost(mod1, folds = cv(rep(1, length(unique(mod1$id))), B = 5), 
> ##D                         grid = 1:500)
> ##D   ## plot(val1)
> ##D   mstop(val1)
> ##D   mod1[mstop(val1)]
> ##D 
> ##D   ## possibility 3: very efficient 
> ##D   ## using the function cvrisk; be careful to do the resampling on the level of curves
> ##D   folds1 <- cvLong(id = mod1$id, weights = model.weights(mod1), B = 5)
> ##D   cvm1 <- cvrisk(mod1, folds = folds1, grid = 1:500)
> ##D   ## plot(cvm1)
> ##D   mstop(cvm1)
> ##D   
> ##D ## look at the model
> ##D summary(mod1)
> ##D coef(mod1)
> ##D plot(mod1)
> ##D plotPredicted(mod1, lwdPred = 2)
> ## End(Not run)
> 
> ######## Example for scalar-on-function-regression 
> data("fuelSubset", package = "FDboost")
> 
> ## center the functional covariates per observed wavelength
> fuelSubset$UVVIS <- scale(fuelSubset$UVVIS, scale = FALSE)
> fuelSubset$NIR <- scale(fuelSubset$NIR, scale = FALSE)
> 
> ## to make mboost:::df2lambda() happy (all design matrix entries < 10)
> ## reduce range of argvals to [0,1] to get smaller integration weights
> fuelSubset$uvvis.lambda <- with(fuelSubset, (uvvis.lambda - min(uvvis.lambda)) / 
+                                           (max(uvvis.lambda) - min(uvvis.lambda) ))
> fuelSubset$nir.lambda <- with(fuelSubset, (nir.lambda - min(nir.lambda)) / 
+                                           (max(nir.lambda) - min(nir.lambda) )) 
> 
> ## model fit with scalar response 
> ## include no intercept as all base-learners are centered around 0
> mod2 <- FDboost(heatan ~ bsignal(UVVIS, uvvis.lambda, knots = 40, df = 4, check.ident = FALSE) 
+                + bsignal(NIR, nir.lambda, knots = 40, df = 4, check.ident = FALSE), 
+                timeformula = NULL, data = fuelSubset, control = boost_control(mstop = 200)) 
Error in solve.default(XtX, crossprod(X, y), LINPACK = FALSE) : 
  the LINPACK argument has been defunct since R 3.1.0
Calls: FDboost ... <Anonymous> -> mysolve -> solve -> solve -> solve.default
Execution halted