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

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: bsignal
> ### Title: Base-learners for Functional Covariates
> ### Aliases: bsignal bconcurrent bhist bfpc bconcurrent bhist bfpc
> ### Keywords: models
> 
> ### ** Examples
> 
> ######## Example for scalar-on-function-regression with bsignal()  
> 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 and two functional linear effects 
> ## 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) 
> summary(mod2) 
$object

	 Model-based Boosting with Scalar Response 

Call:
FDboost(formula = 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)

10
	 Squared Error (Regression) 

Loss function: (y - f)^2 
 

Number of boosting iterations: mstop = 100 
Step size:  0.1 
Offset:  24.766 
Number of baselearners:  2 


$selprob
    bsignal(x = NIR, s = nir.lambda, knots = 40, df = 4, check.ident = FALSE) 
                                                                         0.75 
bsignal(x = UVVIS, s = uvvis.lambda, knots = 40, df = 4, check.ident = FALSE) 
                                                                         0.25 

attr(,"class")
[1] "summary.FDboost"
> ## plot(mod2)
> 
> 
> ###############################################
> ### data simulation like in manual of pffr::ff
> 
> if(require(refund)){
+ 
+ #########
+ # model with linear functional effect, use bsignal()
+ # Y(t) = f(t) + \int X1(s)\beta(s,t)ds + eps
+ set.seed(2121)
+ data1 <- pffrSim(scenario = "ff", n = 40)
+ data1$X1 <- scale(data1$X1, scale = FALSE)
+ dat_list <- as.list(data1)
+ dat_list$t <- attr(data1, "yindex")
+ dat_list$s <- attr(data1, "xindex")
+ 
+ ## model fit by FDboost 
+ m1 <- FDboost(Y ~ 1 + bsignal(x = X1, s = s, knots = 5), 
+               timeformula = ~ bbs(t, knots = 5), data = dat_list, 
+               control = boost_control(mstop = 21))
+ 
+ ## search optimal mSTOP
+ ## Not run: 
+ ##D   set.seed(123)
+ ##D   cv <- validateFDboost(m1, grid = 1:100) # 21 iterations
+ ## End(Not run)
+ 
+ ## model fit by pffr
+ t <- attr(data1, "yindex")
+ s <- attr(data1, "xindex")
+ m1_pffr <- pffr(Y ~ ff(X1, xind = s), yind = t, data = data1)
+ 
+ ## Not run: 
+ ##D   par(mfrow = c(2, 2))
+ ##D   plot(m1, which = 1); plot(m1, which = 2) 
+ ##D   plot(m1_pffr, select = 1, shift = m1_pffr$coefficients["(Intercept)"]) 
+ ##D   plot(m1_pffr, select = 2)
+ ## End(Not run)
+ 
+ 
+ ############################################
+ # model with functional historical effect, use bhist() 
+ # Y(t) = f(t)  + \int_0^t X1(s)\beta(s,t)ds + eps
+ set.seed(2121)
+ mylimits <- function(s, t){
+   (s < t) | (s == t)
+ }
+ data2 <- pffrSim(scenario = "ff", n = 40, limits = mylimits)
+ data2$X1 <- scale(data2$X1, scale = FALSE)
+ dat2_list <- as.list(data2)
+ dat2_list$t <- attr(data2, "yindex")
+ dat2_list$s <- attr(data2, "xindex")
+ 
+ ## model fit by FDboost 
+ m2 <- FDboost(Y ~ 1 + bhist(x = X1, s = s, time = t, knots = 5), 
+               timeformula = ~ bbs(t, knots = 5), data = dat2_list, 
+               control = boost_control(mstop = 40))
+               
+ ## search optimal mSTOP
+ ## Not run: 
+ ##D   set.seed(123)
+ ##D   cv2 <- validateFDboost(m2, grid = 1:100) # 40 iterations
+ ## End(Not run)               
+ 
+ ## model fit by pffr
+ t <- attr(data2, "yindex")
+ s <- attr(data2, "xindex")
+ m2_pffr <- pffr(Y ~ ff(X1, xind = s, limits = "s<=t"), yind = t, data = data2)
+ 
+ ## Not run: 
+ ##D par(mfrow = c(2, 2))
+ ##D plot(m2, which = 1); plot(m2, which = 2)
+ ##D ## plot of smooth intercept does not contain m1_pffr$coefficients["(Intercept)"]
+ ##D plot(m2_pffr, select = 1, shift = m2_pffr$coefficients["(Intercept)"]) 
+ ##D plot(m2_pffr, select = 2) 
+ ##D 
+ ## End(Not run)
+ 
+ 
+ }
Loading required package: refund
Use a smooth offset.
Warning in bols(ONEx, intercept = FALSE, df = 1) %A0% bbs(t, knots = 5,  :
  Specified df in bbs(t, knots = 5, df = 16) are higher than the number of columns, which is 9.
Warning in df2lambda(X = diag(rankMatrix(X$X1, method = "qr", warn.t = FALSE) *  :
  'df' too large:
  Degrees of freedom cannot be larger than the rank of the design matrix.
  Unpenalized base-learner with df = 9 used. Re-consider model specification.
Error: C stack usage  9961632 is too close to the limit
Execution halted