Description
Regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, are fitted by a component-wise gradient boosting algorithm. For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.
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tests
Running ‘factorize_test_irregular.R’ [1s/1s]
Running ‘factorize_test_regular.R’ [1s/1s]
Running ‘general_tests.R’ [3s/2s]
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 1.1-3.
> 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)){
+
+ ## simulate a small data set
+ print("simulate data")
+ set.seed(230)
+ pffr_data <- 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"
*** caught segfault ***
address 0x110, cause 'invalid permissions'
*** caught segfault ***
address 0x110, cause 'invalid permissions'
Traceback:
1: eigen(x, only.values = TRUE, symmetric = TRUE)
2: make_psd(as.matrix(A))
3: df2lambda(X, df = args$df, lambda = args$lambda, dmat = K, weights = w, XtX = XtX)
4: object$dpp(weights)
5: dpp.blg(X[[i]], ...)
6: FUN(X[[i]], ...)
7: lapply(blg, dpp, weights = weights)
8: mboost_fit(bl, response = response, weights = weights, offset = offset, family = family, control = control, oobweights = oobweights, ...)
9: mboost(fm, data = data, weights = w, offset = offset, ...)
10: FDboost(formula = Y ~ 1 + bhist(X1, svals, tvals, knots = 10, df = 6) + bsignal(X1, svals, knots = 6, df = 3) + bbsc(xsmoo, knots = 6, df = 3) + bolsc(xte1, df = 3) + brandomc(xte2, df = 3), timeformula = ~bbs(tvals, knots = 9, df = 2, differences = 1), control = list(mstop = 10, nu = 0.1, risk = "inbag", stopintern = FALSE, center = TRUE, trace = FALSE), data = list(X1 = c(0.199640973727573, -0.384847598069494, 0.603457570969962, -1.96808631110356, 0.557581610120466, 0.557581610120466, 0.557581610120466, -0.154249399607682, -0.154249399607682, 0.332466129802816, 0.123047695162448, 0.123047695162448, -0.217719731066488, 0.849859353431247, 0.37815504640687, 0.391825789806197, -0.140268672389776, 0.136658906977929, 1.12946412406419, 0.293001205700839, 0.293001205700839, 0.293001205700839, -0.19364526360259, -0.19364526360259, -0.19364526360259, 0.217694367220776, -0.364005355579847, 0.626844910204011, -2.02253814517902, 0.413423575359256, 0.413423575359256, 0.413423575359256, 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9.4083738341913,
Traceback:
1: eigen(x, only.values = TRUE, symmetric = TRUE)
2: make_psd(as.matrix(A))
3: df2lambda(X, df = args$df, lambda = args$lambda, dmat = K, weights = w, XtX = XtX)
4: 6.60813109794438, 5.51657370220289, 9.26937156407052, 7.57714670085009, 7.57714670085009, 7.57714670085009, 7.83181035871939, 7.83181035871939, 7.83181035871939, 9.56887272345038, 10.2919101071996, 7.83417807358798, 6.50406097378059, 5.59004322730142, 5.59004322730142, 5.59004322730142, 3.75673238973066, 3.75673238973066, 6.95810829248155, 6.58396020389178, 6.58396020389178, 3.85198378716585, 7.49844877523399, 6.26542919406394, 6.33477061300236, 7.09170078734533, 7.16131206171303, 13.4780800784935, object$dpp(weights)
5: dpp.blg(X[[i]], ...)
6: FUN(X[[i]], ...)
7: 8.03621417285636, 8.03621417285636, 8.03621417285636, 6.50101049720017, 6.50101049720017, 6.50101049720017, 10.7280571005898, 11.1617728575079, 8.54482845379778, 5.97903974089772, 4.09387511704214, 4.09387511704214, 4.09387511704214, 7.39040833034233, 7.39040833034233, 7.32679691977306, 7.22478815768494, 7.22478815768494, 3.3814444692434, 8.80620184590264, 8.66162017289597, 8.1028148572908, 5.66949499134051, 5.63555084818458, 7.96322259570954, 6.39540917919449, 6.39540917919449, 6.39540917919449, 7.03530009555109, 7.03530009555109, 7.03530009555109, 8.37034108131976, 11.54879494807, 7.48024784386413, 7.49439869475747, 5.05697313056137, 5.05697313056137, 5.05697313056137, 4.73097851833431, 4.73097851833431, 6.55734603817935, 7.18980211173506, 7.18980211173506, 3.14926009263069, 8.89847970608806, 6.25587236187813, 7.92673265529014, 8.31384625637494, 8.0793989306894, 10.8969507162214, 8.15419116971303, 8.15419116971303, 8.15419116971303, 4.8828998942077, 4.8828998942077, 4.8828998942077, 9.15942791953567, 10.0828102832128, 7.3863866946528, 4.4894533496343, lapply(blg, dpp, weights = weights)
8: 0.477967551149892, 0.477967551149892, 0.477967551149892, 1.76330460175524, 1.76330460175524, 5.75925479744874, 6.762472326976, 6.762472326976, 2.64590720664884, 8.05707967792883, 5.53636058958423, mboost_fit(bl, response = response, weights = weights, offset = offset, family = family, control = control, oobweights = oobweights, ...)
9: mboost(fm, data = data, weights = w, offset = offset, ...)
10: FDboost(formula = 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 = list(mstop = 10, nu = 0.1, risk = "inbag", stopintern = FALSE, center = TRUE, trace = FALSE), data = list(X1 = c(-0.384847598069494, -0.384847598069494, 0.603457570969962, -1.96808631110356, 7.7347398782817, 4.59084138420101, 6.48322177433504, 8.82138677885528, 6.29591264710364, 6.29591264710364, 6.29591264710364, 5.67738343931805, 5.67738343931805, 5.67738343931805, 10.467532252557, 9.40342027067766, -1.96808631110356, -0.189932698342793, -0.189932698342793, -0.189932698342793, 0.557581610120466, -0.154249399607682, -0.154249399607682, 0.332466129802816, 0.123047695162448, 7.43804652343618, 3.46170545338917, 3.36892580779415, 3.36892580779415, 3.36892580779415, 3.16549015989961, 3.16549015989961, 4.89875721828134, 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c(-0.549195033818743, -1.35404768117909, -2.01530438871807, 0.592790929152534, 1.66967841532004, 1.66967841532004, 1.66967841532004, -1.56138731415552, -0.31890383476143, -0.0679961093581712, -0.0679961093581712, -0.809782278300482, -0.809782278300482, -0.809782278300482, 1.38135038680079, -0.379481750569987, -0.967266027908846, -0.967266027908846, -0.829341365126664, -0.0770599273382019, -0.0770599273382019, -0.355420964782674, -1.01347414154902, 1.09468338499145, -2.14041259297538, -0.397138397437131, -0.018231201876963, -0.389472649293914, -0.389472649293914, -0.0814263003526759, -0.233576410974102, -0.233576410974102, 0.683777126279056, 0.683777126279056, 0.683777126279056, 0.119229904665798, -0.487779732599774, -0.487779732599774, 0.704598000987812, 0.49869951759393, -1.18741469933918, -1.18741469933918, -1.39940348292775, -0.371568895835106, -0.0949137529881868, -0.166277230051912, -1.51762356607987, -0.435807645806459, -0.435807645806459, -0.435807645806459, -1.04935338453092, -1.04935338453092, -1.04935338453092), xte2 = c(0.255728440380257, -0.0949137529881868, -0.828773430984499, -0.828773430984499, -0.828773430984499, 1.40123417354242, -0.365343030472985, 0.0712848574864238, -0.672285199112463, -0.672285199112463, 0.165208465776849, 0.46315273045661, 1.06993012661437, 0.156569007297343, 0.156569007297343, 0.156569007297343, -0.551799774632661, -0.551799774632661, -0.281543982899657, 1.48072466486686, -0.052883790812609, -0.150786891806164, -0.150786891806164, 0.693461658667105, 0.693461658667105, 0.693461658667105, 0.345604165745155, -0.577868880979357, -0.577868880979357, 1.48072466486686, 0.134508992127929, -0.936917694826534, 1.18258861189291, 1.20039445269639, -0.615308105970782, 0.956450973560131, -0.139363949620574, 0.387620826324863, 0.387620826324863, 0.683593917124232, 0.513851545591929, -1.32336525946559, -1.32336525946559, -1.20479979296663, -0.408558338183947, -0.117581557455552, -0.117581557455552, -0.85105973197631, -0.85105973197631, 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0.388888888888889, 0.444444444444444, 0.619289773102981, 0.619289773102981, 0.619289773102981, 0.72275114251554, -0.591395962605519, -0.591395962605519, 0.577171086888269, 0.439312005199013, -1.27685575343531, -1.27685575343531, -0.831452632377742, -0.411738051607178, -0.196950455033918, -0.196950455033918, -0.913733358074282, -0.913733358074282, -0.913733358074282, 1.08768435590225, 0.5, 0.555555555555556, 0.611111111111111, 0.666666666666667, 0.722222222222222, 0.777777777777778, 0.833333333333333, 0.888888888888889, 0.944444444444444, 1), object_id = NULL))
11: -0.34501546529828, 0.135164801465576, -0.595913844115834, -0.595913844115834, -0.207413772192455, -0.160850192880216, -0.160850192880216, 0.54392300442427, 0.54392300442427, 0.54392300442427, 0.834454958490338, -0.525928980155063, -0.525928980155063, 0.507148615187443, 0.339253167685072, -1.0879320066386, -1.0879320066386, -0.673143482921043, -0.378277190719291, -0.245338254648561, -0.245338254648561, -0.946107688517591, -0.946107688517591, -0.946107688517591, 0.913995541137479, -0.347700323782568, 0.0549887590352762, -0.325807238312839, -0.325807238312839, eval(call)
12: eval(call)
13: -0.305797687202275, -0.228652346423871, -0.228652346423871, 0.453697441053248, 0.453697441053248, 0.453697441053248, 0.875725158362203, -0.465786439776897, -0.465786439776897, 0.439918850959048, 0.20473049394524, -0.836592542312432, -0.836592542312432, -0.52268247156616, -0.329973020426625, -0.272500907636831, -0.272500907636831, -0.965617861240613, withCallingHandlers(expr, message = function(c) if (inherits(c, classes)) tryInvokeRestart("muffleMessage"))
-0.965617861240613, -0.965617861240613, 0.730628868658872, -0.349691608564007, -0.0118509545286885, -0.0347650954298771, -0.0347650954298771, -0.388029277812632, -0.31060416253014, 14: suppressMessages(eval(call))
-0.31060416253014, 0.356957936606738, 0.356957936606738, 0.356957936606738, 0.832649282343939, -0.454287868683601, -0.454287868683601, 0.386242168830194, 0.0422875504475274, 15: withCallingHandlers(suppressMessages(eval(call)), warning = h)
-0.562507058036594, -0.562507058036594, -0.370053489693764, -0.27379614901845, -0.256670799338597, -0.256670799338597, -0.961524662129004, -0.961524662129004, -0.961524662129004, 16: fitfct(weights = weights, oobweights = oobweights)
0.552130851537197, -0.346781835247241, -0.015029518886746, 0.214013138264263, 0.214013138264263, -0.429572896428909, -0.395615411904174, -0.395615411904174, 0.262049344701559, 17: dummyfct(weights = folds[, i], oobweights = OOBweights[, i])
0.262049344701559, 0.262049344701559, 0.691314870648353, -0.534752794087752, -0.534752794087752, 0.356878943427988, -0.14153209634005, -0.305345251390882, -0.305345251390882, 18: doTryCatch(return(expr), name, parentenv, handler)
-0.205240428684528, -0.21671718478404, -0.176080315093724, -0.176080315093724, -0.923088877068421, -0.923088877068421, -0.923088877068421, 0.393048002843222, -0.334763519436913, 0.0957778863006762), Y = c(9.5949504015943, 9.5949504015943, 8.61344455788643, 6.5995732217885, 6.5995732217885, 8.37153474639064, 8.37153474639064, 8.37153474639064, 6.59810945113043, 5.5021172342669, 5.5021172342669, 7.16715430668448, 7.05265974090098, 10.6842351400083, 10.6842351400083, 6.12895958392685, 2.85285424265382, 4.92881798003667, 4.92881798003667, 3.35585090312442, 3.35585090312442, 3.35585090312442, 19: tryCatchOne(expr, names, parentenv, handlers[[1L]])
20: tryCatchList(expr, classes, parentenv, handlers)
3.52099677581483, 2.85325970340929, 6.78370561080518, 13.0754009681391, 13.0754009681391, 12.2636673145085, 15.1732198241385, 15.1732198241385, 15.8501088543432, 15.8501088543432, 15.8501088543432, 12.0568635542716, 10.8306442154272, 10.8306442154272, 10.9457023799698, 9.94762338875895, 14.1531958548585, 14.1531958548585, 10.6798393615683, 6.47471387396918, 10.9575368985821, 10.9575368985821, 5.70781580813834, 5.70781580813834, 21: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L 5.70781580813834, 4.14552223567563, 8.67109220241596, 12.5088868117415, 15.5336691247304, 15.5336691247304, 13.4087448808166, 11.7336220387193, 11.7336220387193, 16.8927864190794, 16.8927864190794, 16.8927864190794, sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) 12.2510492122091, 12.0572260530758, 12.0572260530758, 14.2022238914053, 12.690608487368, 15.3258852663848, 15.3258852663848, 13.4008755243725, 10.4594497344414, 12.914543849625, 12.914543849625, 9.88498193334743, w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " 9.88498193334743, 9.88498193334743, 5.97272915767439, 9.87863422209007, 14.3868056074568, 16.2138571453341, 16.2138571453341, 13.0189946660659, 12.9130850792063, 12.9130850792063, 17.2176030482912, 17.2176030482912, msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { 17.2176030482912, 14.0827968209166, 12.2167115488798, 12.2167115488798, 17.142207804944, 10.5370324060672, 15.8440164489401, 15.8440164489401, 11.7857201634212, 11.5473783790476, 12.4639853312215, 12.4639853312215, cat(msg, file = outFile) .Internal(printDeferredWarnings()) } 7.71397678149811, 7.71397678149811, 7.71397678149811, 6.27119084862376, 11.0070868369002, 13.1173831266096, 15.6857112228227, 15.6857112228227, 13.3174763424916, 13.8782617689466, 13.8782617689466, 15.9411864466316, invisible(structure(msg, class = "try-error", condition = e))})
15.9411864466316, 15.9411864466316, 12.1837450064575, 11.2464004285451, 11.2464004285451, 15.5543065979242, 11.9472008960215, 15.3940778309177, 15.3940778309177, 11.5608504877255, 7.56271793781209, 13.8202120974261, 22: try(dummyfct(weights = folds[, i], oobweights = OOBweights[, i]), silent = TRUE)
23: FUN(X[[i]], ...) 13.8202120974261, 8.36756104821634, 8.36756104821634, 8.36756104821634, 7.14891130025113, 10.2852210097186, 15.4954503577333, 13.3007822061282, 13.3007822061282, 13.3685797054909, 12.4648248247858, 12.4648248247858, 14.4134762226516, 14.4134762226516, 14.4134762226516, 12.3362871418548, 9.9775079522741, 9.9775079522741, 16.1789192891736, 11.0379769493391, 12.2359721657062, 12.2359721657062, 10.1033447415563, 6.70086606724926,
24: eval(expr, env)
25: doTryCatch(return(expr), name, parentenv, handler) 12.0260950770304, 12.0260950770304, 7.13303782295137, 7.13303782295137, 7.13303782295137, 4.53361355993138, 9.37739401116593, 12.2264570882414, 13.2263782253478, 13.2263782253478, 11.304604723962, 10.1436692509721, 10.1436692509721, 14.8433876374833, 14.8433876374833, 14.8433876374833, 9.68854589193723, 10.7193359853077, 10.7193359853077, 13.79670105834,
26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
27: tryCatchList(expr, classes, parentenv, handlers)
28: tryCatch(expr, error = function(e) { 9.34549649516708, 11.7593506075689, 11.7593506075689, 10.0146285225385, 5.98354841565647, 10.0641104266191, 10.0641104266191, 7.08347955116493, 7.08347955116493, 7.08347955116493, 5.06497292747622, 7.93158133887571, call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) 10.4928737398457, 12.6231014029796, 12.6231014029796, 12.2660703913135, 9.53407349697419, 9.53407349697419, 12.950542449956, 12.950542449956, 12.950542449956, 13.639950930242, 7.5453432986752, 7.5453432986752, call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") 11.6487263985094, 11.2167525842424, 11.6883572193711, 11.6883572193711, 10.1574211659286, 4.22327149546088, 8.13238550373012, 8.13238550373012, 7.19294184376232, 7.19294184376232, 7.19294184376232, 2.89256458218739, LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") 6.98506627476757, 10.8452243420862, 10.7626324325419, 10.7626324325419, 8.71930411190778, 9.92348029630378, 9.92348029630378, 10.2786393181843, 10.2786393181843, 10.2786393181843, 7.60808817060135, 5.75068803701575, if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) 5.75068803701575, 9.6850469002331, 9.96605267231027, 10.2347254079565, 10.2347254079565, 8.53261180726428, 1.61627258156577, 6.28226961952663, 6.28226961952663, 6.98601401709901, 6.98601401709901, 6.98601401709901, if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))}) 3.79468547731753, 8.7253912590341, 11.2632514281757, 12.0733653050541, 12.0733653050541, 8.65539939763193, 8.68813578910662, 8.68813578910662, 11.3318034168308, 11.3318034168308, 11.3318034168308, 7.5654466222814,
29: try(eval(expr, env), silent = TRUE) 6.05680349085804, 6.05680349085804, 8.84200639263773, 7.86061923272567, 9.50197948632077, 9.50197948632077, 8.91920313633734, 4.47838893223576, 6.96724701463868, 6.96724701463868, 6.60813109794438, 6.60813109794438,
30: serialize(what, NULL, xdr = FALSE) 6.60813109794438, 3.32097605670191, 5.51657370220289, 7.57714670085009, 10.2919101071996, 10.2919101071996, 7.83417807358798, 6.50406097378059, 6.50406097378059, 10.7551234012543, 10.7551234012543, 10.7551234012543,
31: sendMaster(try(eval(expr, env), silent = TRUE), FALSE)
32: mcparallel(FUN(X[[i]], ...), name = names(X)[i], mc.set.seed = mc.set.seed, 5.59004322730142, 3.75673238973066, 3.75673238973066, 6.95810829248155, 6.58396020389178, 8.01100911337153, 8.01100911337153, 7.49844877523399, 4.60469981209194, 7.567943638852, 7.567943638852, 7.09170078734533, 7.09170078734533, 7.09170078734533, 5.41128254036811, 7.16131206171303, 8.03621417285636, 11.1617728575079, 11.1617728575079, 8.54482845379778, 5.97903974089772, 5.97903974089772, 9.30929565638687, 9.30929565638687, silent = mc.silent)
33: FUN(X[[i]], ...)
34: 9.30929565638687, 4.09387511704214, 7.39040833034233, 7.39040833034233, 7.32679691977306, 7.22478815768494, 8.58792106949346, 8.58792106949346, 8.80620184590264, 0.999970737259481, 6.78052586063727, 6.78052586063727, 5.66949499134051, 5.66949499134051, 5.66949499134051, 4.51544438076207, 5.63555084818458, 6.39540917919449, 11.54879494807, 11.54879494807, 7.48024784386413, 7.49439869475747, 7.49439869475747, 9.62103542354552, 9.62103542354552, 9.62103542354552, 5.05697313056137, 4.73097851833431, 4.73097851833431, 6.55734603817935, 7.18980211173506, 9.986015148312, 9.986015148312, 8.89847970608806, 1.83250102860073, 6.42765345915411, 6.42765345915411, 8.31384625637494, 8.31384625637494, 8.31384625637494, 5.97319128123771, 8.0793989306894, 8.15419116971303, 10.0828102832128, lapply(seq_along(X), function(i) mcparallel(FUN(X[[i]], ...), name = names(X)[i], mc.set.seed = mc.set.seed, silent = mc.silent))
35: papply(seq_len(ncol(folds)), function(i) try(dummyfct(weights = folds[, i], oobweights = OOBweights[, i]), silent = TRUE), mc.preschedule = mc.preschedule, 10.0828102832128, 7.3863866946528, 4.4894533496343, 4.4894533496343, 6.46804916892808, 6.46804916892808, 6.46804916892808, 0.477967551149892, 1.76330460175524, 1.76330460175524, 5.75925479744874, 6.762472326976, ...)
36: 8.90439416413036, 8.90439416413036, 8.05707967792883, 0.566090143611434, 5.40231603690707, 5.40231603690707, 4.59084138420101, 4.59084138420101, 4.59084138420101, 3.71100455441436, 6.48322177433504, 6.29591264710364, applyFolds(m, folds = cv(rep(1, length(unique(m$id))), B = 2), grid = 0:5)
9.40342027067766, 9.40342027067766, 7.43804652343618, 3.46170545338917, 3.46170545338917, 5.90233380203771, 5.90233380203771, 5.90233380203771, 3.36892580779415, 3.16549015989961, 3.16549015989961, 4.89875721828134, An irrecoverable exception occurred. R is aborting now ...
8.79738970641577, 6.66011122497792, 6.66011122497792, 8.3682113031237, 1.83845724944196, 5.36394046490261, 5.36394046490261, 6.06503797654488, 6.06503797654488, 6.06503797654488, 3.89593098363015, 5.74118116617385, 8.3775768960541, 9.72376812684263, 9.72376812684263, 6.3623837161092, 7.13830559080834, 7.13830559080834, 8.01653692837935, 8.01653692837935, 8.01653692837935, 2.47464082047802, 2.4816905595452, 2.4816905595452, 4.94294014675545, 7.0948740282431, 8.05061777798787, 8.05061777798787, 7.84665054007237, 1.954497372299, 5.6941676440096, 5.6941676440096, 5.06848066624312, 5.06848066624312, 5.06848066624312, 5.77031742492159, 5.80549299953768, 7.82477868449231, 9.60023546544402, 9.60023546544402, 8.03187944630361, 8.34570835713978, 8.34570835713978, 7.65553056804936, 7.65553056804936, 7.65553056804936, 1.90713340695206, 2.03566597767679, 2.03566597767679, 6.85877752189216, 5.75638061145435, 7.30773204012457, 7.30773204012457, 7.66103811843421, 0.791339200184756, 4.32089363410653, 4.32089363410653, 5.67521505558472, 5.67521505558472, 5.67521505558472, 4.61366142043961, 8.17991694241109, 6.62089762361192, 10.0042063607192, 10.0042063607192, 7.28875884533376, 6.82516650969599, 6.82516650969599, 9.14163049119645, 9.14163049119645, 9.14163049119645, 2.78739028667702, 0.992559502611712, 0.992559502611712, 7.02528428024291, 5.25441771697918, 7.212292386562, 7.212292386562, 7.19522656917909, 1.4077893801522, 5.23248875476627, 5.23248875476627, 6.3246497157767, 6.3246497157767, 6.3246497157767, 5.42667518799236, 5.89774305719458, 6.83990747539866, 10.5624076792504, 10.5624076792504, 9.02287370280385, 9.00000710027316, 9.00000710027316, 8.29312057943188, 8.29312057943188, 8.29312057943188, 1.18819818278275, 1.79768434077991, 1.79768434077991, 10.4339939283081, 7.41084428356965, 6.20371033061321, 6.20371033061321, 9.90794792411486, 2.10484018080433, 6.82629213090569, 6.82629213090569, 3.7180501355292, 3.7180501355292, 3.7180501355292, 4.87396125903024, 3.85357053927014, 8.05917985471927), integration_weights = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), xsmoo = c(0.557721586591504, 0.557721586591504, -0.0417697968384276, -1.07978489717087, -1.07978489717087, -0.0704813379155723, -0.0704813379155723, -0.0704813379155723, 0.734238068587715, -0.939335378954645, -0.939335378954645, -0.668668653057256, -0.95554000099689, 1.29865785117607, 1.29865785117607, -0.331576476827406, -0.290247668111241, -0.268630239085725, -0.268630239085725, -0.679850675855534, -0.679850675855534, -0.679850675855534, -0.556677222282395, -0.518569840883196, 0.389513221181857), xte1 = c(-1.35404768117909, -1.35404768117909, -2.01530438871807, 0.592790929152534, 0.592790929152534, -0.0640761212458992, -0.0640761212458992, -0.0640761212458992, 1.66967841532004, -0.967266027908846, -0.967266027908846, -0.829341365126664, -0.0770599273382019, -0.132711538630847, -0.132711538630847, -1.01347414154902, -0.980085379933654, -0.100268039262386, -0.100268039262386, -0.397138397437131, -0.397138397437131, -0.397138397437131, 0.0998999307605635, -0.166277230051912, -0.435807645806459 ), xte2 = c(0.165208465776849, 0.165208465776849, 0.46315273045661, 1.06993012661437, 1.06993012661437, -0.156651842902546, -0.156651842902546, -0.156651842902546, 0.156569007297343, -0.551799774632661, -0.551799774632661, -0.281543982899657, 1.48072466486686, 1.05404603663137, 1.05404603663137, -0.936917694826534, -0.910985596559708, -0.949702949591695, -0.949702949591695, -0.615308105970782, -0.615308105970782, -0.615308105970782, 1.60927596055172, 0.956450973560131, 0.387620826324863), svals = c(0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1), tvals = c(0, 0.0555555555555556, 0.111111111111111, 0.166666666666667, 0.222222222222222, 0.277777777777778, 0.333333333333333, 0.388888888888889, 0.444444444444444, 0.5, 0.555555555555556, 0.611111111111111, 0.666666666666667, 0.722222222222222, 0.777777777777778, 0.833333333333333, 0.888888888888889, 0.944444444444444, 1), object_id = NULL))
11: eval(call)
12: eval(call)
13: withCallingHandlers(expr, message = function(c) if (inherits(c, classes)) tryInvokeRestart("muffleMessage"))
14: suppressMessages(eval(call))
15: withCallingHandlers(suppressMessages(eval(call)), warning = h)
16: fitfct(weights = weights, oobweights = oobweights)
17: dummyfct(weights = folds[, i], oobweights = OOBweights[, i])
18: doTryCatch(return(expr), name, parentenv, handler)
19: tryCatchOne(expr, names, parentenv, handlers[[1L]])
20: tryCatchList(expr, classes, parentenv, handlers)
21: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
22: try(dummyfct(weights = folds[, i], oobweights = OOBweights[, i]), silent = TRUE)
23: FUN(X[[i]], ...)
24: eval(expr, env)
25: doTryCatch(return(expr), name, parentenv, handler)
26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
27: tryCatchList(expr, classes, parentenv, handlers)
28: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
29: try(eval(expr, env), silent = TRUE)
30: serialize(what, NULL, xdr = FALSE)
31: sendMaster(try(eval(expr, env), silent = TRUE), FALSE)
32: mcparallel(FUN(X[[i]], ...), name = names(X)[i], mc.set.seed = mc.set.seed, silent = mc.silent)
33: FUN(X[[i]], ...)
34: lapply(seq_along(X), function(i) mcparallel(FUN(X[[i]], ...), name = names(X)[i], mc.set.seed = mc.set.seed, silent = mc.silent))
35: papply(seq_len(ncol(folds)), function(i) try(dummyfct(weights = folds[, i], oobweights = OOBweights[, i]), silent = TRUE), mc.preschedule = mc.preschedule, ...)
36: applyFolds(m, folds = cv(rep(1, length(unique(m$id))), B = 2), grid = 0:5)
An irrecoverable exception occurred. R is aborting now ...
Error in dimnames(x) <- dn :
length of 'dimnames' [2] not equal to array extent
Calls: applyFolds -> colnames<-
In addition: Warning messages:
1: In .qr.rank.def.warn(r) :
matrix is structurally rank deficient; using augmented matrix with additional 56 row(s) of zeros
2: In .qr.rank.def.warn(r) :
matrix is structurally rank deficient; using augmented matrix with additional 21 row(s) of zeros
3: In FDboost(Y ~ bbsc(xsmoo), timeformula = ~bols(xfactor) %X% bbs(tvals), :
More than one variable specified in time formula,
'plot' and other convenience functions potentially not designed for that, yet.
4: In FDboost(Y ~ bbsc(xsmoo), timeformula = ~bols(xfactor) %X% bbs(tvals), :
For timeformulas with multiple variables dfs are not checked automatically.
Please make sure that all base-learner df are equal to ensure a fair selection.
5: In FDboost(Y ~ bbsc(xsmoo), timeformula = ~bols(xfactor) %X% bbs(tvals), :
In case of factor or multiple time variables no default offset implemented, yet.
offset is set to 0.
6: In mccollect(jobs) : 2 parallel jobs did not deliver results
Execution halted
OK
r-devel-windows-x86_64
*
OK
r-oldrel-macos-arm64
*
OK
r-oldrel-macos-x86_64
*
OK
r-oldrel-windows-x86_64
*
OK
r-patched-linux-x86_64
*
OK
r-release-linux-x86_64
*
OK
r-release-macos-arm64
*
OK
r-release-macos-x86_64
*
OK
r-release-windows-x86_64
*
Check History
ERROR 13 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE Mar 9, 2026
ERROR
r-devel-macos-arm64
tests
Running ‘factorize_test_irregular.R’ [1s/1s] Running ‘factorize_test_regular.R’ [1s/1s] Running ‘general_tests.R’ [3s/2s] 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 1.1-3. > library(gamboostLSS) Loading required package: gamlss.dist Attaching package: 'gamlss.dist' The following object is masked
Reverse Dependencies (3)
Dependency Network
Version History
new
1.1-3
Mar 9, 2026