Last updated on 2024-11-12 15:49:56 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.9-11 | 16.35 | 276.36 | 292.71 | OK | |
| r-devel-linux-x86_64-debian-gcc | 2.9-11 | 10.96 | 172.15 | 183.11 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 2.9-11 | 467.18 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2.9-11 | 454.83 | OK | |||
| r-devel-windows-x86_64 | 2.9-11 | 18.00 | 136.00 | 154.00 | OK | --no-vignettes |
| r-patched-linux-x86_64 | 2.9-11 | 14.64 | 259.58 | 274.22 | OK | |
| r-release-linux-x86_64 | 2.9-11 | 15.78 | 263.52 | 279.30 | OK | |
| r-release-macos-arm64 | 2.9-11 | 141.00 | OK | |||
| r-release-macos-x86_64 | 2.9-11 | 464.00 | OK | |||
| r-release-windows-x86_64 | 2.9-11 | 17.00 | 137.00 | 154.00 | OK | --no-vignettes |
| r-oldrel-macos-arm64 | 2.9-11 | 198.00 | OK | |||
| r-oldrel-macos-x86_64 | 2.9-11 | 313.00 | OK | |||
| r-oldrel-windows-x86_64 | 2.9-11 | 24.00 | 185.00 | 209.00 | OK | --no-vignettes |
Version: 2.9-11
Check: examples
Result: ERROR
Running examples in ‘mboost-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: baselearners
> ### Title: Base-learners for Gradient Boosting
> ### Aliases: baselearners baselearner base-learner bols bbs bspatial brad
> ### bkernel brandom btree bmono bmrf buser bns bss %+% %X% %O%
> ### Keywords: models
>
> ### ** Examples
>
>
> set.seed(290875)
>
> n <- 100
> x1 <- rnorm(n)
> x2 <- rnorm(n) + 0.25 * x1
> x3 <- as.factor(sample(0:1, 100, replace = TRUE))
> x4 <- gl(4, 25)
> y <- 3 * sin(x1) + x2^2 + rnorm(n)
> weights <- drop(rmultinom(1, n, rep.int(1, n) / n))
>
> ### set up base-learners
> spline1 <- bbs(x1, knots = 20, df = 4)
> extract(spline1, "design")[1:10, 1:10]
1 2 3 4 5 6 7 8 9
[1,] 0 0 0 0 0.00000000 0.00000000 0.000000e+00 0.000000000 0.000000000
[2,] 0 0 0 0 0.00000000 0.00000000 0.000000e+00 0.000000000 0.000000000
[3,] 0 0 0 0 0.00000000 0.00000000 0.000000e+00 0.000000000 0.000000000
[4,] 0 0 0 0 0.00000000 0.00000000 0.000000e+00 0.000000000 0.000000000
[5,] 0 0 0 0 0.01490533 0.44554054 5.113987e-01 0.028155480 0.000000000
[6,] 0 0 0 0 0.00000000 0.00000000 0.000000e+00 0.000000000 0.000000000
[7,] 0 0 0 0 0.00000000 0.06481227 6.035695e-01 0.328334430 0.003283771
[8,] 0 0 0 0 0.00000000 0.00000000 0.000000e+00 0.000000000 0.000000000
[9,] 0 0 0 0 0.00000000 0.00000000 1.551197e-09 0.167720617 0.666662247
[10,] 0 0 0 0 0.00000000 0.00000000 0.000000e+00 0.009196839 0.401902997
10
[1,] 0.0000000
[2,] 0.0000000
[3,] 0.0000000
[4,] 0.0000000
[5,] 0.0000000
[6,] 0.0000000
[7,] 0.0000000
[8,] 0.0000000
[9,] 0.1656171
[10,] 0.5493155
> extract(spline1, "penalty")
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] 1 -2 1 0 0 0 0 0 0 0 0 0 0
[2,] -2 5 -4 1 0 0 0 0 0 0 0 0 0
[3,] 1 -4 6 -4 1 0 0 0 0 0 0 0 0
[4,] 0 1 -4 6 -4 1 0 0 0 0 0 0 0
[5,] 0 0 1 -4 6 -4 1 0 0 0 0 0 0
[6,] 0 0 0 1 -4 6 -4 1 0 0 0 0 0
[7,] 0 0 0 0 1 -4 6 -4 1 0 0 0 0
[8,] 0 0 0 0 0 1 -4 6 -4 1 0 0 0
[9,] 0 0 0 0 0 0 1 -4 6 -4 1 0 0
[10,] 0 0 0 0 0 0 0 1 -4 6 -4 1 0
[11,] 0 0 0 0 0 0 0 0 1 -4 6 -4 1
[12,] 0 0 0 0 0 0 0 0 0 1 -4 6 -4
[13,] 0 0 0 0 0 0 0 0 0 0 1 -4 6
[14,] 0 0 0 0 0 0 0 0 0 0 0 1 -4
[15,] 0 0 0 0 0 0 0 0 0 0 0 0 1
[16,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[17,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[18,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[19,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[20,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[21,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[22,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[23,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[24,] 0 0 0 0 0 0 0 0 0 0 0 0 0
[,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
[1,] 0 0 0 0 0 0 0 0 0 0 0
[2,] 0 0 0 0 0 0 0 0 0 0 0
[3,] 0 0 0 0 0 0 0 0 0 0 0
[4,] 0 0 0 0 0 0 0 0 0 0 0
[5,] 0 0 0 0 0 0 0 0 0 0 0
[6,] 0 0 0 0 0 0 0 0 0 0 0
[7,] 0 0 0 0 0 0 0 0 0 0 0
[8,] 0 0 0 0 0 0 0 0 0 0 0
[9,] 0 0 0 0 0 0 0 0 0 0 0
[10,] 0 0 0 0 0 0 0 0 0 0 0
[11,] 0 0 0 0 0 0 0 0 0 0 0
[12,] 1 0 0 0 0 0 0 0 0 0 0
[13,] -4 1 0 0 0 0 0 0 0 0 0
[14,] 6 -4 1 0 0 0 0 0 0 0 0
[15,] -4 6 -4 1 0 0 0 0 0 0 0
[16,] 1 -4 6 -4 1 0 0 0 0 0 0
[17,] 0 1 -4 6 -4 1 0 0 0 0 0
[18,] 0 0 1 -4 6 -4 1 0 0 0 0
[19,] 0 0 0 1 -4 6 -4 1 0 0 0
[20,] 0 0 0 0 1 -4 6 -4 1 0 0
[21,] 0 0 0 0 0 1 -4 6 -4 1 0
[22,] 0 0 0 0 0 0 1 -4 6 -4 1
[23,] 0 0 0 0 0 0 0 1 -4 5 -2
[24,] 0 0 0 0 0 0 0 0 1 -2 1
> knots.x2 <- quantile(x2, c(0.25, 0.5, 0.75))
> spline2 <- bbs(x2, knots = knots.x2, df = 5)
> ols3 <- bols(x3)
> extract(ols3)
(Intercept) x31
1 1 1
3 1 0
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$x3
[1] "contr.treatment"
> ols4 <- bols(x4)
>
> ### compute base-models
> drop(ols3$dpp(weights)$fit(y)$model) ## same as:
(Intercept) x31
1.3842061 0.2083405
> coef(lm(y ~ x3, weights = weights))
(Intercept) x31
1.3842061 0.2083405
>
> drop(ols4$dpp(weights)$fit(y)$model) ## same as:
(Intercept) x42 x43 x44
0.9162875 0.3180593 0.8982705 0.8162401
> coef(lm(y ~ x4, weights = weights))
(Intercept) x42 x43 x44
0.9162875 0.3180593 0.8982705 0.8162401
>
> ### fit model, component-wise
> mod1 <- mboost_fit(list(spline1, spline2, ols3, ols4), y, weights)
>
> ### more convenient formula interface
> mod2 <- mboost(y ~ bbs(x1, knots = 20, df = 4) +
+ bbs(x2, knots = knots.x2, df = 5) +
+ bols(x3) + bols(x4), weights = weights)
> all.equal(coef(mod1), coef(mod2))
[1] TRUE
>
>
> ### grouped linear effects
> # center x1 and x2 first
> x1 <- scale(x1, center = TRUE, scale = FALSE)
> x2 <- scale(x2, center = TRUE, scale = FALSE)
> model <- gamboost(y ~ bols(x1, x2, intercept = FALSE) +
+ bols(x1, intercept = FALSE) +
+ bols(x2, intercept = FALSE),
+ control = boost_control(mstop = 50))
> coef(model, which = 1) # one base-learner for x1 and x2
$`bols(x1, x2, intercept = FALSE)`
x1 x2
1.81077137 -0.02249335
attr(,"offset")
[1] 1.334042
> coef(model, which = 2:3) # two separate base-learners for x1 and x2
$`bols(x1, intercept = FALSE)`
x1
0
$`bols(x2, intercept = FALSE)`
x2
0
attr(,"offset")
[1] 1.334042
> # zero because they were (not yet) selected.
>
> ### example for bspatial
> x1 <- runif(250,-pi,pi)
> x2 <- runif(250,-pi,pi)
>
> y <- sin(x1) * sin(x2) + rnorm(250, sd = 0.4)
>
> spline3 <- bspatial(x1, x2, knots = 12)
> Xmat <- extract(spline3, "design")
> ## 12 inner knots + 4 boundary knots = 16 knots per direction
> ## THUS: 16 * 16 = 256 columns
> dim(Xmat)
[1] 250 256
> extract(spline3, "penalty")[1:10, 1:10]
10 x 10 sparse Matrix of class "dsCMatrix"
[1,] 2 -2 1 . . . . . . .
[2,] -2 6 -4 1 . . . . . .
[3,] 1 -4 7 -4 1 . . . . .
[4,] . 1 -4 7 -4 1 . . . .
[5,] . . 1 -4 7 -4 1 . . .
[6,] . . . 1 -4 7 -4 1 . .
[7,] . . . . 1 -4 7 -4 1 .
[8,] . . . . . 1 -4 7 -4 1
[9,] . . . . . . 1 -4 7 -4
[10,] . . . . . . . 1 -4 7
>
> ## specify number of knots separately
> form1 <- y ~ bspatial(x1, x2, knots = list(x1 = 12, x2 = 14))
>
> ## decompose spatial effect into parametric part and
> ## deviation with one df
> form2 <- y ~ bols(x1) + bols(x2) + bols(x1, by = x2, intercept = FALSE) +
+ bspatial(x1, x2, knots = 12, center = TRUE, df = 1)
>
> mod1 <- gamboost(form1)
> ## Not run:
> ##D plot(mod1)
> ##D
> ## End(Not run)
>
> mod2 <- gamboost(form2)
> ## automated plot function:
> ## Not run:
> ##D plot(mod2)
> ##D
> ## End(Not run)
> ## plot sum of linear and smooth effects:
> library("lattice")
> df <- expand.grid(x1 = unique(x1), x2 = unique(x2))
> df$pred <- predict(mod2, newdata = df)
> ## Not run:
> ##D levelplot(pred ~ x1 * x2, data = df)
> ##D
> ## End(Not run)
>
> ## specify radial basis function base-learner for spatial effect
> ## and use data-adaptive effective range (theta = NULL, see 'args')
> form3 <- y ~ brad(x1, x2)
> ## Now use different settings, e.g. 50 knots and theta fixed to 0.4
> ## (not really a good setting)
> form4 <- y ~ brad(x1, x2, knots = 50, args = list(theta = 0.4))
>
> mod3 <- gamboost(form3)
Loading required namespace: fields
Failed with error: ‘there is no package called ‘fields’’
Error in hyper_brad(mf, vary, knots = knots, df = df, lambda = lambda, :
Cannot load package‘fields’, which is needed for the automatic knot placement
Calls: gamboost ... eval -> eval -> brad -> bl_lin -> newX -> hyper_brad
Execution halted
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
Family 5.144 0.048 5.509
Flavor: r-devel-linux-x86_64-debian-gcc