Last updated on 2019-03-22 19:48:52 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.9-1 | 15.31 | 481.83 | 497.14 | OK | |
| r-devel-linux-x86_64-debian-gcc | 2.9-1 | 13.47 | 356.87 | 370.34 | OK | |
| r-devel-linux-x86_64-fedora-clang | 2.9-1 | 602.82 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 2.9-1 | 545.59 | OK | |||
| r-devel-windows-ix86+x86_64 | 2.9-1 | 39.00 | 158.00 | 197.00 | OK | --no-vignettes |
| r-patched-linux-x86_64 | 2.9-1 | 11.78 | 478.34 | 490.12 | OK | |
| r-patched-solaris-x86 | 2.9-1 | 801.90 | OK | |||
| r-release-linux-x86_64 | 2.9-1 | 11.49 | 480.05 | 491.54 | OK | |
| r-release-windows-ix86+x86_64 | 2.9-1 | 22.00 | 146.00 | 168.00 | OK | --no-vignettes |
| r-release-osx-x86_64 | 2.9-1 | OK | ||||
| r-oldrel-windows-ix86+x86_64 | 2.9-1 | 18.00 | 169.00 | 187.00 | ERROR | --no-vignettes |
| r-oldrel-osx-x86_64 | 2.9-1 | OK |
Version: 2.9-1
Flags: --no-vignettes
Check: running examples for arch ‘i386’
Result: ERROR
Running examples in 'mboost-Ex.R' failed
The error most likely occurred in:
> ### 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 0
3 1 1
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
1.094457 1.008338
> coef(lm(y ~ x3, weights = weights))
(Intercept) x31
1.094457 1.008338
>
> 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 "dgTMatrix"
Error in isFALSE(suppRows) : could not find function "isFALSE"
Calls: <Anonymous> -> <Anonymous> -> printSpMatrix2
Execution halted
Flavor: r-oldrel-windows-ix86+x86_64
Version: 2.9-1
Flags: --no-vignettes
Check: running examples for arch ‘x64’
Result: ERROR
Running examples in 'mboost-Ex.R' failed
The error most likely occurred in:
> ### 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 0
3 1 1
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)$model) ## same as:
(Intercept) x31
1.094457 1.008338
> coef(lm(y ~ x3, weights = weights))
(Intercept) x31
1.094457 1.008338
>
> drop(ols4$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 "dgTMatrix"
Error in isFALSE(suppRows) : could not find function "isFALSE"
Calls: <Anonymous> -> <Anonymous> -> printSpMatrix2
Execution halted
Flavor: r-oldrel-windows-ix86+x86_64