MidnightBSD Magusdevel/R-cran-gbm
Extensions to AdaBoost algorithm
| Flavor | Version | Run | OSVersion | Arch | License | Restricted | Build | Fetch | Test | Scan | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.0.8_12 | 639 | 4.0 | amd64 | gpl2 | 0 | fail | untested | untested | untested |
License Permissions: dist-mirror dist-sell pkg-mirror pkg-sell auto-accept
Events
| Machine | Phase | Type | Time | Message |
|---|---|---|---|---|
| m4064 | info | 2026-05-27 14:55:56.330537 | Test Started | |
| m4064 | warn | 2026-05-27 14:59:21.177419 | MASTER_SITES contains non-HTTPS URLs: http://cran.utstat.utoronto.ca/src/contrib/, http://cran.utstat.utoronto.ca/src/contrib/Archive/gbm/ | |
| m4064 | warn | 2026-05-27 14:59:21.187661 | fake-qa reported: /usr/local/lib/R/library/gbm/libs/gbm.so is linked to /usr/local/lib/R/lib/libR.so.4 that does not belong to any package; /usr/local/lib/R/library/gbm/libs/gbm.so is linked to /usr/local/lib/gcc14/libgcc_s.so.1 that does not belong to any package | |
| m4064 | fail | 2026-05-27 14:59:21.19113 | make test returned non-zero: 1 | |
| m4064 | fail | 2026-05-27 14:59:21.3934 | Test complete. |
Build Log
[1m===> Testing for R-cran-gbm-2.0.8_12[0m
* using log directory '/magus/work/usr/mports/devel/R-cran-gbm/work/gbm.Rcheck'
* using R version 4.4.0 (2024-04-24)
* using platform: amd64-portbld-midnightbsd4.0
* R was compiled by
MidnightBSD clang version 19.1.7 (https://github.com/llvm/llvm-project.git llvmorg-19.1.7-0-gcd708029e0b2)
GNU Fortran (MidnightBSD Ports Collection) 14.2.0
* running under: MidnightBSD m4064 4.0.5 MidnightBSD 4.0.5 stable/4.0-n13794-ebf9c891ff GENERIC amd64
* using session charset: ASCII
* using options '--no-manual --no-build-vignettes'
* checking for file 'gbm/DESCRIPTION' ... OK
* this is package 'gbm' version '2.0-8'
* checking package namespace information ... OK
* checking package dependencies ... NOTE
Package suggested but not available for checking: 'RUnit'
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package 'gbm' can be installed ... WARNING
Found the following significant warnings:
node_categorical.cpp:42:23: warning: format specifies type 'int' but the argument has type 'unsigned long' [-Wformat]
node_categorical.cpp:45:22: warning: format specifies type 'int' but the argument has type 'unsigned long' [-Wformat]
node_categorical.cpp:52:27: warning: format specifies type 'int' but the argument has type 'unsigned long' [-Wformat]
node_categorical.cpp:55:22: warning: format specifies type 'int' but the argument has type 'unsigned long' [-Wformat]
node_continuous.cpp:38:12: warning: format specifies type 'int' but the argument has type 'unsigned long' [-Wformat]
node_continuous.cpp:44:12: warning: format specifies type 'int' but the argument has type 'unsigned long' [-Wformat]
See '/magus/work/usr/mports/devel/R-cran-gbm/work/gbm.Rcheck/00install.out' for details.
* used C++ compiler: 'MidnightBSD clang version 19.1.7 (https://github.com/llvm/llvm-project.git llvmorg-19.1.7-0-gcd708029e0b2)'
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... NOTE
File
LICENSE
is not mentioned in the DESCRIPTION file.
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... NOTE
'library' or 'require' call to 'RUnit' in package code.
Please use :: or requireNamespace() instead.
See section 'Suggested packages' in the 'Writing R Extensions' manual.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
basehaz.gbm: no visible global function definition for 'supsmu'
basehaz.gbm: no visible global function definition for 'approx'
calibrate.plot: no visible binding for global variable 'binomial'
calibrate.plot: no visible binding for global variable 'poisson'
calibrate.plot: no visible binding for global variable 'gaussian'
calibrate.plot: no visible global function definition for 'glm'
calibrate.plot: no visible global function definition for 'predict'
calibrate.plot: no visible global function definition for 'polygon'
calibrate.plot: no visible global function definition for 'lines'
calibrate.plot: no visible global function definition for 'abline'
gbm: no visible binding for global variable 'na.pass'
gbm: no visible global function definition for 'model.response'
gbm: no visible global function definition for 'model.weights'
gbm: no visible global function definition for 'model.offset'
gbm: no visible global function definition for 'model.frame'
gbm: no visible global function definition for 'terms'
gbm: no visible global function definition for 'reformulate'
gbm.fit: no visible global function definition for 'quantile'
gbm.loss: no visible global function definition for 'weighted.mean'
gbm.more: no visible global function definition for 'model.extract'
gbm.more: no visible global function definition for 'model.frame'
gbm.more: no visible global function definition for 'delete.response'
gbm.more: no visible binding for global variable 'na.pass'
gbm.perf: no visible global function definition for 'loess'
gbm.perf: no visible global function definition for 'par'
gbm.perf: no visible global function definition for 'lines'
gbm.perf: no visible global function definition for 'abline'
gbm.perf: no visible global function definition for 'axis'
gbm.perf: no visible global function definition for 'mtext'
interact.gbm: no visible global function definition for 'weighted.mean'
interact.gbm: no visible binding for global variable 'weighted.mean'
perf.pairwise: no visible global function definition for 'runif'
perf.pairwise: no visible global function definition for
'weighted.mean'
plot.gbm: no visible global function definition for 'lines'
plot.gbm: no visible global function definition for 'axis'
plot.gbm: no visible global function definition for 'mtext'
plot.gbm: no visible global function definition for 'segments'
plot.gbm: no visible global function definition for 'title'
predict.gbm: no visible global function definition for 'model.frame'
predict.gbm: no visible global function definition for 'terms'
predict.gbm: no visible global function definition for 'reformulate'
predict.gbm: no visible binding for global variable 'na.pass'
quantile.rug: no visible global function definition for 'quantile'
quantile.rug: no visible global function definition for 'rug'
shrink.gbm.pred: no visible global function definition for
'model.frame'
shrink.gbm.pred: no visible global function definition for
'delete.response'
shrink.gbm.pred: no visible binding for global variable 'na.pass'
summary.gbm: no visible global function definition for 'barplot'
summary.gbm: no visible global function definition for 'rainbow'
test.gbm: no visible global function definition for 'runif'
test.gbm: no visible global function definition for 'var'
test.gbm: no visible global function definition for 'rnorm'
test.gbm: no visible global function definition for 'predict'
test.gbm: no visible global function definition for 'checkTrue'
test.gbm: no visible global function definition for 'sd'
test.gbm: no visible global function definition for 'rexp'
test.gbm: no visible global function definition for 'rbinom'
test.relative.influence : : no visible global function
definition for 'rnorm'
test.relative.influence: no visible global function definition for
'checkEqualsNumeric'
validate.gbm: no visible global function definition for
'defineTestSuite'
validate.gbm: no visible global function definition for 'runTestSuite'
Undefined global functions or variables:
abline approx axis barplot binomial checkEqualsNumeric checkTrue
defineTestSuite delete.response gaussian glm lines loess
model.extract model.frame model.offset model.response model.weights
mtext na.pass par poisson polygon predict quantile rainbow rbinom
reformulate rexp rnorm rug runTestSuite runif sd segments supsmu
terms title var weighted.mean
Consider adding
importFrom("grDevices", "rainbow")
importFrom("graphics", "abline", "axis", "barplot", "lines", "mtext",
"par", "polygon", "rug", "segments", "title")
importFrom("stats", "approx", "binomial", "delete.response",
"gaussian", "glm", "loess", "model.extract", "model.frame",
"model.offset", "model.response", "model.weights",
"na.pass", "poisson", "predict", "quantile", "rbinom",
"reformulate", "rexp", "rnorm", "runif", "sd", "supsmu",
"terms", "var", "weighted.mean")
to your NAMESPACE file.
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... OK
* checking installed files from 'inst/doc' ... OK
* checking examples ... ERROR
Running examples in 'gbm-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: gbm
> ### Title: Generalized Boosted Regression Modeling
> ### Aliases: gbm gbm.more gbm.fit
> ### Keywords: models nonlinear survival nonparametric tree
>
> ### ** Examples
> # A least squares regression example # create some data
>
> N <- 1000
> X1 <- runif(N)
> X2 <- 2*runif(N)
> X3 <- ordered(sample(letters[1:4],N,replace=TRUE),levels=letters[4:1])
> X4 <- factor(sample(letters[1:6],N,replace=TRUE))
> X5 <- factor(sample(letters[1:3],N,replace=TRUE))
> X6 <- 3*runif(N)
> mu <- c(-1,0,1,2)[as.numeric(X3)]
>
> SNR <- 10 # signal-to-noise ratio
> Y <- X1**1.5 + 2 * (X2**.5) + mu
> sigma <- sqrt(var(Y)/SNR)
> Y <- Y + rnorm(N,0,sigma)
>
> # introduce some missing values
> X1[sample(1:N,size=500)] <- NA
> X4[sample(1:N,size=300)] <- NA
>
> data <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)
>
> # fit initial model
> gbm1 <-
+ gbm(Y~X1+X2+X3+X4+X5+X6, # formula
+ data=data, # dataset
+ var.monotone=c(0,0,0,0,0,0), # -1: monotone decrease,
+ # +1: monotone increase,
+ # 0: no monotone restrictions
+ distribution="gaussian", # see the help for other choices
+ n.trees=1000, # number of trees
+ shrinkage=0.05, # shrinkage or learning rate,
+ # 0.001 to 0.1 usually work
+ interaction.depth=3, # 1: additive model, 2: two-way interactions, etc.
+ bag.fraction = 0.5, # subsampling fraction, 0.5 is probably best
+ train.fraction = 0.5, # fraction of data for training,
+ # first train.fraction*N used for training
+ n.minobsinnode = 10, # minimum total weight needed in each node
+ cv.folds = 3, # do 3-fold cross-validation
+ keep.data=TRUE, # keep a copy of the dataset with the object
+ verbose=FALSE) # don't print out progress
>
> # check performance using an out-of-bag estimator
> # OOB underestimates the optimal number of iterations
> best.iter <- gbm.perf(gbm1,method="OOB")
Warning in gbm.perf(gbm1, method = "OOB") :
OOB generally underestimates the optimal number of iterations although predictive performance is reasonably competitive. Using cv.folds>0 when calling gbm usually results in improved predictive performance.
> print(best.iter)
[1] 91
>
> # check performance using a 50% heldout test set
> best.iter <- gbm.perf(gbm1,method="test")
> print(best.iter)
[1] 133
>
> # check performance using 5-fold cross-validation
> best.iter <- gbm.perf(gbm1,method="cv")
> print(best.iter)
[1] 137
>
> # plot the performance # plot variable influence
> summary(gbm1,n.trees=1) # based on the first tree
Length Class Mode
initF 1 -none- numeric
fit 1000 -none- numeric
train.error 1000 -none- numeric
valid.error 1000 -none- numeric
oobag.improve 1000 -none- numeric
trees 1000 -none- list
c.splits 725 -none- list
bag.fraction 1 -none- numeric
distribution 1 -none- list
interaction.depth 1 -none- numeric
n.minobsinnode 1 -none- numeric
num.classes 1 -none- numeric
n.trees 1 -none- numeric
nTrain 1 -none- numeric
train.fraction 1 -none- numeric
response.name 1 -none- character
shrinkage 1 -none- numeric
var.levels 6 -none- list
var.monotone 6 -none- numeric
var.names 6 -none- character
var.type 6 -none- numeric
verbose 1 -none- logical
data 6 -none- list
Terms 3 terms call
cv.error 1000 -none- numeric
cv.folds 1 -none- numeric
call 14 -none- call
m 5 -none- call
> summary(gbm1,n.trees=best.iter) # based on the estimated best number of trees
Length Class Mode
initF 1 -none- numeric
fit 1000 -none- numeric
train.error 1000 -none- numeric
valid.error 1000 -none- numeric
oobag.improve 1000 -none- numeric
trees 1000 -none- list
c.splits 725 -none- list
bag.fraction 1 -none- numeric
distribution 1 -none- list
interaction.depth 1 -none- numeric
n.minobsinnode 1 -none- numeric
num.classes 1 -none- numeric
n.trees 1 -none- numeric
nTrain 1 -none- numeric
train.fraction 1 -none- numeric
response.name 1 -none- character
shrinkage 1 -none- numeric
var.levels 6 -none- list
var.monotone 6 -none- numeric
var.names 6 -none- character
var.type 6 -none- numeric
verbose 1 -none- logical
data 6 -none- list
Terms 3 terms call
cv.error 1000 -none- numeric
cv.folds 1 -none- numeric
call 14 -none- call
m 5 -none- call
>
> # compactly print the first and last trees for curiosity
> print(pretty.gbm.tree(gbm1,1))
SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight
0 2 1.500000000 1 5 9 212.10072 249
1 1 0.665744827 2 3 4 47.89661 108
2 -1 -0.090595276 -1 -1 -1 0.00000 41
3 -1 -0.021982507 -1 -1 -1 0.00000 67
4 -1 -0.048029947 -1 -1 -1 0.00000 108
5 1 0.812172223 6 7 8 49.83074 141
6 -1 0.015986696 -1 -1 -1 0.00000 72
7 -1 0.075448410 -1 -1 -1 0.00000 69
8 -1 0.045084982 -1 -1 -1 0.00000 141
9 -1 0.004697784 -1 -1 -1 0.00000 249
Prediction
0 0.004697784
1 -0.048029947
2 -0.090595276
3 -0.021982507
4 -0.048029947
5 0.045084982
6 0.015986696
7 0.075448410
8 0.045084982
9 0.004697784
> print(pretty.gbm.tree(gbm1,gbm1$n.trees))
SplitVar SplitCodePred LeftNode RightNode MissingNode ErrorReduction Weight
0 1 1.7476068130 1 8 9 0.2515803 249
1 0 0.2598220657 2 6 7 0.2409673 217
2 1 0.7957157667 3 4 5 0.3137274 24
3 -1 -0.0089752058 -1 -1 -1 0.0000000 14
4 -1 0.0026202495 -1 -1 -1 0.0000000 10
5 -1 -0.0041437661 -1 -1 -1 0.0000000 24
6 -1 0.0006726056 -1 -1 -1 0.0000000 80
7 -1 0.0013544047 -1 -1 -1 0.0000000 113
8 -1 -0.0042540531 -1 -1 -1 0.0000000 32
9 -1 -0.0001153570 -1 -1 -1 0.0000000 249
Prediction
0 -0.0001153570
1 0.0004949576
2 -0.0041437661
3 -0.0089752058
4 0.0026202495
5 -0.0041437661
6 0.0006726056
7 0.0013544047
8 -0.0042540531
9 -0.0001153570
>
> # make some new data
> N <- 1000
> X1 <- runif(N)
> X2 <- 2*runif(N)
> X3 <- ordered(sample(letters[1:4],N,replace=TRUE))
> X4 <- factor(sample(letters[1:6],N,replace=TRUE))
> X5 <- factor(sample(letters[1:3],N,replace=TRUE))
> X6 <- 3*runif(N)
> mu <- c(-1,0,1,2)[as.numeric(X3)]
>
> Y <- X1**1.5 + 2 * (X2**.5) + mu + rnorm(N,0,sigma)
>
> data2 <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)
>
> # predict on the new data using "best" number of trees
> # f.predict generally will be on the canonical scale (logit,log,etc.)
> f.predict <- predict(gbm1,data2,best.iter)
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "gbm"
Calls: predict
Execution halted
* DONE
Status: 1 ERROR, 1 WARNING, 4 NOTEs
See
'/magus/work/usr/mports/devel/R-cran-gbm/work/gbm.Rcheck/00check.log'
for details.
*** Error code 1
Stop.
make: stopped in /usr/mports/devel/R-cran-gbm
Links
Depends On
- devel/binutils (build)
- lang/gcc14 (build)
- math/R (build)
- lang/gcc14 (run)
- math/R (run)
Depend Of
NothingCategories
CVEs
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