Last updated on 2023-03-24 22:00:03 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.0 | 11.22 | 99.17 | 110.39 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.1.0 | 9.54 | 77.51 | 87.05 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 0.1.0 | 152.31 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 0.1.0 | 141.94 | NOTE | |||
| r-patched-linux-x86_64 | 0.1.0 | 8.16 | 97.57 | 105.73 | NOTE | |
| r-release-linux-x86_64 | 0.1.0 | 12.02 | 96.43 | 108.45 | NOTE | |
| r-release-macos-arm64 | 0.1.0 | 38.00 | NOTE | |||
| r-release-macos-x86_64 | 0.1.0 | 73.00 | NOTE | |||
| r-release-windows-x86_64 | 0.1.0 | 18.00 | 125.00 | 143.00 | NOTE | |
| r-oldrel-macos-arm64 | 0.1.0 | 46.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 0.1.0 | 63.00 | NOTE | |||
| r-oldrel-windows-ix86+x86_64 | 0.1.0 | 24.00 | 153.00 | 177.00 | NOTE |
Version: 0.1.0
Check: LazyData
Result: NOTE
'LazyData' is specified without a 'data' directory
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in ‘sambia-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: smoteMod
> ### Title: smoteMod is a modified version of the 'synthetic minority
> ### oversampling technique to generate new data.
> ### Aliases: smoteMod
>
> ### ** Examples
>
> ## simulate data for a population
> require(pROC)
Loading required package: pROC
Type 'citation("pROC")' for a citation.
Attaching package: ‘pROC’
The following objects are masked from ‘package:stats’:
cov, smooth, var
>
> set.seed(1342334)
> N = 100000
> x1 <- rnorm(N, mean=0, sd=1)
> x2 <- rt(N, df=25)
> x3 <- x1 + rnorm(N, mean=0, sd=.6)
> x4 <- x2 + rnorm(N, mean=0, sd=1.3)
> x5 <- rbinom(N, 1, prob=.6)
> x6 <- rnorm(N, 0, sd = 1) # noise not known as variable
> x7 <- x1*x5 # interaction
> x <- cbind(x1, x2, x3, x4, x5, x6, x7)
>
> ## stratum variable (covariate)
> xs <- c(rep(1,0.1*N), rep(0,(1-0.1)*N))
>
> ## effects
> beta <- c(-1, 0.2, 0.4, 0.4, 0.5, 0.5, 0.6)
> beta0 <- -2
>
> ## generate binary outcome
> linpred.slopes <- log(0.5)*xs + c(x %*% beta)
> eta <- beta0 + linpred.slopes
>
> p <- 1/(1+exp(-eta)) # this is the probability P(Y=1|X), we want the binary outcome however:
> y<-rbinom(n=N, size=1, prob=p) #
>
> population <- data.frame(y,xs,x)
>
> #### draw "given" data set for training
> sel.prob <- rep(1,N)
> sel.prob[population$xs == 1] <- 9
> sel.prob[population$y == 1] <- 8
> sel.prob[population$y == 1 & population$xs == 1] <- 150
> ind <- sample(1:N, 200, prob = sel.prob)
>
> data = population[ind, ]
>
> ## calculate weights from original numbers for xs and y
> w.matrix <- table(population$y, population$xs)/table(data$y, data$xs)
> w <- rep(NA, nrow(data))
> w[data$y==0 & data$xs ==0] <- w.matrix[1,1]
> w[data$y==1 & data$xs ==0] <- w.matrix[2,1]
> w[data$y==0 & data$xs ==1] <- w.matrix[1,2]
> w[data$y==1 & data$xs ==1] <- w.matrix[2,2]
>
> ### draw a test data set
> newdata = population[sample(1:N, size=200 ), ]
>
> K = 5
> genData = smoteMod(data.x = data[ , -which(colnames(data) %in% c('y', 'xs'))] ,
+ stratum = w, data.y = data$y, weights = w, K=K)
Error in get.knnx(data, query, k, algorithm) :
DLL requires the use of native symbols
Calls: smoteMod -> smoteNew -> <Anonymous> -> <Anonymous> -> get.knnx
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang