Last updated on 2020-08-07 01:49:34 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0 | 247.18 | 60.14 | 307.32 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0 | 173.97 | 46.47 | 220.44 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0 | 432.26 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0 | 358.90 | NOTE | |||
| r-devel-windows-ix86+x86_64 | 1.0 | 441.00 | 107.00 | 548.00 | ERROR | |
| r-patched-linux-x86_64 | 1.0 | 206.13 | 59.57 | 265.70 | OK | |
| r-patched-solaris-x86 | 1.0 | 364.00 | NOTE | |||
| r-release-linux-x86_64 | 1.0 | 206.84 | 59.07 | 265.91 | OK | |
| r-release-macos-x86_64 | 1.0 | NOTE | ||||
| r-release-windows-ix86+x86_64 | 1.0 | 438.00 | 140.00 | 578.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 1.0 | 321.00 | 168.00 | 489.00 | NOTE |
Version: 1.0
Check: installed package size
Result: NOTE
installed size is 28.7Mb
sub-directories of 1Mb or more:
libs 28.5Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64
Version: 1.0
Check: dependencies in R code
Result: NOTE
Namespace in Imports field not imported from: ‘mvnfast’
All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-macos-x86_64, r-oldrel-macos-x86_64
Version: 1.0
Check: running examples for arch ‘i386’
Result: ERROR
Running examples in 'bartBMA-Ex.R' failed
The error most likely occurred in:
> ### Name: ITEs_bartBMA
> ### Title: ITE Predictions (in-sample) using bartBMA and the method
> ### described by Hill (2011)
> ### Aliases: ITEs_bartBMA
>
> ### ** Examples
>
> n <- 250
> x1 <- rnorm(n)
> x2 <- rnorm(n)
> x3 <- rnorm(n)
> x4 <- rbinom(n,1,0.5)
> x5 <- as.factor(sample( LETTERS[1:3], n, replace=TRUE))
>
> p= 0
> xnoise = matrix(rnorm(n*p), nrow=n)
> x5A <- ifelse(x5== 'A',1,0)
> x5B <- ifelse(x5== 'B',1,0)
> x5C <- ifelse(x5== 'C',1,0)
>
> x_covs_train <- cbind(x1,x2,x3,x4,x5A,x5B,x5C,xnoise)
>
> #Treatment effect
> #tautrain <- 3
> tautrain <- 1+2*x_covs_train[,2]*x_covs_train[,4]
>
> #Prognostic function
> mutrain <- 1 + 2*x_covs_train[,5] -1*x_covs_train[,6]-4*x_covs_train[,7] +
+ x_covs_train[,1]*x_covs_train[,3]
> sd_mtrain <- sd(mutrain)
> utrain <- runif(n)
> #pitrain <- 0.8*pnorm((3*mutrain/sd_mtrain)-0.5*x_covs_train[,1])+0.05+utrain/10
> pitrain <- 0.5
> ztrain <- rbinom(n,1,pitrain)
> ytrain <- mutrain + tautrain*ztrain
> #pihattrain <- pbart(x_covs_train,ztrain )$prob.train.mean
>
> #set lower and upper quantiles for intervals
> lbound <- 0.025
> ubound <- 0.975
>
> example_output <- ITEs_bartBMA(x_covariates = x_covs_train,
+ z_train = ztrain,
+ y_train = ytrain)
>
>
>
> cleanEx()
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64