CRAN Package Check Results for Package FeaLect

Last updated on 2020-01-22 00:48:55 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.14 12.30 107.29 119.59 ERROR
r-devel-linux-x86_64-debian-gcc 1.14 10.58 91.61 102.19 OK
r-devel-linux-x86_64-fedora-clang 1.14 155.24 OK
r-devel-linux-x86_64-fedora-gcc 1.14 151.66 OK
r-devel-windows-ix86+x86_64 1.14 51.00 199.00 250.00 OK
r-devel-windows-ix86+x86_64-gcc8 1.14 54.00 198.00 252.00 OK
r-patched-linux-x86_64 1.14 9.64 102.43 112.07 OK
r-patched-solaris-x86 1.14 205.20 OK
r-release-linux-x86_64 1.14 8.97 103.13 112.10 OK
r-release-windows-ix86+x86_64 1.14 25.00 163.00 188.00 OK
r-release-osx-x86_64 1.14 OK
r-oldrel-windows-ix86+x86_64 1.14 14.00 147.00 161.00 OK
r-oldrel-osx-x86_64 1.14 OK

Check Details

Version: 1.14
Check: examples
Result: ERROR
    Running examples in 'FeaLect-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: FeaLect-package
    > ### Title: Scores Features for Feature Selection
    > ### Aliases: FeaLect-package
    > ### Keywords: package regression multivariate classif models
    >
    > ### ** Examples
    >
    > library(FeaLect)
    > data(mcl_sll)
    > F <- as.matrix(mcl_sll[ ,-1]) # The Feature matrix
    > L <- as.numeric(mcl_sll[ ,1]) # The labels
    > names(L) <- rownames(F)
    > message(dim(F)[1], " samples and ",dim(F)[2], " features.")
    22 samples and 236 features.
    >
    > ## For this data, total.num.of.models is suggested to be at least 100.
    > FeaLect.result.1 <-FeaLect(F=F,L=L,maximum.features.num=10,total.num.of.models=20,talk=TRUE)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    FeaLect
     --- call from context ---
    input.check.FeaLect(F_ = F, L_ = L, maximum.features.num = maximum.features.num,
     gamma = gamma)
     --- call from argument ---
    if (class(F_) != "matrix" | class(L_) != "numeric") stop(paste("Input error! F should be a matrix, L should be a numeric vector."))
     --- R stacktrace ---
    where 1: input.check.FeaLect(F_ = F, L_ = L, maximum.features.num = maximum.features.num,
     gamma = gamma)
    where 2: FeaLect(F = F, L = L, maximum.features.num = 10, total.num.of.models = 20,
     talk = TRUE)
    
     --- value of length: 2 type: logical ---
    [1] FALSE TRUE
     --- function from context ---
    function (F_, L_, maximum.features.num, gamma)
    {
     if (class(F_) != "matrix" | class(L_) != "numeric")
     stop(paste("Input error! F should be a matrix, L should be a numeric vector."))
     if (dim(F_)[1] != length(L_))
     stop(paste("Number of rows of F (input feature matrix) and length of L (vector of labels) should be the same."))
     if (is.null(colnames(F_))) {
     colnames(F_) <- 1:dim(F_)[2]
     }
     inds <- which(is.na(colnames(F_)))
     colnames(F_)[inds] <- paste("NA", inds, sep = "")
     if (is.null(rownames(F_)) | is.null(names(L_))) {
     rownames(F_) <- 1:dim(F_)[1]
     names(L_) <- 1:length(L_)
     }
     if (maximum.features.num > gamma * length(unique(rownames(F_)))) {
     maximum.features.num <- round(gamma * length(unique(rownames(F_))),
     0)
     warning("\nmaximum.features.num was more than gamma times the number of instances (number of distinct rows of F_). \n",
     "It was automatically reduced to: ", maximum.features.num,
     " (gamma times the number of instances,) \n", "because Lasso would not select more than this number of features by any bound value for lambda. \n",
     "The technical reason is that the linear equation will have exact solution with error zero!\n")
     }
     if (prod(rownames(F_) == names(L_)) == 0) {
     warning(" rownames(F_) != rownames(L_) !!! ", "\n The names of row of the labels (L_) should be exatcly the same as row names of features matrix (F_). \n")
     }
     return(list(F_ = F_, L_ = L_, maximum.features.num = maximum.features.num))
    }
    <bytecode: 0xa3ff780>
    <environment: namespace:FeaLect>
     --- function search by body ---
    Function input.check.FeaLect in namespace FeaLect has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(F_) != "matrix" | class(L_) != "numeric") stop(paste("Input error! F should be a matrix, L should be a numeric vector.")) :
     the condition has length > 1
    Calls: FeaLect -> input.check.FeaLect
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.14
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building 'FeaLect_feature_scorer.Rnw' using Sweave
    Loading required package: lars
    Loaded lars 1.2
    
    Loading required package: rms
    Loading required package: Hmisc
    Loading required package: lattice
    Loading required package: survival
    Loading required package: Formula
    Loading required package: ggplot2
    
    Attaching package: 'Hmisc'
    
    The following objects are masked from 'package:base':
    
     format.pval, units
    
    Loading required package: SparseM
    
    Attaching package: 'SparseM'
    
    The following object is masked from 'package:base':
    
     backsolve
    
    22 samples and 236 features.
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    FeaLect
     --- call from context ---
    input.check.FeaLect(F_ = F, L_ = L, maximum.features.num = maximum.features.num,
     gamma = gamma)
     --- call from argument ---
    if (class(F_) != "matrix" | class(L_) != "numeric") stop(paste("Input error! F should be a matrix, L should be a numeric vector."))
     --- R stacktrace ---
    where 1: input.check.FeaLect(F_ = F, L_ = L, maximum.features.num = maximum.features.num,
     gamma = gamma)
    where 2: FeaLect(F = F, L = L, maximum.features.num = 10, total.num.of.models = 100,
     talk = TRUE)
    where 3: eval(expr, .GlobalEnv)
    where 4: eval(expr, .GlobalEnv)
    where 5: withVisible(eval(expr, .GlobalEnv))
    where 6: doTryCatch(return(expr), name, parentenv, handler)
    where 7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
    where 8: tryCatchList(expr, classes, parentenv, handlers)
    where 9: tryCatch(expr, error = function(e) {
     call <- conditionCall(e)
     if (!is.null(call)) {
     if (identical(call[[1L]], quote(doTryCatch)))
     call <- sys.call(-4L)
     dcall <- deparse(call)[1L]
     prefix <- paste("Error in", dcall, ": ")
     LONG <- 75L
     sm <- strsplit(conditionMessage(e), "\n")[[1L]]
     w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")
     if (is.na(w))
     w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],
     type = "b")
     if (w > LONG)
     prefix <- paste0(prefix, "\n ")
     }
     else prefix <- "Error : "
     msg <- paste0(prefix, conditionMessage(e), "\n")
     .Internal(seterrmessage(msg[1L]))
     if (!silent && isTRUE(getOption("show.error.messages"))) {
     cat(msg, file = outFile)
     .Internal(printDeferredWarnings())
     }
     invisible(structure(msg, class = "try-error", condition = e))
    })
    where 10: try(withVisible(eval(expr, .GlobalEnv)), silent = TRUE)
    where 11: evalFunc(ce, options)
    where 12: tryCatchList(expr, classes, parentenv, handlers)
    where 13: tryCatch(evalFunc(ce, options), finally = {
     cat("\n")
     sink()
    })
    where 14: driver$runcode(drobj, chunk, chunkopts)
    where 15: utils::Sweave(...)
    where 16: engine$weave(file, quiet = quiet, encoding = enc)
    where 17: doTryCatch(return(expr), name, parentenv, handler)
    where 18: tryCatchOne(expr, names, parentenv, handlers[[1L]])
    where 19: tryCatchList(expr, classes, parentenv, handlers)
    where 20: tryCatch({
     engine$weave(file, quiet = quiet, encoding = enc)
     setwd(startdir)
     output <- find_vignette_product(name, by = "weave", engine = engine)
     if (!have.makefile && vignette_is_tex(output)) {
     texi2pdf(file = output, clean = FALSE, quiet = quiet)
     output <- find_vignette_product(name, by = "texi2pdf",
     engine = engine)
     }
     outputs <- c(outputs, output)
    }, error = function(e) {
     thisOK <<- FALSE
     fails <<- c(fails, file)
     message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
     file, conditionMessage(e)))
    })
    where 21: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/FeaLect.Rcheck/vign_test/FeaLect",
     ser_elibs = "/tmp/RtmppHTQXw/file1d5911ff0fff.rds")
    
     --- value of length: 2 type: logical ---
    [1] FALSE TRUE
     --- function from context ---
    function (F_, L_, maximum.features.num, gamma)
    {
     if (class(F_) != "matrix" | class(L_) != "numeric")
     stop(paste("Input error! F should be a matrix, L should be a numeric vector."))
     if (dim(F_)[1] != length(L_))
     stop(paste("Number of rows of F (input feature matrix) and length of L (vector of labels) should be the same."))
     if (is.null(colnames(F_))) {
     colnames(F_) <- 1:dim(F_)[2]
     }
     inds <- which(is.na(colnames(F_)))
     colnames(F_)[inds] <- paste("NA", inds, sep = "")
     if (is.null(rownames(F_)) | is.null(names(L_))) {
     rownames(F_) <- 1:dim(F_)[1]
     names(L_) <- 1:length(L_)
     }
     if (maximum.features.num > gamma * length(unique(rownames(F_)))) {
     maximum.features.num <- round(gamma * length(unique(rownames(F_))),
     0)
     warning("\nmaximum.features.num was more than gamma times the number of instances (number of distinct rows of F_). \n",
     "It was automatically reduced to: ", maximum.features.num,
     " (gamma times the number of instances,) \n", "because Lasso would not select more than this number of features by any bound value for lambda. \n",
     "The technical reason is that the linear equation will have exact solution with error zero!\n")
     }
     if (prod(rownames(F_) == names(L_)) == 0) {
     warning(" rownames(F_) != rownames(L_) !!! ", "\n The names of row of the labels (L_) should be exatcly the same as row names of features matrix (F_). \n")
     }
     return(list(F_ = F_, L_ = L_, maximum.features.num = maximum.features.num))
    }
    <bytecode: 0x75d0170>
    <environment: namespace:FeaLect>
     --- function search by body ---
    Function input.check.FeaLect in namespace FeaLect has this body.
     ----------- END OF FAILURE REPORT --------------
    
    Error: processing vignette 'FeaLect_feature_scorer.Rnw' failed with diagnostics:
     chunk 1 (label = FeaLect_example)
    Error in if (class(F_) != "matrix" | class(L_) != "numeric") stop(paste("Input error! F should be a matrix, L should be a numeric vector.")) :
     the condition has length > 1
    
    --- failed re-building 'FeaLect_feature_scorer.Rnw'
    
    SUMMARY: processing the following file failed:
     'FeaLect_feature_scorer.Rnw'
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang