CRAN Package Check Results for Package jomo

Last updated on 2019-04-21 01:47:52 CEST.

Check Details

Version: 2.6-7
Check: whether package can be installed
Result: WARN
    Found the following significant warnings:
     jomo1clmmC.c:105:3: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
     jomo1clmmhrC.c:107:3: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
     jomo2clmmC.c:128:3: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
     jomo2clmmhrC.c:132:3: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
     jomooprobitC.c:84:3: warning: explicitly assigning value of variable of type 'int' to itself [-Wself-assign]
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-fedora-clang, r-release-osx-x86_64

Version: 2.6-7
Check: examples
Result: ERROR
    Running examples in ‘jomo-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: jomo.clmm
    > ### Title: Joint Modelling Imputation Compatible with Cumulative Link Mixed
    > ### Model
    > ### Aliases: jomo.clmm
    >
    > ### ** Examples
    >
    >
    >
    > # make sure social is a factor:
    >
    > cldata<-within(cldata, social<-factor(social))
    >
    > # we define the data frame with all the variables
    >
    > data<-cldata[,c("measure","age", "social", "city")]
    >
    > # And the formula of the substantive lm model
    > # social as an outcome only because it is the only ordinal variable in the dataset...
    >
    > formula<-as.formula(social~age+measure+(1|city))
    >
    > #And finally we run the imputation function:
    >
    > imp<-jomo.clmm(formula,data, nburn=2, nbetween=2, nimp=2)
    This function is beta software. Please use carefully and report any bug to the package mantainer
    
     *** caught segfault ***
    address 3ff00008, cause 'memory not mapped'
    
    Traceback:
     1: .C("NRalgv3", as.integer(ctrl$trace), as.integer(ctrl$maxIter), as.double(ctrl$gradTol), as.integer(ctrl$maxLineIter), as.integer(grFac), as.double(tau), as.double(o1), as.double(o2), as.double(eta1Fix), as.double(eta2Fix), as.double(sigma), as.integer(linkInt), as.double(wts), u = as.double(uStart), fitted = as.double(fitted), funValue = double(1), gradValues = as.double(uStart), hessValues = as.double(rep(1, length(uStart))), length(fitted), length(uStart), maxGrad = double(1), conv = 0L, as.double(lambda), Niter = as.integer(Niter))
     2: eval(substitute(expr), data, enclos = parent.frame())
     3: eval(substitute(expr), data, enclos = parent.frame())
     4: with.default(rho, { .C("NRalgv3", as.integer(ctrl$trace), as.integer(ctrl$maxIter), as.double(ctrl$gradTol), as.integer(ctrl$maxLineIter), as.integer(grFac), as.double(tau), as.double(o1), as.double(o2), as.double(eta1Fix), as.double(eta2Fix), as.double(sigma), as.integer(linkInt), as.double(wts), u = as.double(uStart), fitted = as.double(fitted), funValue = double(1), gradValues = as.double(uStart), hessValues = as.double(rep(1, length(uStart))), length(fitted), length(uStart), maxGrad = double(1), conv = 0L, as.double(lambda), Niter = as.integer(Niter))[c("u", "fitted", "funValue", "gradValues", "hessValues", "maxGrad", "conv", "Niter")]})
     5: with(rho, { .C("NRalgv3", as.integer(ctrl$trace), as.integer(ctrl$maxIter), as.double(ctrl$gradTol), as.integer(ctrl$maxLineIter), as.integer(grFac), as.double(tau), as.double(o1), as.double(o2), as.double(eta1Fix), as.double(eta2Fix), as.double(sigma), as.integer(linkInt), as.double(wts), u = as.double(uStart), fitted = as.double(fitted), funValue = double(1), gradValues = as.double(uStart), hessValues = as.double(rep(1, length(uStart))), length(fitted), length(uStart), maxGrad = double(1), conv = 0L, as.double(lambda), Niter = as.integer(Niter))[c("u", "fitted", "funValue", "gradValues", "hessValues", "maxGrad", "conv", "Niter")]})
     6: update.uC(rho)
     7: obj.fun(rho, par)
     8: objective(.par, ...)
     9: nlminb(rho$par, function(par) obj.fun(rho, par), control = control)
    10: doTryCatch(return(expr), name, parentenv, handler)
    11: tryCatchOne(expr, names, parentenv, handlers[[1L]])
    12: tryCatchList(expr, classes, parentenv, handlers)
    13: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call)[1L] prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})
    14: try(nlminb(rho$par, function(par) obj.fun(rho, par), control = control), silent = TRUE)
    15: clmm.fit.ssr(rho, control = control$optCtrl, method = control$method, Hess)
    16: ordinal::clmm(formula, data = data, na.action = na.omit, Hess = T, link = "probit")
    17: jomo.clmm(formula, data, nburn = 2, nbetween = 2, nimp = 2)
    An irrecoverable exception occurred. R is aborting now ...
Flavor: r-patched-solaris-x86