CRAN Package Check Results for Package DivE

Last updated on 2020-01-27 00:48:01 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.1 6.24 49.74 55.98 ERROR
r-devel-linux-x86_64-debian-gcc 1.1 5.80 44.27 50.07 OK
r-devel-linux-x86_64-fedora-clang 1.1 74.33 OK
r-devel-linux-x86_64-fedora-gcc 1.1 74.71 OK
r-devel-windows-ix86+x86_64 1.1 18.00 96.00 114.00 OK
r-devel-windows-ix86+x86_64-gcc8 1.1 20.00 95.00 115.00 OK
r-patched-linux-x86_64 1.1 5.70 50.88 56.58 OK
r-patched-solaris-x86 1.1 109.00 OK
r-release-linux-x86_64 1.1 5.84 51.24 57.08 OK
r-release-windows-ix86+x86_64 1.1 10.00 64.00 74.00 OK
r-release-osx-x86_64 1.1 OK
r-oldrel-windows-ix86+x86_64 1.1 7.00 73.00 80.00 OK
r-oldrel-osx-x86_64 1.1 OK

Check Details

Version: 1.1
Check: examples
Result: ERROR
    Running examples in 'DivE-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: DiveMaster
    > ### Title: DiveMaster
    > ### Aliases: DiveMaster print.DiveMaster summary.DiveMaster
    > ### print.summary.DiveMaster
    > ### Keywords: diversity
    >
    > ### ** Examples
    >
    > require(DivE)
    > data(Bact1)
    > data(ModelSet)
    > data(ParamSeeds)
    > data(ParamRanges)
    >
    > testmodels <- list()
    > testmeta <- list()
    > paramranges <- list()
    >
    > # Choose a single model
    > testmodels <- c(testmodels, ModelSet[1])
    > #testmeta[[1]] <- (ParamSeeds[[1]]) # Commented out for sake of brevity)
    > testmeta[[1]] <- matrix(c(0.9451638, 0.007428265, 0.9938149, 1.0147441, 0.009543598, 0.9870419),
    + nrow=2, byrow=TRUE, dimnames=list(c(), c("a1", "a2", "a3"))) # Example seeds
    > paramranges[[1]] <- ParamRanges[[1]]
    >
    >
    > # Create DivSubsamples object (NB: For quick illustration only -- not default parameters)
    > dss_1 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=40, NResamples=5)
    > dss_2 <- DivSubsamples(Bact1, nrf=2, minrarefac=1, maxrarefac=65, NResamples=5)
    > dss <- list(dss_2, dss_1)
    >
    > # Implement the function (NB: For quick illustration only -- not default parameters)
    > out <- DiveMaster(models=testmodels, init.params=testmeta, param.ranges=paramranges,
    + main.samp=Bact1, subsizes=c(65, 40), NResamples=5, fitloops=1,
    + dssamp=dss, numit=2, varleft=10)
    Loading predefined subsamples
    Fitting model 1 of 1 (Est. time remaining: tbd mins)
    Fitting loop 1
    Performing fitting routine for sample 1
    Choosing optimal initial parameters for global fit
    Performing global fit
    Performing local fit
    Performing fitting routine for sample 2
    Choosing optimal initial parameters for global fit
    Performing global fit
    Performing local fit
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    DivE
     --- call from context ---
    FitAllSubs(SS, model.list, init.param, param.range, dSS, numit,
     varleft, tot.pop, v1, minplaus)
     --- call from argument ---
    if ((class(sim.local.mat.tmp) != "try-error") & (class(sim.global.mat.tmp) !=
     "try-error")) {
     sim.local.mat <- sim.local.mat.tmp
     sim.global.mat <- sim.global.mat.tmp
    }
     --- R stacktrace ---
    where 1: FitAllSubs(SS, model.list, init.param, param.range, dSS, numit,
     varleft, tot.pop, v1, minplaus)
    where 2: FitSingleMod(model.list = models[i], init.param = init.params[[i]],
     param.range = param.ranges[[i]], main.samp, data.default = FALSE,
     tot.pop = tot.pop, numit = numit, varleft = varleft, subsizes = mas.SS,
     dssamps = mas.dss, nrf = nrf, minrarefac = minrarefac, NResamples = NResamples,
     minplaus = minplaus, fitloops = fitloops)
    where 3: MultipleScoring(models, init.params, param.ranges, main.samp,
     tot.pop, numit, varleft, subsizes, dssamps, nrf, minrarefac,
     NResamples, minplaus, precision.lv, plaus.pen, crit.wts,
     fitloops, numpred)
    where 4: DiveMaster(models = testmodels, init.params = testmeta, param.ranges = paramranges,
     main.samp = Bact1, subsizes = c(65, 40), NResamples = 5,
     fitloops = 1, dssamp = dss, numit = 2, varleft = 10)
    
     --- value of length: 2 type: logical ---
    [1] TRUE TRUE
     --- function from context ---
    function (SS, model.list, init.param, param.range, dSS, numit,
     varleft, tot.pop, v1, minsampsize)
    {
     model <- model.list[[1]]
     model.name <- names(model.list)
     lower.bd <- param.range[1, ]
     upper.bd <- param.range[2, ]
     param.mat <- matrix(nrow = length(SS), ncol = ncol(init.param))
     rownames(param.mat) <- as.character(SS)
     colnames(param.mat) <- names(init.param[1, ])
     ssr.mat <- matrix(nrow = length(SS), ncol = 1)
     rownames(ssr.mat) <- as.character(SS)
     colnames(ssr.mat) <- c("Sum_of_squares")
     ms.mat <- matrix(nrow = length(SS), ncol = 1)
     rownames(ms.mat) <- as.character(SS)
     colnames(ms.mat) <- c("Mean_squared_residual")
     discrep.mat <- matrix(nrow = length(SS), ncol = 1)
     rownames(discrep.mat) <- as.character(SS)
     colnames(discrep.mat) <- c("Mean_absolute_error")
     local.mat <- matrix(nrow = length(SS), ncol = 1)
     rownames(local.mat) <- as.character(SS)
     colnames(local.mat) <- c("Predicted_local_diversity")
     global.mat <- matrix(nrow = length(SS), ncol = 1)
     rownames(global.mat) <- as.character(SS)
     colnames(global.mat) <- c("Predicted_global_diversity")
     AccuracyToObserved.mat <- matrix(nrow = length(SS), ncol = 1)
     rownames(AccuracyToObserved.mat) <- as.character(SS)
     colnames(AccuracyToObserved.mat) <- c("Accuracy_to_observed_dataset_diversity")
     sim.local.mat <- matrix(rep(0, length(SS) * length(SS)),
     nrow = length(SS), ncol = length(SS))
     rownames(sim.local.mat) <- as.character(SS)
     colnames(sim.local.mat) <- as.character(SS)
     sim.global.mat <- matrix(rep(0, length(SS) * length(SS)),
     nrow = length(SS), ncol = length(SS))
     rownames(sim.global.mat) <- as.character(SS)
     colnames(sim.global.mat) <- as.character(SS)
     msr.curve.mat <- matrix(nrow = 2, ncol = 1)
     rownames(msr.curve.mat) <- c("local", "global")
     colnames(msr.curve.mat) <- c("MSR_between_subsample_curves")
     sim.local.ref <- matrix(nrow = length(SS) - 1, ncol = 1)
     rownames(sim.local.ref) <- as.character(SS[2:length(SS)])
     colnames(sim.local.ref) <- c("Distance_from_local_reference_curve")
     sim.global.ref <- matrix(nrow = length(SS) - 1, ncol = 1)
     rownames(sim.global.ref) <- as.character(SS[2:length(SS)])
     colnames(sim.global.ref) <- c("Distance_from_global_reference_curve")
     monotonic.local.mat <- matrix(nrow = length(SS), ncol = 1)
     monotonic.global.mat <- matrix(nrow = length(SS), ncol = 1)
     slowing.local.mat <- matrix(nrow = length(SS), ncol = 1)
     slowing.global.mat <- matrix(nrow = length(SS), ncol = 1)
     plausibility.mat <- matrix(nrow = length(SS), ncol = 3)
     rownames(monotonic.local.mat) <- as.character(SS)
     rownames(monotonic.global.mat) <- as.character(SS)
     rownames(slowing.local.mat) <- as.character(SS)
     rownames(slowing.global.mat) <- as.character(SS)
     rownames(plausibility.mat) <- as.character(SS)
     colnames(monotonic.local.mat) <- c("Is monotonic (local)")
     colnames(monotonic.global.mat) <- c("Is monotonic (global)")
     colnames(slowing.local.mat) <- c("Has_decreasing_2nd_derivative (local)")
     colnames(slowing.global.mat) <- c("Has_decreasing_2nd_derivative (global)")
     colnames(plausibility.mat) <- c("Is plausible", "Monotonically increasing",
     "Slowing")
     for (b in 1:length(SS)) {
     cat("Performing fitting routine for sample ", b, "\n")
     if (b > 1) {
     rbind(init.param, fit$par)
     }
     fit <- try(FitSample(model, init.param, lower.bd, upper.bd,
     dSS[[b]], numit, varleft), silent = TRUE)
     if (class(fit) == "try-error") {
     next
     }
     param.mat[b, ] <- fit$par
     ssr.mat[b, ] <- fit$ssr
     ms.mat[b, ] <- fit$ms
     discrep.mat[b, ] <- ModelCostAbs(model, fit$par, dSS[[b]])
     local.mat[b, ] <- model(length(v1), fit$par)
     global.mat[b, ] <- model(tot.pop, fit$par)
     AccuracyToObserved.mat[b, ] <- (model(length(v1), fit$par) -
     length(unique(v1)))/length(unique(v1))
     monotonic.local.mat[b, ] <- IsMonotonic(model, fit$par,
     min.samp = minsampsize, max.samp = length(v1), tot.length = length(v1) -
     minsampsize + 1)
     monotonic.global.mat[b, ] <- IsMonotonic(model, fit$par,
     min.samp = minsampsize, max.samp = tot.pop, tot.length = min(tot.pop -
     minsampsize + 1, 2000))
     slowing.local.mat[b, ] <- IsSlowing(model, fit$par, min.samp = minsampsize,
     max.samp = length(v1), tot.length = length(v1) -
     minsampsize + 1)
     slowing.global.mat[b, ] <- IsSlowing(model, fit$par,
     min.samp = minsampsize, max.samp = tot.pop, tot.length = min(tot.pop -
     minsampsize + 1, 2000))
     plausibility.mat[b, 2] <- ((monotonic.local.mat[b, ]) *
     (monotonic.global.mat[b, ])) == 1
     plausibility.mat[b, 3] <- (slowing.global.mat[b, ]) ==
     1
     plausibility.mat[b, 1] <- (plausibility.mat[b, 2] * plausibility.mat[b,
     3]) == 1
     }
     norm.fac.local <- try(RefArea(model, param.mat[1, ], minsampsize,
     length(v1)), silent = TRUE)
     norm.fac.global <- try(RefArea(model, param.mat[1, ], minsampsize,
     tot.pop), silent = TRUE)
     for (b in 1:length(SS)) {
     for (c in (b:length(SS))) {
     if (b == c) {
     sim.local.mat[b, c] <- 0
     sim.global.mat[b, c] <- 0
     }
     else {
     local.tmp.mat <- try(Simm(model, param.mat[b,
     ], param.mat[c, ], lowerlimit = minsampsize,
     upperlimit = length(v1), tot.length = length(v1) -
     minsampsize + 1), silent = TRUE)
     global.tmp.mat <- try(Simm(model, param.mat[b,
     ], param.mat[c, ], lowerlimit = minsampsize,
     upperlimit = tot.pop, tot.length = tot.pop -
     minsampsize + 1), silent = TRUE)
     if ((class(local.tmp.mat) != "try-error") & (class(global.tmp.mat) !=
     "try-error")) {
     sim.local.mat[b, c] <- local.tmp.mat
     sim.global.mat[b, c] <- global.tmp.mat
     }
     }
     }
     }
     sim.local.mat <- sim.local.mat + t(sim.local.mat)
     sim.global.mat <- sim.global.mat + t(sim.global.mat)
     sim.local.mat.tmp <- try(sim.local.mat/norm.fac.local, silent = TRUE)
     sim.global.mat.tmp <- try(sim.global.mat/norm.fac.global,
     silent = TRUE)
     if ((class(sim.local.mat.tmp) != "try-error") & (class(sim.global.mat.tmp) !=
     "try-error")) {
     sim.local.mat <- sim.local.mat.tmp
     sim.global.mat <- sim.global.mat.tmp
     }
     for (b in 2:length(SS)) {
     sim.local.ref[b - 1, ] <- sim.local.mat[b, 1]
     sim.global.ref[b - 1, ] <- sim.global.mat[b, 1]
     }
     msr.curve.mat[1, 1] <- MSRcurves(sim.local.mat)
     msr.curve.mat[2, 1] <- MSRcurves(sim.global.mat)
     list(param = param.mat, ssr = ssr.mat, ms = ms.mat, discrep = discrep.mat,
     local = local.mat, global = global.mat, AccuracyToObserved = AccuracyToObserved.mat,
     subsamplesizes = SS, datapoints = dSS, modelname = names(model.list),
     numparam = ncol(param.mat), sampvar = msr.curve.mat,
     mono.local = monotonic.local.mat, mono.global = monotonic.global.mat,
     slowing.local = slowing.local.mat, slowing.global = slowing.global.mat,
     plausibility = plausibility.mat, dist.local = sim.local.mat,
     dist.global = sim.global.mat, local.ref.dist = sim.local.ref,
     global.ref.dist = sim.global.ref, popsize = tot.pop,
     themodel = model)
    }
    <bytecode: 0x43497c0>
    <environment: namespace:DivE>
     --- function search by body ---
    Function FitAllSubs in namespace DivE has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if ((class(sim.local.mat.tmp) != "try-error") & (class(sim.global.mat.tmp) != :
     the condition has length > 1
    Calls: DiveMaster -> MultipleScoring -> FitSingleMod -> FitAllSubs
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang