CRAN Package Check Results for Package SSP

Last updated on 2023-10-01 07:52:46 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0.1 10.42 146.07 156.49 OK
r-devel-linux-x86_64-debian-gcc 1.0.1 10.18 84.89 95.07 ERROR
r-devel-linux-x86_64-fedora-clang 1.0.1 160.45 ERROR
r-devel-linux-x86_64-fedora-gcc 1.0.1 169.77 ERROR
r-devel-windows-x86_64 1.0.1 10.00 97.00 107.00 ERROR
r-patched-linux-x86_64 1.0.1 16.60 136.41 153.01 OK
r-release-linux-x86_64 1.0.1 11.29 139.09 150.38 OK
r-release-macos-arm64 1.0.1 60.00 OK
r-release-macos-x86_64 1.0.1 94.00 OK
r-release-windows-x86_64 1.0.1 16.00 147.00 163.00 OK
r-oldrel-macos-arm64 1.0.1 52.00 OK
r-oldrel-macos-x86_64 1.0.1 96.00 OK
r-oldrel-windows-x86_64 1.0.1 15.00 147.00 162.00 OK

Check Details

Version: 1.0.1
Check: examples
Result: ERROR
    Running examples in ‘SSP-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: SSP-package
    > ### Title: SSP: Simulated Sampling Procedure for Community Ecology
    > ### Aliases: SSP
    >
    > ### ** Examples
    >
    > ###To speed up the simulation of these examples, the cases, sites and N were set small.
    >
    > ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico)
    > data(micromollusk)
    >
    > #Estimation of parameters of pilot data
    > par.mic<-assempar (data = micromollusk,
    + type= "P/A",
    + Sest.method = "average")
    >
    > #Simulation of 3 data sets, each one with 20 potential sampling units from a single site
    > sim.mic<-simdata(par.mic, cases= 3, N = 20, sites = 1)
    >
    > #Sampling and estimation of MultSE for each sample size (few repetitions
    > #to speed up the example)
    >
    > sam.mic<-sampsd(dat.sim = sim.mic,
    + Par = par.mic,
    + transformation = "P/A",
    + method = "jaccard",
    + n = 10,
    + m = 1,
    + k = 3)
    >
    > #Summary of MultSE for each sampling effort
    > summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE)
    >
    > #Cut-off points to identify optimal sampling effort
    > opt.mic<-ioptimum(xx = summ.mic, multi.site = FALSE)
    Warning: The required optimization (c3) was not achieved, instead, the maximum effort generated with sampsd is presented. Increase the 'n' value in 'sampsd' to achieve the specified optimization or relax the optimization in argument c3
    >
    > #Plot
    > plot_ssp(xx = summ.mic, opt = opt.mic, multi.site = FALSE)
    >
    > ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico).
    > data(sponges)
    >
    > #Estimation of parameters of pilot data
    > par.spo<-assempar(data = sponges,
    + type= "counts",
    + Sest.method = "average")
    >
    > #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites.
    > sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3)
    >
    > #Sampling and estimation of MultSE for each sampling design (few repetitions
    > #to speed up the example)
    >
    > sam.spo<-sampsd(dat.sim = sim.spo,
    + Par = par.spo,
    + transformation = "square root",
    + method = "bray",
    + n = 10,
    + m = 3,
    + k = 3)
    >
    > #Summary of MultSE for each sampling effort
    > summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE)
    >
    > #Cut-off points to identify optimal sampling effort
    > opt.spo<-ioptimum(xx = summ.spo, multi.site = TRUE)
    Warning: Sites: the required optimizations were not achieved, instead, the maximum number of sites sampled with 'sampsd' is presented. Increase the 'm' value in 'sampsd' to achieve the specified optimization or relax the optimization values in arguments c1, c2 and c3
    Warning: Sites: the required optimizations (c2, c3) were not achieved, instead, the maximum number of sites sampled with 'sampsd' is presented. Increase the 'm' value in 'sampsd' to achieve the specified optimization values or relax the optimization in arguments c2 and c3
    Warning: Sites: the required optimizations (c3) was not achieved, instead, the maximum number of sites sampled with 'sampsd' is presented. Increase the 'm' value in 'sampsd' to achieve the specified optimization or relax the optimization in argument c3
    >
    > #Plot
    > plot_ssp(xx = summ.spo, opt = opt.spo, multi.site = TRUE)
    Error in `FUN()`:
    ! Aesthetic must be a one-sided formula, call, name, or constant.
    Backtrace:
     ▆
     1. └─SSP::plot_ssp(xx = summ.spo, opt = opt.spo, multi.site = TRUE)
     2. ├─ggplot2::geom_rect(...)
     3. │ └─ggplot2::layer(...)
     4. └─ggplot2::aes_(...)
     5. └─base::lapply(mapping, as_quosure_aes)
     6. └─ggplot2 (local) FUN(X[[i]], ...)
     7. └─cli::cli_abort("Aesthetic must be a one-sided formula, call, name, or constant.")
     8. └─rlang::abort(...)
    Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0.1
Check: examples
Result: ERROR
    Running examples in ‘SSP-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: SSP-package
    > ### Title: SSP: Simulated Sampling Procedure for Community Ecology
    > ### Aliases: SSP
    >
    > ### ** Examples
    >
    > ###To speed up the simulation of these examples, the cases, sites and N were set small.
    >
    > ##Single site: micromollusk from Cayo Nuevo (Yucatan, Mexico)
    > data(micromollusk)
    >
    > #Estimation of parameters of pilot data
    > par.mic<-assempar (data = micromollusk,
    + type= "P/A",
    + Sest.method = "average")
    >
    > #Simulation of 3 data sets, each one with 20 potential sampling units from a single site
    > sim.mic<-simdata(par.mic, cases= 3, N = 20, sites = 1)
    >
    > #Sampling and estimation of MultSE for each sample size (few repetitions
    > #to speed up the example)
    >
    > sam.mic<-sampsd(dat.sim = sim.mic,
    + Par = par.mic,
    + transformation = "P/A",
    + method = "jaccard",
    + n = 10,
    + m = 1,
    + k = 3)
    >
    > #Summary of MultSE for each sampling effort
    > summ.mic<-summary_ssp(results = sam.mic, multi.site = FALSE)
    >
    > #Cut-off points to identify optimal sampling effort
    > opt.mic<-ioptimum(xx = summ.mic, multi.site = FALSE)
    Warning: The required optimization (c3) was not achieved, instead, the maximum effort generated with sampsd is presented. Increase the 'n' value in 'sampsd' to achieve the specified optimization or relax the optimization in argument c3
    >
    > #Plot
    > plot_ssp(xx = summ.mic, opt = opt.mic, multi.site = FALSE)
    >
    > ##Multiple sites: Sponges from Alacranes National Park (Yucatan, Mexico).
    > data(sponges)
    >
    > #Estimation of parameters of pilot data
    > par.spo<-assempar(data = sponges,
    + type= "counts",
    + Sest.method = "average")
    >
    > #Simulation of 3 data sets, each one with 10 potential sampling units in 3 sites.
    > sim.spo<-simdata(par.spo, cases= 3, N = 10, sites = 3)
    >
    > #Sampling and estimation of MultSE for each sampling design (few repetitions
    > #to speed up the example)
    >
    > sam.spo<-sampsd(dat.sim = sim.spo,
    + Par = par.spo,
    + transformation = "square root",
    + method = "bray",
    + n = 10,
    + m = 3,
    + k = 3)
    >
    > #Summary of MultSE for each sampling effort
    > summ.spo<-summary_ssp(results = sam.spo, multi.site = TRUE)
    >
    > #Cut-off points to identify optimal sampling effort
    > opt.spo<-ioptimum(xx = summ.spo, multi.site = TRUE)
    Warning: Sites: the required optimizations were not achieved, instead, the maximum number of sites sampled with 'sampsd' is presented. Increase the 'm' value in 'sampsd' to achieve the specified optimization or relax the optimization values in arguments c1, c2 and c3
    Warning: Sites: the required optimizations (c2, c3) were not achieved, instead, the maximum number of sites sampled with 'sampsd' is presented. Increase the 'm' value in 'sampsd' to achieve the specified optimization values or relax the optimization in arguments c2 and c3
    Warning: Sites: the required optimizations (c3) was not achieved, instead, the maximum number of sites sampled with 'sampsd' is presented. Increase the 'm' value in 'sampsd' to achieve the specified optimization or relax the optimization in argument c3
    >
    > #Plot
    > plot_ssp(xx = summ.spo, opt = opt.spo, multi.site = TRUE)
    Error in `FUN()`:
    ! Aesthetic must be a one-sided formula, call, name, or constant.
    Backtrace:
     ▆
     1. └─SSP::plot_ssp(xx = summ.spo, opt = opt.spo, multi.site = TRUE)
     2. ├─ggplot2::geom_rect(...)
     3. │ └─ggplot2::layer(...)
     4. └─ggplot2::aes_(...)
     5. └─base::lapply(mapping, as_quosure_aes)
     6. └─ggplot2 (local) FUN(X[[i]], ...)
     7. └─cli::cli_abort("Aesthetic must be a one-sided formula, call, name, or constant.")
     8. └─rlang::abort(...)
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64