Last updated on 2023-09-26 14:05:02 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.1 | 5.34 | 39.26 | 44.60 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.1 | 3.48 | 30.18 | 33.66 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.1 | 57.99 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.1 | 56.32 | ERROR | |||
| r-devel-windows-x86_64 | 1.1 | 5.00 | 56.00 | 61.00 | ERROR | |
| r-patched-linux-x86_64 | 1.1 | 5.60 | 40.80 | 46.40 | OK | |
| r-release-linux-x86_64 | 1.1 | 3.13 | 40.26 | 43.39 | OK | |
| r-release-macos-arm64 | 1.1 | 23.00 | NOTE | |||
| r-release-macos-x86_64 | 1.1 | 32.00 | NOTE | |||
| r-release-windows-x86_64 | 1.1 | 7.00 | 63.00 | 70.00 | NOTE | |
| r-oldrel-macos-arm64 | 1.1 | 22.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 1.1 | 39.00 | NOTE | |||
| r-oldrel-windows-x86_64 | 1.1 | 6.00 | 58.00 | 64.00 | NOTE |
Version: 1.1
Check: examples
Result: ERROR
Running examples in ‘momr-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: momr-package
> ### Title: Mining Metaomics Data In R
> ### Aliases: momr-package momr
> ### Keywords: package shotgun metagenomics MGS MetaGenomicSpecies data
> ### mining biomarker selection metagenomics
>
> ### ** Examples
>
>
> # load the package
> library(momr)
>
> #' all the data in the package
> # data(package="momr")
>
> #' load the raw and frequency test dataset
> data("hs_3.3_metahit_sample_dat_raw")
> data("hs_3.3_metahit_sample_dat_freq")
>
>
> #' NORMALIZATION
> #' This should be performed with the whole dataset (complete catalogue).
> #' But here is an exemple with the subset of the data for illustration purposes
> data(hs_3.3_metahit_genesize)
> norm.data <- normFreqRPKM(dat=hs_3.3_metahit_sample_dat_raw, cat=hs_3.3_metahit_genesize)
[1] "The dataset is a matrix"
>
>
> #' CLUSTERING OF SAMPLES
> hc.data <- hierClust(data=hs_3.3_metahit_sample_dat_freq[,1:5], side="col")
The "ward" method has been renamed to "ward.D"; note new "ward.D2"
> clust.order <- hc.data$mat.hclust$order
> #' order samples followin the hierarchical clustering
> ordered.samples <- colnames(hs_3.3_metahit_sample_dat_freq[,1:5])[clust.order]
> #' how close are the two first samples (spearman, rho)
> hc.data$mat.rho[ordered.samples[1], ordered.samples[2]]
[1] 0.1038056
> # select the samples closely related together
> close.samples <- filt.hierClust(hc.data$mat.rho, hclust.method = "ward", plot = TRUE, filt = 0.5)
Warning in filt.hierClust(hc.data$mat.rho, hclust.method = "ward", plot = TRUE, :
There are less than 5 samples. Setting size to the number of rows
Warning in filt.hierClust(hc.data$mat.rho, hclust.method = "ward", plot = TRUE, :
There are no related samples above the threshold 0.5
>
> #' CLUSTER GENES ON THE MGS CATALOG
> #' load the curated mgs data for the hs_3.3_metahit catalog
> data("mgs_hs_3.3_metahit_sup500")
>
> #' project a list of genes onto the mgs
> genebag <- rownames(hs_3.3_metahit_sample_dat_freq)
> mgs <- projectOntoMGS(genebag=genebag, list.mgs=mgs_hs_3.3_metahit_sup500)
>
> #' extract the profile of a list of genes from the whole dataset
> mgs.dat <- extractProfiles(mgs, hs_3.3_metahit_sample_dat_freq, silent=FALSE)
[1] "Multiple profile extraction"
>
> #' plot the barcodes
> par(mfrow=c(5,1), mar=c(1,0,0,0))
> for(i in 1:5){
+ plotBarcode(mgs.dat[[i]])
+ }
>
> #' compute the filtered vectors
> mgs.mean.vect <- computeFilteredVectors(profile=mgs.dat, type="mean")
>
>
> #' TEST RELATIONS
> #' for the first 1000 genes
> res.test <- testRelations(data=hs_3.3_metahit_sample_dat_freq[1:500,],
+ trait=c(rep(1,150),rep(2,142)),type="wilcoxon")
Error in wilcox.test.formula(data[i, restrict] ~ trait[restrict], paired = paired) :
cannot use 'paired' in formula method
Calls: testRelations -> wilcox.test -> wilcox.test.formula
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 1.1
Check: installed package size
Result: NOTE
installed size is 7.4Mb
sub-directories of 1Mb or more:
data 2.1Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Version: 1.1
Check: examples
Result: ERROR
Running examples in ‘momr-Ex.R’ failed
The error most likely occurred in:
> ### Name: momr-package
> ### Title: Mining Metaomics Data In R
> ### Aliases: momr-package momr
> ### Keywords: package shotgun metagenomics MGS MetaGenomicSpecies data
> ### mining biomarker selection metagenomics
>
> ### ** Examples
>
>
> # load the package
> library(momr)
>
> #' all the data in the package
> # data(package="momr")
>
> #' load the raw and frequency test dataset
> data("hs_3.3_metahit_sample_dat_raw")
> data("hs_3.3_metahit_sample_dat_freq")
>
>
> #' NORMALIZATION
> #' This should be performed with the whole dataset (complete catalogue).
> #' But here is an exemple with the subset of the data for illustration purposes
> data(hs_3.3_metahit_genesize)
> norm.data <- normFreqRPKM(dat=hs_3.3_metahit_sample_dat_raw, cat=hs_3.3_metahit_genesize)
[1] "The dataset is a matrix"
>
>
> #' CLUSTERING OF SAMPLES
> hc.data <- hierClust(data=hs_3.3_metahit_sample_dat_freq[,1:5], side="col")
The "ward" method has been renamed to "ward.D"; note new "ward.D2"
> clust.order <- hc.data$mat.hclust$order
> #' order samples followin the hierarchical clustering
> ordered.samples <- colnames(hs_3.3_metahit_sample_dat_freq[,1:5])[clust.order]
> #' how close are the two first samples (spearman, rho)
> hc.data$mat.rho[ordered.samples[1], ordered.samples[2]]
[1] 0.1038056
> # select the samples closely related together
> close.samples <- filt.hierClust(hc.data$mat.rho, hclust.method = "ward", plot = TRUE, filt = 0.5)
Warning in filt.hierClust(hc.data$mat.rho, hclust.method = "ward", plot = TRUE, :
There are less than 5 samples. Setting size to the number of rows
Warning in filt.hierClust(hc.data$mat.rho, hclust.method = "ward", plot = TRUE, :
There are no related samples above the threshold 0.5
>
> #' CLUSTER GENES ON THE MGS CATALOG
> #' load the curated mgs data for the hs_3.3_metahit catalog
> data("mgs_hs_3.3_metahit_sup500")
>
> #' project a list of genes onto the mgs
> genebag <- rownames(hs_3.3_metahit_sample_dat_freq)
> mgs <- projectOntoMGS(genebag=genebag, list.mgs=mgs_hs_3.3_metahit_sup500)
>
> #' extract the profile of a list of genes from the whole dataset
> mgs.dat <- extractProfiles(mgs, hs_3.3_metahit_sample_dat_freq, silent=FALSE)
[1] "Multiple profile extraction"
>
> #' plot the barcodes
> par(mfrow=c(5,1), mar=c(1,0,0,0))
> for(i in 1:5){
+ plotBarcode(mgs.dat[[i]])
+ }
>
> #' compute the filtered vectors
> mgs.mean.vect <- computeFilteredVectors(profile=mgs.dat, type="mean")
>
>
> #' TEST RELATIONS
> #' for the first 1000 genes
> res.test <- testRelations(data=hs_3.3_metahit_sample_dat_freq[1:500,],
+ trait=c(rep(1,150),rep(2,142)),type="wilcoxon")
Error in wilcox.test.formula(data[i, restrict] ~ trait[restrict], paired = paired) :
cannot use 'paired' in formula method
Calls: testRelations -> wilcox.test -> wilcox.test.formula
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64