CRAN Package Check Results for Package SFSI

Last updated on 2024-11-30 09:49:28 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	1.4.1	14.53	507.55	522.08	OK
r-devel-linux-x86_64-debian-gcc	1.4.1	10.40	267.68	278.08	OK
r-devel-linux-x86_64-fedora-clang	1.4.1			776.42	OK
r-devel-linux-x86_64-fedora-gcc	1.4.1			831.88	OK
r-devel-windows-x86_64	1.4.1	125.00	539.00	664.00	ERROR
r-patched-linux-x86_64	1.4.1	15.78	420.97	436.75	OK
r-release-linux-x86_64	1.4.1	14.73	425.63	440.36	OK
r-release-macos-arm64	1.4.1			306.00	OK
r-release-macos-x86_64	1.4.1			903.00	OK
r-release-windows-x86_64	1.4.1	124.00	550.00	674.00	ERROR
r-oldrel-macos-arm64	1.4.1			305.00	OK
r-oldrel-macos-x86_64	1.4.1			917.00	OK
r-oldrel-windows-x86_64	1.4.1	135.00	616.00	751.00	ERROR

Check Details

Version: 1.4.1
Check: examples
Result: ERROR Running examples in 'SFSI-Ex.R' failed The error most likely occurred in: > ### Name: Reading and combining SGP outputs > ### Title: Read and combine SGP outputs > ### Aliases: read_SGP read_summary > > ### ** Examples > > require(SFSI) > data(wheatHTP) > > index = which(Y$trial %in% 1:10) # Use only a subset of data > Y = Y[index,] > M = scale(M[index,])/sqrt(ncol(M)) # Subset and scale markers > G = tcrossprod(M) # Genomic relationship matrix > y = as.vector(scale(Y[,"E1"])) # Scale response variable > > # Training and testing sets > tst = which(Y$trial %in% 1:3) > trn = seq_along(y)[-tst] > > path = paste0(tempdir(),"/testSGP_") > > # Run the analysis into 4 subsets and save them at a given path > SGP(y, K=G, trn=trn, tst=tst, subset=c(1,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 1/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_28_01_50_01_4993\RtmpSMmC0N\testSGP_subset_1_of_4_SGP.RData' > SGP(y, K=G, trn=trn, tst=tst, subset=c(2,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 2/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_28_01_50_01_4993\RtmpSMmC0N\testSGP_subset_2_of_4_SGP.RData' > SGP(y, K=G, trn=trn, tst=tst, subset=c(3,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 3/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_28_01_50_01_4993\RtmpSMmC0N\testSGP_subset_3_of_4_SGP.RData' > SGP(y, K=G, trn=trn, tst=tst, subset=c(4,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 4/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_28_01_50_01_4993\RtmpSMmC0N\testSGP_subset_4_of_4_SGP.RData' > > # Collect all results after completion > fm = read_SGP(path) Warning in grep(pattern = paste0(fullpath, "$"), value = TRUE, x = list.files(infolder, : TRE pattern compilation error 'Invalid back reference' Error in grep(pattern = paste0(fullpath, "$"), value = TRUE, x = list.files(infolder, : invalid regular expression 'D:\temp\2024_11_28_01_50_01_4993\RtmpSMmC0N\testSGP_.*SGP.RData$', reason 'Invalid back reference' Calls: read_SGP -> lapply -> FUN -> basename -> grep Execution halted Flavor: r-devel-windows-x86_64

Version: 1.4.1
Check: examples
Result: ERROR Running examples in 'SFSI-Ex.R' failed The error most likely occurred in: > ### Name: Reading and combining SGP outputs > ### Title: Read and combine SGP outputs > ### Aliases: read_SGP read_summary > > ### ** Examples > > require(SFSI) > data(wheatHTP) > > index = which(Y$trial %in% 1:10) # Use only a subset of data > Y = Y[index,] > M = scale(M[index,])/sqrt(ncol(M)) # Subset and scale markers > G = tcrossprod(M) # Genomic relationship matrix > y = as.vector(scale(Y[,"E1"])) # Scale response variable > > # Training and testing sets > tst = which(Y$trial %in% 1:3) > trn = seq_along(y)[-tst] > > path = paste0(tempdir(),"/testSGP_") > > # Run the analysis into 4 subsets and save them at a given path > SGP(y, K=G, trn=trn, tst=tst, subset=c(1,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 1/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_29_01_50_00_24992\RtmpSq4bON\testSGP_subset_1_of_4_SGP.RData' > SGP(y, K=G, trn=trn, tst=tst, subset=c(2,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 2/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_29_01_50_00_24992\RtmpSq4bON\testSGP_subset_2_of_4_SGP.RData' > SGP(y, K=G, trn=trn, tst=tst, subset=c(3,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 3/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_29_01_50_00_24992\RtmpSq4bON\testSGP_subset_3_of_4_SGP.RData' > SGP(y, K=G, trn=trn, tst=tst, subset=c(4,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 4/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_29_01_50_00_24992\RtmpSq4bON\testSGP_subset_4_of_4_SGP.RData' > > # Collect all results after completion > fm = read_SGP(path) Warning in grep(pattern = paste0(fullpath, "$"), value = TRUE, x = list.files(infolder, : TRE pattern compilation error 'Invalid back reference' Error in grep(pattern = paste0(fullpath, "$"), value = TRUE, x = list.files(infolder, : invalid regular expression 'D:\temp\2024_11_29_01_50_00_24992\RtmpSq4bON\testSGP_.*SGP.RData$', reason 'Invalid back reference' Calls: read_SGP -> lapply -> FUN -> basename -> grep Execution halted Flavor: r-release-windows-x86_64

Version: 1.4.1
Check: examples
Result: ERROR Running examples in 'SFSI-Ex.R' failed The error most likely occurred in: > ### Name: Reading and combining SGP outputs > ### Title: Read and combine SGP outputs > ### Aliases: read_SGP read_summary > > ### ** Examples > > require(SFSI) > data(wheatHTP) > > index = which(Y$trial %in% 1:10) # Use only a subset of data > Y = Y[index,] > M = scale(M[index,])/sqrt(ncol(M)) # Subset and scale markers > G = tcrossprod(M) # Genomic relationship matrix > y = as.vector(scale(Y[,"E1"])) # Scale response variable > > # Training and testing sets > tst = which(Y$trial %in% 1:3) > trn = seq_along(y)[-tst] > > path = paste0(tempdir(),"/testSGP_") > > # Run the analysis into 4 subsets and save them at a given path > SGP(y, K=G, trn=trn, tst=tst, subset=c(1,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 1/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_29_01_50_01_24992\RtmpmChn56\testSGP_subset_1_of_4_SGP.RData' > SGP(y, K=G, trn=trn, tst=tst, subset=c(2,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 2/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_29_01_50_01_24992\RtmpmChn56\testSGP_subset_2_of_4_SGP.RData' > SGP(y, K=G, trn=trn, tst=tst, subset=c(3,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 3/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_29_01_50_01_24992\RtmpmChn56\testSGP_subset_3_of_4_SGP.RData' > SGP(y, K=G, trn=trn, tst=tst, subset=c(4,4), save.at=path) Parameter estimation from a LMM within training data (nTRN = 194) Variance components: varU varE 1.5145659 0.1149247 Fixed effects: (Intercept) 0.0009088514 Fitting a SGP model using nTST = 21 (subset 4/4) of 84 and nTRN = 194 records | | | 0% | |=== | 5% | |======= | 10% | |========== | 14% | |============= | 19% | |================= | 24% | |==================== | 29% | |======================= | 33% | |=========================== | 38% | |============================== | 43% | |================================= | 48% | |===================================== | 52% | |======================================== | 57% | |=========================================== | 62% | |=============================================== | 67% | |================================================== | 71% | |===================================================== | 76% | |========================================================= | 81% | |============================================================ | 86% | |=============================================================== | 90% | |=================================================================== | 95% | |======================================================================| 100% Results were saved at file: 'D:\temp\2024_11_29_01_50_01_24992\RtmpmChn56\testSGP_subset_4_of_4_SGP.RData' > > # Collect all results after completion > fm = read_SGP(path) Warning in grep(pattern = paste0(fullpath, "$"), value = TRUE, x = list.files(infolder, : TRE pattern compilation error 'Invalid back reference' Error in grep(pattern = paste0(fullpath, "$"), value = TRUE, x = list.files(infolder, : invalid regular expression 'D:\temp\2024_11_29_01_50_01_24992\RtmpmChn56\testSGP_.*SGP.RData$', reason 'Invalid back reference' Calls: read_SGP -> lapply -> FUN -> basename -> grep Execution halted Flavor: r-oldrel-windows-x86_64