• using R version 4.3.3 (2024-02-29 ucrt)
• using platform: x86_64-w64-mingw32 (64-bit)
• R was compiled by     gcc.exe (GCC) 12.3.0     GNU Fortran (GCC) 12.3.0
• running under: Windows Server 2022 x64 (build 20348)
• using session charset: UTF-8
• checking for file 'SFSI/DESCRIPTION' ... OK
• this is package 'SFSI' version '1.4.1'
• package encoding: UTF-8
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• checking whether package 'SFSI' can be installed ... OK See the install log for details.
• used C compiler: 'gcc.exe (GCC) 12.3.0'
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• checking DESCRIPTION meta-information ... OK
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• checking examples ... [23s] 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_12_20_01_50_00_19051\Rtmp2vdAVO\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%   | 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 (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_12_20_01_50_00_19051\Rtmp2vdAVO\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%   | 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:   TRE pattern compilation error 'Invalid back reference' Error in grep(pattern = paste0(fullpath, "$"), value = TRUE, x = list.files(infolder, :   invalid regular expression 'D:\temp\2024_12_20_01_50_00_19051\Rtmp2vdAVO\testSGP_.*SGP.RData$', reason 'Invalid back reference' Calls: read_SGP -> lapply -> FUN -> basename -> grep Execution halted
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes in 'inst/doc' ... OK
• checking re-building of vignette outputs ... [8s] OK
• checking PDF version of manual ... [18s] OK
• checking HTML version of manual ... [5s] OK
• DONE

Status: 1 ERROR