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Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > library(ASRgenomics) > > test_check("ASRgenomics") Initial data: Number of Individuals: 247 Number of Markers: 50 Missing data check: Total SNPs: 50 0 SNPs dropped due to missing data threshold of 0.5 Total of: 50 SNPs MAF check: No SNPs with MAF below 0 Heterozigosity data check: No SNPs with heterozygosity, missing threshold of = 0 Summary check: Initial: 50 SNPs Final: 50 SNPs ( 0 SNPs removed) Completed! Time = 0.01 seconds Reciprocal conditional number for original matrix is: 0 Reciprocal conditional number of G is: 0, which is lower than the threshold of 1e-12 G seems to be an ill-conditioned matrix. Reciprocal conditional number for original matrix is: 0 Reciprocal conditional number of G is: 0, which is lower than the threshold of 1e-12 G seems to be an ill-conditioned matrix. Reciprocal conditional number for inverted matrix is: 0.0010679765264986 Inverse of matrix G does not appear to be ill-conditioned. Reciprocal conditional number for original matrix is: 0 Reciprocal conditional number of G is: 0, which is lower than the threshold of 1e-12 G seems to be an ill-conditioned matrix. Reciprocal conditional number for inverted matrix is: 0.0010679765264986 Inverse of matrix G does not appear to be ill-conditioned. Reciprocal conditional number for original matrix is: 0 Reciprocal conditional number of G is: 0, which is lower than the threshold of 1e-12 G seems to be an ill-conditioned matrix. Reciprocal conditional number for inverted matrix is: 0.0010679765264986 Inverse of matrix G does not appear to be ill-conditioned. Reciprocal conditional number for original matrix is: 0 Reciprocal conditional number of G is: 0, which is lower than the threshold of 1e-12 G seems to be an ill-conditioned matrix. Reciprocal conditional number for inverted matrix is: 0.0010679765264986 Inverse of matrix G does not appear to be ill-conditioned. Initial data: Number of Individuals: 20 Number of Markers: 50 Missing data check: Total SNPs: 50 0 SNPs dropped due to missing data threshold of 0.5 Total of: 50 SNPs MAF check: No SNPs with MAF below 0 Heterozigosity data check: No SNPs with heterozygosity, missing threshold of = 0 Summary check: Initial: 50 SNPs Final: 50 SNPs ( 0 SNPs removed) Completed! Time = 0 seconds Initial data: Number of Individuals: 20 Number of Markers: 10 Missing data check: Total SNPs: 10 0 SNPs dropped due to missing data threshold of 0.5 Total of: 10 SNPs MAF check: No SNPs with MAF below 0 Heterozigosity data check: No SNPs with heterozygosity, missing threshold of = 0 Summary check: Initial: 10 SNPs Final: 10 SNPs ( 0 SNPs removed) Completed! Time = 0 seconds Reciprocal conditional number for original matrix is: 0 Determinant for original matrix is: 0 Matrix was BLENDED using an identity matrix. Reciprocal conditional number for tune-up matrix is: 4.65251000480448e-08 Initial data: Number of Individuals: 20 Number of Markers: 50 Missing data check: Total SNPs: 50 0 SNPs dropped due to missing data threshold of 0.5 Total of: 50 SNPs MAF check: No SNPs with MAF below 0 Heterozigosity data check: No SNPs with heterozygosity, missing threshold of = 0 Summary check: Initial: 50 SNPs Final: 50 SNPs ( 0 SNPs removed) Completed! Time = 0 seconds Reciprocal conditional number for original matrix is: 0.635294117647059 Determinant for original matrix is: 0.9375 Matrix was BLENDED using an identity matrix. Reciprocal conditional number for tune-up matrix is: 0.665950590762621 Reciprocal conditional number for original matrix is: 2.24022977149314e-10 Determinant for original matrix is: -1.76706403547069e-24 Matrix was BENDED. Reciprocal conditional number for tune-up matrix is: 0.00368868521743848 Reciprocal conditional number for original matrix is: 0.635294117647059 Determinant for original matrix is: 0.9375 Matrix was ALIGNED. Reciprocal conditional number for tune-up matrix is: 0.400315680151888 Reciprocal conditional number for original matrix is: 0.635294117647059 Determinant for original matrix is: 0.9375 Matrix was BLENDED using the provided A matrix. Reciprocal conditional number for tune-up matrix is: 0.628638260654523 Reciprocal conditional number for original matrix is: 0.635294117647059 Determinant for original matrix is: 0.9375 Matrix was BLENDED using the provided A matrix. Reciprocal conditional number for tune-up matrix is: 0.628638260654523 Reciprocal conditional number for original matrix is: 0.635294117647059 Determinant for original matrix is: 0.9375 Reciprocal conditional number for original matrix is: 0.635294117647059 Determinant for original matrix is: 0.9375 Matrix was ALIGNED. Reciprocal conditional number for tune-up matrix is: 0.27300209666922 Reciprocal conditional number for original matrix is: 0.635294117647059 Determinant for original matrix is: 0.9375 Reciprocal conditional number for original matrix is: 1 Determinant is not calculated as matrix is too large. Matrix was BLENDED using an identity matrix. Reciprocal conditional number for tune-up matrix is: 1 Reciprocal conditional number for original matrix is: 1 Determinant for original matrix is: Inf Matrix was BLENDED using an identity matrix. Reciprocal conditional number for tune-up matrix is: 1 Reciprocal conditional number for original matrix is: 0.635294117647059 Determinant for original matrix is: 0.9375 Reciprocal conditional number for original matrix is: 0 Reciprocal conditional number of G is: 0, which is lower than the threshold of 1e-12 G seems to be an ill-conditioned matrix. Reciprocal conditional number for inverted matrix is: 0.00748567238719749 Inverse of matrix G does not appear to be ill-conditioned. Reciprocal conditional number for original matrix is: 0.224489795918367 Reciprocal conditional number for inverted matrix is: 0.224489794715743 Inverse of matrix G does not appear to be ill-conditioned. A lambda value was provided and it will be used instead of tau and omega. A lambda value was provided and it will be used instead of tau and omega. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. No lambda value was provided, tau and omega scaling factors will be considered. A lambda value was provided and it will be used instead of tau and omega. Reciprocal conditional number for original matrix is: 0.635294117647059 Reciprocal conditional number for inverted matrix is: 0.599999997 Inverse of matrix G does not appear to be ill-conditioned. A lambda value was provided and it will be used instead of tau and omega. Creating dummy map. Matrix dimension is: 5x5 Range diagonal values: 0.8747 to 1.05366 Mean diagonal values: 0.94315 Range off-diagonal values: -0.11524 to 0.9 Mean off-diagonal values: 0.20953 No individuals filtered out by the diagonal thresholds, as none were found. No individuals filtered by duplicate.thr = 0.99, as none were found. There are 0 extreme diagonal values, outside < 0.7 and > 1.3 There are 0 records of possible duplicates, based on: k(i,j)/sqrt[k(i,i)*k(j,j)] > 0.99 Matrix dimension is: 5x5 Range diagonal values: 0.8747 to 1.05366 Mean diagonal values: 0.94315 Range off-diagonal values: -0.11524 to 0.9 Mean off-diagonal values: 0.20953 No individuals filtered out by the diagonal thresholds, as none were found. No individuals filtered by duplicate.thr = 0.99, as none were found. There are 0 extreme diagonal values, outside < 0.7 and > 1.3 There are 0 records of possible duplicates, based on: k(i,j)/sqrt[k(i,i)*k(j,j)] > 0.99 Matrix dimension is: 5x5 Range diagonal values: 0.8747 to 1.05366 Mean diagonal values: 0.94315 Range off-diagonal values: -0.11524 to 0.9 Mean off-diagonal values: 0.20953 No individuals filtered out by the diagonal thresholds, as none were found. No individuals filtered by duplicate.thr = 0.99, as none were found. There are 0 extreme diagonal values, outside < 0.7 and > 1.3 There are 0 records of possible duplicates, based on: k(i,j)/sqrt[k(i,i)*k(j,j)] > 0.99 Matrix dimension is: 5x5 Range diagonal values: 0.8747 to 1.05366 Mean diagonal values: 0.94315 Range off-diagonal values: -0.11524 to 0.9 Mean off-diagonal values: 0.20953 There are 1 extreme diagonal values, outside < 0.9 and > 1.3 There are 1 records of possible duplicates, based on: k(i,j)/sqrt[k(i,i)*k(j,j)] > 0.8 Matrix dimension is: 5x5 Range diagonal values: 0.8747 to 1.05366 Mean diagonal values: 0.94315 Range off-diagonal values: -0.11524 to 0.9 Mean off-diagonal values: 0.20953 There are 1 extreme diagonal values, outside < 0.9 and > 1.3 There are 1 records of possible duplicates, based on: k(i,j)/sqrt[k(i,i)*k(j,j)] > 0.8 Matrix dimension is: 5x5 Range diagonal values: 0.8747 to 1.05366 Mean diagonal values: 0.94315 Range off-diagonal values: -0.11524 to 0.9 Mean off-diagonal values: 0.20953 There are 1 extreme diagonal values, outside < 0.9 and > 1.3 There are 1 records of possible duplicates, based on: k(i,j)/sqrt[k(i,i)*k(j,j)] > 0.8 Initial data: Number of Individuals: 20 Number of Markers: 50 Missing data check: Total SNPs: 50 0 SNPs dropped due to missing data threshold of 0.5 Total of: 50 SNPs MAF check: No SNPs with MAF below 0 Heterozigosity data check: No SNPs with heterozygosity, missing threshold of = 0 Summary check: Initial: 50 SNPs Final: 50 SNPs ( 0 SNPs removed) Completed! Time = 0 seconds All 4 individuals from matrix A match those individuals from matrix G. All 4 individuals from matrix G match those individuals from matrix A. All 4 individuals from matrix A match those individuals from matrix G. All 4 individuals from matrix G match those individuals from matrix A. All 2 individuals from matrix G match those individuals from matrix A. Matrix A has 2 individuals (out of 4) NOT present on matrix G. Matrix G has 1 individuals (out of 4) NOT present on matrix A. Order of individual names from matched matrices A and G DO NOT agree. Matrix G has 1 individuals (out of 4) NOT present on matrix A. All 2 individuals from matrix A match those individuals from matrix G. Matrix G has 2 individuals (out of 4) NOT present on matrix A. All 4 individuals from matrix A match those individuals from matrix G. All 4 individuals from matrix G match those individuals from matrix A. All individuals within the kinship matrix match the phenotyped individuals. All phenotyped individuals match the individuals within the kinship matrix. Kinship matrix contains 1 individuals that DO NOT match the phenotyped individuals. Kinship matrix contains 4 individuals that match the phenotyped individuals. All phenotyped individuals match the individuals within the kinship matrix. Individuals within the kinship matrix that do not match those in the phenotypic data will be removed from this matrix. All individuals within the kinship matrix match the phenotyped individuals. Phenotypic data contains 1 individuals that DO NOT match the kinship matrix individuals. Phenotypic data contains 4 individuals that match the kinship matrix individuals. All individuals within the kinship matrix match the phenotyped individuals. Phenotypic data contains 1 individuals that DO NOT match the kinship matrix individuals. Phenotypic data contains 4 individuals that match the kinship matrix individuals. All individuals within the kinship matrix match the phenotyped individuals. All phenotyped individuals match the individuals within the kinship matrix. Initial marker matrix M contains 5 individuals and 6 markers. A total of 9 values were identified as missing with the string -9 and were replaced by NA. A total of 1 markers were removed for only having missing data. A total of 1 markers were removed because their proportion of missing values was equal or larger than 0.39. A total of 1 individuals were removed because their proportion of missing values was equal or larger than 0.49. A total of 1 markers were removed because their MAF was smaller than 0.26. A total of 1 markers were removed because their heterozygosity was larger than 0.99999. A total of 1 markers were removed because their |F| was larger than 0.45. Final cleaned marker matrix M contains 0% of missing SNPs. Final cleaned marker matrix M contains 4 individuals and 1 markers. No imputation was performed as there are no missing marker data. Initial marker matrix M contains 5 individuals and 6 markers. A total of 9 values were identified as missing with the string -9 and were replaced by NA. A total of 1 markers were removed for only having missing data. A total of 0 markers were removed because their proportion of missing values was equal or larger than 1. A total of 0 individuals were removed because their proportion of missing values was equal or larger than 1. A total of 0 markers were removed because their MAF was smaller than 0. A total of 0 markers were removed because their heterozygosity was larger than 1. A total of 0 markers were removed because their |F| was larger than 1. Final cleaned marker matrix M contains 16% of missing SNPs. Final cleaned marker matrix M contains 5 individuals and 5 markers. A total of 4 missing values were imputed, corresponding to 16% of the total number of SNPs. Matrix M was recoded from bi-allelic nucleotide bases to numeric. A total of 0 values were identified as missing with the string nil and were replaced by NA. Matrix M was recoded from bi-allelic nucleotide bases to numeric. Initial marker matrix M contains 655 individuals and 4853 markers. A total of 119670 values were identified as missing with the string -9 and were replaced by NA. A total of 156 markers were removed because their proportion of missing values was equal or larger than 0.2. A total of 11 individuals were removed because their proportion of missing values was equal or larger than 0.2. A total of 1626 markers were removed because their MAF was smaller than 0.05. A total of 21 markers were removed because their heterozygosity was larger than 0.98. A total of 0 markers were removed because their |F| was larger than 1. Final cleaned marker matrix M contains 2.37% of missing SNPs. Final cleaned marker matrix M contains 644 individuals and 3050 markers. Creating dummy map. Creating dummy map. Initiating pruning procedure. Initial marker matrix M contains 644 individuals and 3050 markers. Requesting pruning without chromosome indexing. Iteration: 1 Iteration: 2 Iteration: 3 Final pruned marker matrix M contains 644 individuals and 2954 markers. A total of 96 markers were pruned. Range of minor allele frequency after pruning: 0.05 ~ 0.5 Range of marker call rate after pruning: 80.28 ~ 100 Range of individual call rate after pruning: 81.58 ~ 99.19 Creating dummy map. Initiating pruning procedure. Initial marker matrix M contains 644 individuals and 3050 markers. Requesting pruning without chromosome indexing. Iteration: 1 Iteration: 2 Iteration: 3 Iteration: 4 Final pruned marker matrix M contains 644 individuals and 2956 markers. A total of 94 markers were pruned. Range of minor allele frequency after pruning: 0.05 ~ 0.5 Range of marker call rate after pruning: 80.28 ~ 100 Range of individual call rate after pruning: 81.39 ~ 99.19 Initiating pruning procedure. Initial marker matrix M contains 644 individuals and 3050 markers. Requesting pruning by chromosome. Chromosome: 1 Iteration: 1 Iteration: 2 Iteration: 3 Chromosome: 2 Iteration: 1 Iteration: 2 Iteration: 3 Chromosome: 3 Iteration: 1 Iteration: 2 Iteration: 3 Iteration: 4 Final pruned marker matrix M contains 644 individuals and 2957 markers. A total of 93 markers were pruned. A total of 965 markers were kept in chromosome 1. A total of 966 markers were kept in chromosome 2. A total of 1026 markers were kept in chromosome 3. A total of 35 markers were pruned from chromosome 1. A total of 34 markers were pruned from chromosome 2. A total of 24 markers were pruned from chromosome 3. Range of minor allele frequency after pruning: 0.05 ~ 0.5 Range of marker call rate after pruning: 80.28 ~ 100 Range of individual call rate after pruning: 81.6 ~ 99.19 Creating dummy map. Creating dummy map. Creating dummy map. Creating dummy map. Creating dummy map. Exact method selected. Eliminating markers containing one or more heterozygotic read. Total number of dropped markers: 13 Total number of remaining markers: 2 Generating in hypothetical crosses genotypic information. Generating in hypothetical crosses genotypic information. Generating in hypothetical crosses genotypic information. Generating in hypothetical crosses genotypic information. Exact method selected. Eliminating markers containing one or more heterozygotic read. Total number of dropped markers: 13 Total number of remaining markers: 0 Generating in hypothetical crosses genotypic information. A total of 1 duplicated rows were found in the supplied pedigree. Removing 'ped' lines: 6. Generating in hypothetical crosses genotypic information. [ FAIL 0 | WARN 0 | SKIP 0 | PASS 263 ] > > proc.time() user system elapsed 19.00 1.01 20.01