readme.aireml
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| readme.aireml [2013/07/17 16:19] – shogo | readme.aireml [2014/06/04 13:09] – [Options] ignacio | ||
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| ====== AIREMLF90 ====== | ====== AIREMLF90 ====== | ||
| - | \\ | ||
| - | A modification of REMLF90 for estimating variances with the Average-Information algorithm. Initially written by Shogo Tsuruta in 03/ | ||
| - | See PREGSF90 with genotypes (SNP) for options. | ||
| ===== Summary ===== | ===== Summary ===== | ||
| + | A modification of REMLF90 for estimating variances with the Average-Information algorithm. Initially written by Shogo Tsuruta in 03/ | ||
| + | \\ | ||
| AIREMLF90 uses a second derivative REML algrithm with extra heuristics, as is described in Jensen et al. (1996-7). For most models, it converges in far fewer rounds than EM-REML as implemented in REMLF90. While typically REMLF90 takes 50-300 rounds to converge, AIREMLF90 converges in 5-15 rounds and to a higher accuracy. For selected models, AI-REML may fail to converge when the covariance matrix is close to non-positive definite. Adjust sensitivity of the program by setting the appropriate tolerance or setting good starting values. The final results will be saved in " | AIREMLF90 uses a second derivative REML algrithm with extra heuristics, as is described in Jensen et al. (1996-7). For most models, it converges in far fewer rounds than EM-REML as implemented in REMLF90. While typically REMLF90 takes 50-300 rounds to converge, AIREMLF90 converges in 5-15 rounds and to a higher accuracy. For selected models, AI-REML may fail to converge when the covariance matrix is close to non-positive definite. Adjust sensitivity of the program by setting the appropriate tolerance or setting good starting values. The final results will be saved in " | ||
| + | \\ | ||
| + | See PREGSF90 with genotypes (SNP) for options. | ||
| ===== Options ===== | ===== Options ===== | ||
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| </ | </ | ||
| Run EM-REML (REMLF90) for first 10 rounds to get initial variances within the parameter space (default 0). | Run EM-REML (REMLF90) for first 10 rounds to get initial variances within the parameter space (default 0). | ||
| + | < | ||
| + | OPTION use_yams | ||
| + | </ | ||
| + | Run the program with YAMS (modified FSPAK). The computing time can be dramatically improved. | ||
| < | < | ||
| OPTION tol 1d-12 | OPTION tol 1d-12 | ||
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| Store solutions and those s.e. | Store solutions and those s.e. | ||
| < | < | ||
| - | OPTION | + | OPTION |
| </ | </ | ||
| - | Set the missing value (default 0). | + | y-hat and residuals will be included in " |
| + | < | ||
| + | OPTION missing -999 | ||
| + | </ | ||
| + | Specify | ||
| **Heterogeneous residual variances for a single trait** | **Heterogeneous residual variances for a single trait** | ||
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| Specify the SNP file name to use genotype data. | Specify the SNP file name to use genotype data. | ||
| + | < | ||
| + | |||
| + | < | ||
| + | Set the number of samples to calculate SE for function of (co)variances.\\ | ||
| + | default value 5000 | ||
| + | < | ||
| + | Indicate to store in file samples of (co)variances function for postprocessing (histogram, etc.) | ||
readme.aireml.txt · Last modified: 2024/03/25 18:22 by 127.0.0.1