readme.aireml
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readme.aireml [2014/06/06 15:39] – [Options] ignacio | readme.aireml [2020/11/12 20:13] – [Options] shogo | ||
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===== Summary ===== | ===== Summary ===== | ||
- | A modification of REMLF90 for estimating variances with the Average-Information algorithm. Initially written by Shogo Tsuruta in 03/ | + | A modification of REMLF90 for estimating variances with the Average-Information algorithm. Initially written by Shogo Tsuruta in 03/99-07/99. AIREMLF90 uses a second derivative REML algorithm 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. 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. | See PREGSF90 with genotypes (SNP) for options. | ||
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Convergence criterion (default 1d-10). | Convergence criterion (default 1d-10). | ||
< | < | ||
- | OPTION maxrounds | + | OPTION maxrounds |
</ | </ | ||
- | Maximum rounds (default 5000). When the number < 2, the program calculates BLUP without iterating REML. | + | Maximum rounds (default 5000). When n = 0, the program calculates BLUP without iterating REML and provides some statistics (-2logL, AIC, SE for (co)variances, |
< | < | ||
- | OPTION EM-REML | + | OPTION EM-REML |
</ | </ | ||
- | Run EM-REML (REMLF90) for first 10 rounds to get initial variances within the parameter space (default 0). | + | Run EM-REML (REMLF90) for first n rounds to get initial |
< | < | ||
OPTION use_yams | OPTION use_yams | ||
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OPTION sol se | OPTION sol se | ||
</ | </ | ||
- | Store solutions and those s.e. | + | Store solutions and those standard errors. |
+ | < | ||
+ | OPTION store_pev_pec 6 | ||
+ | </ | ||
+ | Store triangular matrices of standard errors and its covariances for correlated random effects such as direct-maternal effects and random-regression effects in " | ||
< | < | ||
OPTION residual | OPTION residual | ||
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OPTION missing -999 | OPTION missing -999 | ||
</ | </ | ||
- | Specify the missing value (default 0). | + | Specify the missing value (default 0) in integer. |
+ | < | ||
+ | OPTION constant_var 5 1 2 ... | ||
+ | </ | ||
+ | 5: effect number\\ | ||
+ | 1: first trait number\\ | ||
+ | 2: second trait number\\ | ||
+ | implying the covariance between traits 1 and 2 for effect 5. | ||
**Heterogeneous residual variances for a single trait** | **Heterogeneous residual variances for a single trait** | ||
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OPTION hetres_pos 10 11 | OPTION hetres_pos 10 11 | ||
</ | </ | ||
- | Specify the position | + | Specify the column positions |
< | < | ||
OPTION hetres_pol 4.0 0.1 0.1 | OPTION hetres_pol 4.0 0.1 0.1 | ||
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OPTION hetres_pos 10 11 12 13 | OPTION hetres_pos 10 11 12 13 | ||
</ | </ | ||
- | Specify the position | + | Specify the column positions |
"10 10" or "10 11" could be linear for first and second traits.\\ | "10 10" or "10 11" could be linear for first and second traits.\\ | ||
"11 11" or "12 13" could be quadratic. | "11 11" or "12 13" could be quadratic. | ||
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< | < | ||
- | Calculate | + | As an alternative of SE, calculate |
- | + | \\ | |
- | '' | + | '' |
- | '' | + | A name for a particular function |
+ | \\ | ||
+ | '' | ||
+ | A formula to calculate a function of (co)variances to estimate SD. All terms of the function should be written with no spaces.\\ | ||
+ | \\ | ||
+ | Each term of the function corresponds to (co)variance elements and could include any random effects (G) and residual (R) (co)variances.\\ | ||
+ | \\ | ||
+ | Notation is with reference to the effect number and the trait number ('' | ||
+ | where '' | ||
+ | '' | ||
+ | \\ | ||
+ | Several functions could be added, with one OPTION line per function.\\ | ||
\\ | \\ | ||
- | Each element of the function corresponds to (co)variances elements and could include any the random effects (G) and residual (R) (co)variances.\\ | ||
- | Notation is with reference to the effect and trait number:\\ | ||
- | '' | ||
- | '' | ||
- | |||
- | Several functions could be added, with one OPTION line per function. | ||
- | |||
Examples:\\ | Examples:\\ | ||
\\ | \\ | ||
- | '' | + | '' |
- | '' | + | '' |
- | '' | + | '' |
+ | '' | ||
+ | \\ | ||
The first function calculates the SD for the total variance for a maternal model with permanent maternal effect, where 2 and 3 are the effect number for the direct and maternal additive genetic effects respectively, | The first function calculates the SD for the total variance for a maternal model with permanent maternal effect, where 2 and 3 are the effect number for the direct and maternal additive genetic effects respectively, | ||
- | The second function calculates the heritability for the direct component | + | The second function calculates the heritability for the direct component. |
+ | |||
+ | The third function the total heritability. | ||
+ | |||
+ | The fourth function calculates the SD of the genetic correlation between traits 1 and 2 for the direct genetic effect (effect number 2) | ||
< | < | ||
Set the number of samples to calculate SE for function of (co)variances.\\ | Set the number of samples to calculate SE for function of (co)variances.\\ | ||
- | default value 5000 | + | default value 10000 |
< | < | ||
Indicate to store in file samples of (co)variances function for postprocessing (histogram, etc.) | Indicate to store in file samples of (co)variances function for postprocessing (histogram, etc.) | ||
+ | |||
+ | ===== Tricks ===== | ||
+ | When the covariance matrix is close to non-positive definite, the AIREMLF90 may not converge. | ||
+ | There are two options you might want to try: | ||
+ | |||
+ | 1. change the tolerance value (xx) in the option: | ||
+ | |||
+ | OPTION tol xx | ||
+ | |||
+ | to a very strict value (e.g., 1d-20) or a lenient value (1d-06). | ||
+ | |||
+ | 2. use an option to use EM-REML inside AI-REML: | ||
+ | |||
+ | OPTION EM-REML xx | ||
+ | |||
+ | where xx is the number of iterations for EM-REML you expect to get a good starting value for AI-REML. After running xx rounds with EM-REML, the AIREMLF90 program will automatically switch from EM-REML to AI-REML using the last estimate from EM-REML as a starting value for AI-REML. | ||
readme.aireml.txt · Last modified: 2024/03/25 18:22 by 127.0.0.1