readme.aireml
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readme.aireml [2012/05/30 13:30] – shogo | readme.aireml [2020/09/22 23:51] – [Options] shogo | ||
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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/ | ||
===== Summary ===== | ===== Summary ===== | ||
- | AIREMLF90 uses a second derivative REML algrithm | + | 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. | ||
===== Options ===== | ===== Options ===== | ||
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OPTION maxrounds 1000 | OPTION maxrounds 1000 | ||
</ | </ | ||
- | Maximum rounds (default 5000). When the number | + | Maximum rounds (default 5000). When the number |
< | < | ||
- | 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 1000 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 | ||
</ | </ | ||
- | Tolerance (or precision) (default 1d-14) for positive definite matrix and g-inverse subroutines. Convergence may be much faster by changing this value. | + | Tolerance (or precision) (default 1d-14) for positive definite matrix and g-inverse subroutines.\\ |
+ | Convergence may be much faster by changing this value. | ||
< | < | ||
OPTION sol se | OPTION sol se | ||
</ | </ | ||
- | Store solutions and those s.e. | + | Store solutions and those standard errors. |
< | < | ||
- | OPTION | + | OPTION |
</ | </ | ||
- | Set the missing value (default 0). | + | 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 | ||
+ | </ | ||
+ | y-hat and residuals will be included in " | ||
+ | < | ||
+ | OPTION missing -999 | ||
+ | </ | ||
+ | Specify | ||
+ | < | ||
+ | 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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Initial values of coefficients for heterogeneous residual variances using //ln//(a0, a1, a2, ...) to make these values. | Initial values of coefficients for heterogeneous residual variances using //ln//(a0, a1, a2, ...) to make these values. | ||
- | **Heterogeneous residual variances for multiple traits** | + | **Heterogeneous residual variances for multiple traits**\\ |
Convergence will be very slow with multiple trait heterogeneous residual variances | Convergence will be very slow with multiple trait heterogeneous residual variances | ||
< | < | ||
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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.\\ | ||
+ | "11 11" or "12 13" could be quadratic. | ||
< | < | ||
OPTION hetres_pol 4.0 4.0 0.1 0.1 0.01 0.01 | OPTION hetres_pol 4.0 4.0 0.1 0.1 0.01 0.01 | ||
</ | </ | ||
- | Initial values of coefficients for heterogeneous residual variances using //ln//(a0, a1, a2, ...) to make these values (trait first). "4.0 4.0" are intercept for first and second traits. "0.1 0.1" could be linear and "0.01 0.01" could be quadratic. To transform back to the original scale, use exp(a0+a1*X1+a2*X2). | + | Initial values of coefficients for heterogeneous residual variances using //ln//(a0, a1, a2, ...) to make these values (trait first).\\ |
+ | "4.0 4.0" are intercept for first and second traits.\\ | ||
+ | "0.1 0.1" could be linear and "0.01 0.01" could be quadratic.\\ | ||
+ | To transform back to the original scale, use exp(a0+a1*X1+a2*X2). | ||
+ | < | ||
+ | OPTION SNP_file snp | ||
+ | </ | ||
+ | Specify the SNP file name to use genotype data. | ||
+ | |||
+ | < | ||
+ | As an alternative of SE, calculate SD for function of (co)variances by repeated sampling of parameters estimates from their asymptotic multivariate normal distribution, | ||
+ | \\ | ||
+ | ''< | ||
+ | A name for a particular function (e.g., '' | ||
+ | \\ | ||
+ | ''< | ||
+ | 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.\\ | ||
+ | \\ | ||
+ | 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 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.\\ | ||
+ | default value 10000 | ||
+ | < | ||
+ | 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