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--- readme.blupf90new [2018/06/20 20:38] – created shogo
+++ readme.blupf90new [2022/06/29 19:22] (current) – removed dani
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-====== BLUPF90TEST ======
-UNDER CONSTRUCTION
-===== Summary =====
-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.log".
-\\
-\\
-See PREGSF90 with genotypes (SNP) for options.
-===== Options =====
-<file>
-OPTION conv_crit 1d-12
-</file>
-Convergence criterion (default 1d-10).
-<file>
-OPTION maxrounds 1000
-</file>
-Maximum rounds (default 5000). When the number = 0, the program calculates BLUP without iterating REML and some statistics (-2logL, AIC, SE for (co)variances, ...).
-<file>
-OPTION EM-REML 10
-</file>
-Run EM-REML (REMLF90) for first 10 rounds to get initial variances within the parameter space (default 0).
-<file>
-OPTION use_yams
-</file>
-Run the program with YAMS (modified FSPAK). The computing time can be dramatically improved.
-<file>
-OPTION tol 1d-12
-</file>
-Tolerance (or precision) (default 1d-14) for positive definite matrix and g-inverse subroutines.\\
-Convergence may be much faster by changing this value.
-<file>
-OPTION sol se
-</file>
-Store solutions and those standard errors.
-<file>
-OPTION store_pev_pec 6
-</file>
-Store triangular matrices of standard errors and its covariances for correlated random effects such as direct-maternal effects and random-regression effects in "pev_pec_bf90".
-<file>
-OPTION residual
-</file>
-y-hat and residuals will be included in "yhat_residual".
-<file>
-OPTION missing -999
-</file>
-Specify the missing value (default 0) in integer.
-<file>
-OPTION constant_var 5 1 2 ...
-</file>
-: effect number\\
-: first trait number\\
-: second trait number\\
-implying the covariance between traits 1 and 2 for effect 5.
-**Heterogeneous residual variances for a single trait**
-<file>
-OPTION hetres_pos 10 11
-</file>
-Specify the position of covariables.
-<file>
-OPTION hetres_pol 4.0 0.1 0.1
-</file>
-Initial values of coefficients for heterogeneous residual variances using //ln//(a0, a1, a2, ...) to make these values.
-**Heterogeneous residual variances for multiple traits**\\
-Convergence will be very slow with multiple trait heterogeneous residual variances
-<file>
-OPTION hetres_pos 10 10 11 11
-</file>
-or
-<file>
-OPTION hetres_pos 10 11 12 13
-</file>
-Specify the position of covariables (trait first).
-"10 10" or "10 11" could be linear for first and second traits.\\
-"11 11" or "12 13" could be quadratic.
-<file>
-OPTION hetres_pol 4.0 4.0 0.1 0.1 0.01 0.01
-</file>
-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).
-<file>
-OPTION SNP_file snp
-</file>
-Specify the SNP file name to use genotype data.
-<file>OPTION se_covar_function <label> <function></file>
-As an alternative of SE, calculate SD for function of (co)variances by repeated sampling of parameters estimates from their asymptotic multivariate normal distribution, following ideas presented by Meyer and Houle 2013.\\
-\\
-''<label>''\\
-A name for a particular function (e.g., ''P1'' for phenotypic variance of trait 1, ''H2_1'' for heritability for trait 1, ''rg12'' for genetic correlation between traits 1 and 2, …).\\
-\\
-''<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 (''G_eff1_eff2_trt1_trt2'') that indicate the element of the (co)variance matrix for random effect ''eff1'' and ''eff2'' and ''trt1'' and ''trt2'',\\
-where ''eff1'' and ''eff2'' are effect numbers 1 and 2, and ''trt1'' and ''trt2'' are trait numbers 1 and 2.\\
-''R_trt1_trt1'' indicates the element of the residual (co)variance matrix for traits 1 and 2.\\
-\\
-Several functions could be added, with one OPTION line per function.\\
-\\
-Examples:\\
-\\
-''OPTION se_covar_function  P  G_2_2_1_1+G_2_3_1_1+G_3_3_1_1+G_4_4_1_1+R_1_1''\\
-''OPTION se_covar_function  H2d  G_2_2_1_1/(G_2_2_1_1+G_2_3_1_1+G_3_3_1_1+G_4_4_1_1+R_1_1)''\\
-''OPTION se_covar_function  H2t  (G_2_2_1_1+1.5*G_2_3_1_1+0.5*G_3_3_1_1)/(G_2_2_1_1+G_2_3_1_1+G_3_3_1_1+G_4_4_1_1+R_1_1)''\\
-''OPTION se_covar_function  rg12  G_2_2_1_2/(G_2_2_1_1*G_2_2_2_2)**0.5''\\
-\\
-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, and 4 is the effect number for the maternal permanent random effect.
-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)
-<file>OPTION samples_se_covar_function <n></file>
-Set the number of samples to calculate SE for function of (co)variances.\\
-default value 10000
-<file>OPTION out_se_covar_function</file>
-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:
-. 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).
-. 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.