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readme.blupf90new [2018/08/21 20:33]
shogo
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-====== BLUPF90TEST ====== 
- 
-UNDER CONSTRUCTION 
- 
-===== Summary ===== 
-This is a combined program of blupf90 and airemlf90. 
-\\ 
-\\ 
-See PREGSF90 with genotypes (SNP) for options. ​ 
- 
-===== Options ===== 
-<​file>​ 
-OPTION method VCE (default BLUP) 
-</​file>​ 
-Run airemlf90 (default running blupf90) 
-<​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>​ 
-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** 
-<​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: 
- 
-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.blupf90new.1534883585.txt.gz · Last modified: 2018/08/21 20:33 by shogo