Differences

This shows you the differences between two versions of the page.

--- readme.aireml [2012/05/30 14:33] – shogo
+++ readme.aireml [2014/06/06 15:37] – [Options] ignacio
@@ Line 1: / Line 1: @@
 ====== AIREMLF90 ======
-\\
-A modification of REMLF90 for estimating variances with the Average-Information algorithm. Initially written by Shogo Tsuruta in 03/99-07/99.
 ===== Summary =====
-AIREMLF90 uses a second derivative REML algrithm with extra heuristics, as is described in Jensen et al. (1996-7). For most problems, 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 problems, AI-REML fails 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.
+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 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.log".
+\\
+See PREGSF90 with genotypes (SNP) for options.
 ===== Options =====
@@ Line 19: / Line 21: @@
 </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
@@ Line 29: / Line 35: @@
 Store solutions and those s.e.
 <file>
-OPTION missing -1
+OPTION residual
 </file>
-Set the missing value (default 0).
+y-hat and residuals will be included in "yhat_residual".
+<file>
+OPTION missing -999
+</file>
+Specify the missing value (default 0).
 **Heterogeneous residual variances for a single trait**
@@ Line 62: / Line 72: @@
 "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>
+Calculate SD for function of (co)variances by repeated sampling of parameters estimates from their asymptotic multivariate normal distribution, following idea presented by Meyer and Houle 2013.
+''label'' given name for a particular function\\
+''function'' function of (co)variances to estimate SD, all elements of the function should be written with no spaces.\\
+\\
+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:\\
+''G_eff1_eff2_trt1_trt2'': indicate the element of the (co)variance matrix for random effect ''eff1'' and ''eff2'' and ''trt1'' and ''trt2'', where ''eff1'', ''eff2'' are the effect number, and ''trt1'' and ''trt2'' the trait number\\
+''R_trt1_trt1'': indicate the element of the residual (co)variance matrix for traits ''trt1'' and ''trt2''
+Example"
+''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)''\\
+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 and the third the total heritability.
+<file>OPTION samples_se_covar_function <n></file>
+Set the number of samples to calculate SE for function of (co)variances.\\
+default value 5000
+<file>OPTION out_se_covar_function</file>
+Indicate to store in file samples of (co)variances function for postprocessing (histogram, etc.)