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--- readme.reml [2012/05/29 17:06] – shogo
+++ readme.reml [2014/11/25 11:34] – shogo
@@ Line 1: / Line 1: @@
 ====== REMLF90 ======
-Variance component estimation program with EM-REML algorithm.
 ===== Summary =====
+The variance component estimation program with EM-REML algorithm. The final results will be saved in "remlf90.log".
+\\
+See PREGSF90 with genotypes (SNP) for options.
 ===== Options =====
@@ Line 11: / Line 12: @@
 Convergence criterion (default 1d-10).
 <file>
-OPTION maxrounds500
+OPTION maxrounds 10000
 </file>
 Maximum rounds (default 5000).
+<file>
+OPTION sol se
+</file>
+Store solutions and se.
+<file>
+OPTION residual
+</file>
+y-hat and residuals will be included in "yhat_residual".
+<file>
+OPTION missing -999
+</file>
+Specify missing observations (default 0).
+<file>
+OPTION use_yams
+</file>
+Run the program with YAMS (modified FSPAK). The computing time can be dramatically improved.
 <file>
 OPTION constant_var 5 1 2
@@ Line 19: / Line 36: @@
 : effect number\\
 : first trait number\\
-: and the second trait number\\
+: second trait number\\
 implying the covariance between traits 1 and 2 for effect 5.
 <file>
@@ Line 26: / Line 43: @@
 Specify the SNP file name to use genotype data.
-===== Does remlf90 always converge? =====
+===== Does REMLF90 always converge? =====
-Even when there is no variance, remlf90 will estimate a positive variance (in the parameter space), which will be determined by the starting value. If you are not sure if there is any variance, use a small starting value like 0.1 or 0.01. If the estimate does not change, probably there is no variance, so check your parameter file, model, and/or data again. If airemlf90 does not converge but remlf90 converges with the same dataset, rerun remlf90 with a small starting value. The estimate could be artifact.
+When the expected variance is very small or the covariance matrix is close to non-positive definite, try with two different starting values (e.g., much smaller = 0.00001 and much bigger = 1000 when the expected variance is between 0 and 10).
+The REMLF90 program will always estimate a positive variance (within the parameter space) even if there is no variance (using wrong data or format and/or a completely wrong model).
+If AIREMLF90 does not converge but REMLF90 converges with the same data set and the same model, rerun REMLF90 with a small starting value to check the estimate because it could be artifact.