A modification of REMLF90 with computing by the Average-Information Algorithm. ===== 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. Initially written by Shogo Tsuruta, University of Georgia, 03/99-07/99 Several options are avaiable: OPTION conv_crit 1d-12 convergence criterion (default 1d-10). OPTION maxrounds 500 maximum rounds (default 5000). when it is negative, the program calculates BLUP without running REML. OPTION EM-REML 10 run EM-REML (REMLF90) for first 10 rounds to get initial variances within the parameter space (default 0). 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. OPTION sol se store solutions and s.e. OPTION missing -1 set the missing value (default 0). # Heterogeneous residual variances for a single trait OPTION hetres_pos 10 11 specify the position of covariables. OPTION hetres_pol 4.0 0.1 0.1 initial values of coefficients for heterogeneous residual variances use ln(a0, a1, a2, …) to make these values. # Heterogeneous residual variances for mutiple traits OPTION hetres_pos 10 10 11 11 specify the position of covariables (trait first). OPTION hetres_pol 4.0 4.0 0.1 0.1 0.01 0.01 initial values of coefficients for heterogeneous residual variances use ln(a0, a1, a2, …) to make these values (trait first).