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AIREMLF90
A modification of REMLF90 with computing by the Average-Information Algorithm.
Initially written by Shogo Tsuruta, University of Georgia, 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 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.
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).