Allows users to alter the default behavior of multic
1 2 3 4 5 6 7 8 9 10  multic.control(epsilon = 1e5,
max.iterations = 50,
boundary.fix = TRUE,
constraints = c("E", "E", "E", "E", "F", "F", "F"),
initial.values = NULL,
save.output.files = FALSE,
method = c("multic", "leastsq", "maxfun", "emvc"),
calc.fam.log.liks = FALSE,
calc.residuals = FALSE,
keep.input = calc.residuals)

epsilon 
a numeric value specifying the convergence threshold. When the
difference of an iteration's loglikelihood and the previous
iteration's loglikelihood are less than

max.iterations 
an integer value specifying the maximum number of iterations

boundary.fix 
logical flag: if 
constraints 
a character vector of length seven (7) specifying the constraints on
the random effects variance components. Each
value of the vector needs to be either

initial.values 
numeric vector: use the specified initial values instead of calculating them automatically. This vector has a very specific length and order. If n is the number of traits and m is ( n + (n1) + (n2) + ... + 1 ), then the length must be n + 6 * m. So for one trait (univariate), the length must be 7, for two traits (bivariate), 20, and so on. The position of the values in the vector is important as well. The first n terms are the mu starting values. The next starting values come in chunks of m. The next m values are the polygenic starting values, followed by major gene, environmental, siblingsibling, parentparent, and parentoffspring starting values. The metadata\$null.initial.values contains the placement of the starting values. You can use this to verify your order is correct. 
save.output.files 
logical flag: if 
method 
a character value specifying the method to use in fitting the model.
Possible values include 
calc.fam.log.liks 
logical flag: if 
calc.residuals 
logical flag: if 
keep.input 
logical flag: if 
a list that is designed to be supplied as a control argument to
multic
. The values for
multic.control
can
be supplied directly in a
call to multic
(via the
...
parameter). These values are then
filtered through multic.control
inside multic
.
multic
,
multic.object
1 2 3 4 5 6 7 
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