The values supplied in the `lmeControl()`

call replace the
defaults, and a `list`

with all settings (i.e., values for
all possible arguments) is returned. The returned list is
used as the `control`

argument to the `lme`

function.

```
lmeControl(maxIter = 50, msMaxIter = 50, tolerance = 1e-6, niterEM = 25,
msMaxEval = 200,
msTol = 1e-7, msVerbose = FALSE,
returnObject = FALSE, gradHess = TRUE, apVar = TRUE,
.relStep = .Machine$double.eps^(1/3), minAbsParApVar = 0.05,
opt = c("nlminb", "optim"),
optimMethod = "BFGS", natural = TRUE,
sigma = NULL,
allow.n.lt.q = FALSE,
…)
```

maxIter

maximum number of iterations for the `lme`

optimization algorithm. Default is `50`

.

msMaxIter

maximum number of iterations
for the optimization step inside the `lme`

optimization.
Default is `50`

.

tolerance

tolerance for the convergence criterion in the
`lme`

algorithm. Default is `1e-6`

.

niterEM

number of iterations for the EM algorithm used to refine
the initial estimates of the random effects variance-covariance
coefficients. Default is `25`

.

msMaxEval

maximum number of evaluations of the objective
function permitted for nlminb. Default is `200`

.

msTol

tolerance for the convergence criterion on the first
iteration when `optim`

is used. Default is `1e-7`

.

returnObject

gradHess

a logical value indicating whether numerical gradient
vectors and Hessian matrices of the log-likelihood function should
be used in the internal optimization. This option is only available
when the correlation structure (`corStruct`

) and the variance
function structure (`varFunc`

) have no "varying" parameters and
the `pdMat`

classes used in the random effects structure are
`pdSymm`

(general positive-definite), `pdDiag`

(diagonal),
`pdIdent`

(multiple of the identity), or
`pdCompSymm`

(compound symmetry). Default is `TRUE`

.

apVar

a logical value indicating whether the approximate
covariance matrix of the variance-covariance parameters should be
calculated. Default is `TRUE`

.

.relStep

relative step for numerical derivatives
calculations. Default is `.Machine$double.eps^(1/3)`

.

optimMethod

character - the optimization method to be used with
the `optim`

optimizer. The default is
`"BFGS"`

. An alternative is `"L-BFGS-B"`

.

minAbsParApVar

numeric value - minimum absolute parameter value
in the approximate variance calculation. The default is `0.05`

.

natural

a logical value indicating whether the `pdNatural`

parametrization should be used for general positive-definite matrices
(`pdSymm`

) in `reStruct`

, when the approximate covariance
matrix of the estimators is calculated. Default is `TRUE`

.

sigma

optionally a positive number to fix the residual error at.
If `NULL`

, as by default, or `0`

, sigma is estimated.

allow.n.lt.q

`logical`

indicating if it is ok to have
less observations than random effects for each group. The default,
`FALSE`

signals an error; if `NA`

, such a situation only gives
a warning, as in nlme versions prior to 2019; if true, no message
is given at all.

a list with components for each of the possible arguments.

```
# NOT RUN {
# decrease the maximum number iterations in the ms call and
# request that information on the evolution of the ms iterations be printed
str(lCtr <- lmeControl(msMaxIter = 20, msVerbose = TRUE))
## This should always work:
do.call(lmeControl, lCtr)
# }
```

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