This internal function sets the parameter options used for fitting dose-response meta-analytical models,
commonly to pre-specified default values. It is usually internally called by dosresmeta.fit.
The function has many more arguments than needed for dosresmeta. For more details see mixmeta.control.
dosresmeta.control(
optim = list(),
showiter = FALSE,
maxiter = 100,
initPsi = NULL,
Psifix = NULL,
Scor = NULL,
addSlist = NULL,
inputna = FALSE,
inputvar = 10^4,
loglik.iter = "hybrid",
igls.inititer = 10,
gr = FALSE,
hessian = FALSE,
vc.adj = TRUE,
reltol = sqrt(.Machine$double.eps),
checkPD = NULL,
set.negeigen = sqrt(.Machine$double.eps)
)A list with components named as the arguments.
list of parameters passed to the control argument of the function optim, which performs the quasi-Newton optimization in likelihood-based
random-effects models. See optim.
logical. If TRUE, the progress of iterative optimization is shown.
positive interger value. Maximum number of iterations in methods involving optimization procedures.
either a matrix or a vector of its lower triangular elements (with diagonal, taken by column) from which starting
values of the parameters of the between-study (co)variance matrix are derived, used in the optimization procedure for likelihood-based random-effects models.
If NULL (the default, and recommended), the starting value is created internally through an iterative generalized least square algorithm.
either a matrix or a vector of its lower triangular elements (with diagonal, taken by column), or optionally a named list with one or more of such objects. Used to define fixed parts of the random-effects
either a scalar, vector or matrix representing the within-unit correlation(s) to be inputted when the covariances are not provided in multivariate models, and ignored if they are.
a list of m matrices for the (outer-level) groups of units defining the (known) error (co)variance structure, when this cannot be passed through the argument S of mixmeta.
logical. If missing values must be internally inputted. To be used with caution.
multiplier for inputting the missing variances, to be passed as an argument to inputna.
iterative scheme used in in likelihood-based optimization routines. Options are "hybrid", "newton", and "igls" or "RIGLS".
number of iteration of the iterative generalized least square algorithm to be run in the hybrid optimization procedure of linkelihood-based models to provide the starting value.
indicates if the gradient of the (re)ml likelihood should be provided. FALSE by default.
logical. If TRUE, the Hessian matrix of the parameters estimated in the optimization process is computed and returned. Only applicable to likelihood-based estimation methods.
logical. If TRUE, an adjustement to the way the marginal variance part is computed in the (co)variance components estimator is applied in the variance components estimator.
relative convergence tolerance in methods involving optimization procedures. The algorithm stops if it is unable to reduce the value by a factor of \(reltol * (abs(val) + reltol)\) at a step.
logical. Determines if the semi-positiveness of within-unit error or random-effects (co)variance matrices must be checked.
positive value. Value to which negative eigenvalues are to be set in estimators where such method is used to force semi-positive definiteness of the estimated between-study (co)variance matrix.
Alessio Crippa, alessio.crippa@ki.se
Gasparrini, A., Armstrong, B., Kenward, M. G. (2012). Multivariate meta-analysis for non-linear and other multi-parameter associations. Statistics in Medicine, 31(29), 3821-3839.
dosresmeta, dosresmeta-package, dosresmeta.control
## Loading data
data("alcohol_cvd")
## print the iterations (see ?optim) and change the default for starting values
dosresmeta(formula = logrr ~ dose, type = type, id = id, se = se,
cases = cases, n = n, data = alcohol_cvd, proc = "1stage",
control = list(showiter = TRUE, igls.inititer = 20))
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