Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/mixmeta.control.R

This internal function sets the parameter options used for fitting meta-analytical models, commonly to pre-specified default values. It is usually internally called by `mixmeta`

.

1 2 3 4 |

`optim ` |
list of parameters passed to the |

`showiter ` |
logical. If |

`maxiter ` |
positive interger value. Maximum number of iterations in methods involving optimization procedures. |

`initPsi ` |
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 as starting values of random-effects parameters in likelihood-based optimization routines. See Details. |

`Psifix ` |
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 |

`Scor ` |
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. See |

`addSlist ` |
a list of |

`inputna ` |
logical. If missing values must be internally inputted. To be used with caution. See |

`inputvar ` |
multiplier for inputting the missing variances, to be passed as an argument to |

`loglik.iter ` |
iterative scheme used in in likelihood-based optimization routines. Options are |

`igls.inititer ` |
number of iterations of the (restricted) iterative generalized least square algorithm when used in the initial phase of hybrid optimization procedure of likelihood-based estimators. See |

`hessian ` |
logical. If |

`vc.adj ` |
logical. If |

`reltol ` |
relative convergence tolerance in methods involving optimization procedures. The algorithm stops if it is unable to reduce the value by a factor of |

`checkPD ` |
logical. Determines if the semi-positiveness of within-unit error or random-effects (co)variance matrices must be checked. |

`set.negeigen ` |
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. |

This function has default values for most of the arguments, some of them set internally. Non-default values are passed through the control argument of `mixmeta`

. Many arguments refer to specific fitting procedures. See the help page of the related estimator for details.

The function automatically sets non-default values for some control arguments for `optim`

, unless explicitly set in the list passed to it. Specifically, the function selects `fnscale=-1`

, `maxit=maxiter`

and `reltol=reltol`

, where the latter two are specified by other arguments of this function.

The arguments `initPsi`

and `Psifix`

are used to provide information for estimation procedures of the random-effects parameters in likelihood-based methods. Specifically, the former is used to choose non-default starting values (see `mixmeta.ml`

), and the latter for defining the fixed (known) part of specific `(co)variance structures`

. In multilevel models, these arguments must be lists with named components referring to one or more levels of grouping defined by the argument `random`

of `mixmeta`

.

The argument `addSlist`

can be used to define more complex (known) error structures of the outcome(s) that are usually provided through the argument `S`

of `mixmeta`

as within-unit variances (or (co)variance matrices for multivariate models). This can be useful when these error structures spans multiple units (rows), and the between-unit correlation cannot be defined through `S`

, for instance in dose-response meta-analysis (see examples in `mixmeta`

). Note that this information is passed internally after the data have be re-ordered following the grouping defined by `random`

in `mixmeta`

, and this should be consistent in `addSlist`

. Specifically, the grouping variables are assumed as factors and therefore the groups are taken in alphabetical/numeric order. It is suggested to re-order the data according to this order of the groups before fitting the model, so to ensure consistency between the grouped data and `addSlist`

.

A list with components named as the arguments.

The function is expected to be extended and/or modified at every release of the package mixmeta. It is strongly suggested to check the arguments of this function at every release.

Antonio Gasparrini <antonio.gasparrini@lshtm.ac.uk> and Francesco Sera <francesco.sera@lshtm.ac.uk>

Sera F, Armstrong B, Blangiardo M, Gasparrini A (2019). An extended mixed-effects framework for meta-analysis.*Statistics in Medicine*. 2019;38(29):5429-5444. [Freely available **here**].

See `mixmeta`

. See also `glm.control`

. See the help pages of the related fitting functions for details on each parameter. See `mixmeta-package`

for an overview of this modelling framework.

1 2 3 4 5 6 7 8 9 10 11 | ```
# PRINT THE ITERATIONS (SEE ?optim) AND CHANGE THE DEFAULT FOR STARTING VALUES
mixmeta(cbind(PD,AL) ~ pubyear, S=berkey98[5:7], data=berkey98,
control=list(showiter=TRUE, igls.inititer=20))
# INPUT THE CORRELATION
mixmeta(cbind(y1,y2), S=cbind(V1,V2), data=p53, control=list(Scor=0.5))
# FIX (PARTS OF) THE RANDOM-EFFECTS (CO)VARIANCE MATRIX
y <- as.matrix(smoking[11:13])
S <- as.matrix(smoking[14:19])
mixmeta(y, S, bscov="prop", control=list(Psifix=diag(3)+1))
``` |

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