mvmeta.control {mvmeta} | R Documentation |

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 `mvmeta.fit`

.

```
mvmeta.control(optim=list(), showiter=FALSE, maxiter=100, initPsi=NULL,
Psifix=NULL,Psicor=0, Scor=0, inputna=FALSE, inputvar=10^4, igls.iter=10,
hessian=FALSE, vc.adj=TRUE,reltol=sqrt(.Machine$double.eps),
set.negeigen=sqrt(.Machine$double.eps))
```

`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) 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 |

`Psifix` |
either a matrix or a vector of its lower triangular elements (with diagonal, taken by column) equal or proportional to the between-study (co)variance. Only used when |

`Psicor` |
either a scalar, vector or matrix representing the between-study correlation(s) (see |

`Scor` |
either a scalar, vector or matrix representing the within-study correlation(s) to be inputted when the covariances are not provided, and ignored if they are (see |

`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 |

`igls.iter` |
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. 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 |

`set.negeigen` |
positive value. Value to which negative eigenvalues are to be set in estimators where such method is used to force positive semi-definiteness of the estimated between-study (co)variance matrix. |

The control argument of `mvmeta`

is by default passed to `mvmeta.fit`

, which uses its elements as arguments of `mvmeta.control`

.

Many arguments refer to specific fitting procedures. Refer to 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 function is expected to be extended and/or modified at every release of the package mvmeta.

A list with components named as the arguments.

Antonio Gasparrini, antonio.gasparrini@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**].

Gasparrini A, Armstrong B, Kenward MG (2012). Multivariate meta-analysis for non-linear and other multi-parameter associations. *Statistics in Medicine*. **31**(29):3821–3839. [Freely available **here**].

See `mvmeta`

. See also `glm.control`

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

for an overview of this modelling framework.

```
# PRINT THE ITERATIONS (SEE ?optim) AND CHANGE THE DEFAULT FOR STARTING VALUES
model <- mvmeta(cbind(PD,AL)~pubyear,S=berkey98[5:7],data=berkey98,
control=list(showiter=TRUE,igls.iter=20))
# INPUT THE CORRELATION
model <- mvmeta(cbind(y1,y2),S=cbind(V1,V2),data=p53,control=list(Scor=0.5))
```

[Package *mvmeta* version 1.0.3 Index]