# Checks validity of arguments for mb.write

`write.check.Rd`

Checks validity of arguments for mb.write

## Arguments

- fun
An object of class

`"timefun"`

generated (see Details) using any of`tloglin()`

,`tpoly()`

,`titp()`

,`temax()`

,`tfpoly()`

,`tspline()`

or`tuser()`

- positive.scale
A boolean object that indicates whether all continuous mean responses (y) are positive and therefore whether the baseline response should be given a prior that constrains it to be positive (e.g. for scales that cannot be <0).

- intercept
A boolean object that indicates whether an intercept (written as

`alpha`

in the model) is to be included. If left as`NULL`

(the default), an intercept will be included only for studies reporting absolute means, and will be excluded for studies reporting change from baseline (as indicated in`network$cfb`

).- rho
The correlation coefficient when modelling within-study correlation between time points. The default is a string representing a prior distribution in JAGS, indicating that it be estimated from the data (e.g.

`rho="dunif(0,1)"`

).`rho`

also be assigned a numeric value (e.g.`rho=0.7`

), which fixes`rho`

in the model to this value (e.g. for use in a deterministic sensitivity analysis). If set to`rho=0`

(the default) then this implies modelling no correlation between time points.- covar
A character specifying the covariance structure to use for modelling within-study correlation between time-points. This can be done by specifying one of the following:

`"varadj"`

- a univariate likelihood with a variance adjustment to assume a constant correlation between subsequent time points (Jansen et al. 2015) . This is the default.`"CS"`

- a multivariate normal likelihood with a compound symmetry structure`"AR1"`

- a multivariate normal likelihood with an autoregressive AR1 structure

- omega
DEPRECATED IN VERSION 0.2.3 ONWARDS (~uniform(-1,1) now used for correlation between parameters rather than a Wishart prior). A scale matrix for the inverse-Wishart prior for the covariance matrix used to model the correlation between time-course parameters (see Details for time-course functions).

`omega`

must be a symmetric positive definite matrix with dimensions equal to the number of time-course parameters modelled using relative effects (`pool="rel"`

). If left as`NULL`

(the default) a diagonal matrix with elements equal to 1 is used.- link
Can take either

`"identity"`

(the default),`"log"`

(for modelling Ratios of Means (Friedrich et al. 2011) ) or`"smd"`

(for modelling Standardised Mean Differences - although this also corresponds to an identity link function).- sdscale
Logical object to indicate whether to write a model that specifies a reference SD for standardising when modelling using Standardised Mean Differences. Specifying

`sdscale=TRUE`

will therefore only modify the model if link function is set to SMD (`link="smd"`

).- class.effect
A list of named strings that determines which time-course parameters to model with a class effect and what that effect should be (

`"common"`

or`"random"`

). For example:`list(emax="common", et50="random")`

.- UME
Can take either

`TRUE`

or`FALSE`

(for an unrelated mean effects model on all or no time-course parameters respectively) or can be a vector of parameter name strings to model as UME. For example:`c("beta.1", "beta.2")`

.