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Checks validity of arguments for mb.write


  fun = tpoly(degree = 1),
  positive.scale = TRUE,
  intercept = NULL,
  rho = 0,
  covar = NULL,
  omega = NULL,
  link = "identity",
  sdscale = FALSE,
  class.effect = list(),
  UME = c()



An object of class "timefun" generated (see Details) using any of tloglin(), tpoly(), titp(), temax(), tfpoly(), tspline() or tuser()


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).


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).


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.


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


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.


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).


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").


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").


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").


A boolean object that indicates whether the arguments imply modelling correlation between time points.


Used to check if the arguments given to mb.write are valid. The function will return informative errors if arguments are mispecified and will return an object that indicates whether the arguments imply modelling a correlation between time points if it passes.