Generate data for brms models to be passed to Stan

make_standata(
formula,
data,
family = gaussian(),
prior = NULL,
autocor = NULL,
data2 = NULL,
cov_ranef = NULL,
sample_prior = "no",
stanvars = NULL,
knots = NULL,
...
)

## Arguments

formula An object of class formula, brmsformula, or mvbrmsformula (or one that can be coerced to that classes): A symbolic description of the model to be fitted. The details of model specification are explained in brmsformula. An object of class data.frame (or one that can be coerced to that class) containing data of all variables used in the model. A description of the response distribution and link function to be used in the model. This can be a family function, a call to a family function or a character string naming the family. Every family function has a link argument allowing to specify the link function to be applied on the response variable. If not specified, default links are used. For details of supported families see brmsfamily. By default, a linear gaussian model is applied. In multivariate models, family might also be a list of families. One or more brmsprior objects created by set_prior or related functions and combined using the c method or the + operator. See also get_prior for more help. (Deprecated) An optional cor_brms object describing the correlation structure within the response variable (i.e., the 'autocorrelation'). See the documentation of cor_brms for a description of the available correlation structures. Defaults to NULL, corresponding to no correlations. In multivariate models, autocor might also be a list of autocorrelation structures. It is now recommend to specify autocorrelation terms directly within formula. See brmsformula for more details. A named list of objects containing data, which cannot be passed via argument data. Required for some objects used in autocorrelation structures to specify dependency structures as well as for within-group covariance matrices. (Deprecated) A list of matrices that are proportional to the (within) covariance structure of the group-level effects. The names of the matrices should correspond to columns in data that are used as grouping factors. All levels of the grouping factor should appear as rownames of the corresponding matrix. This argument can be used, among others to model pedigrees and phylogenetic effects. It is now recommended to specify those matrices in the formula interface using the gr and related functions. See vignette("brms_phylogenetics") for more details. Indicate if draws from priors should be drawn additionally to the posterior draws. Options are "no" (the default), "yes", and "only". Among others, these draws can be used to calculate Bayes factors for point hypotheses via hypothesis. Please note that improper priors are not sampled, including the default improper priors used by brm. See set_prior on how to set (proper) priors. Please also note that prior draws for the overall intercept are not obtained by default for technical reasons. See brmsformula how to obtain prior draws for the intercept. If sample_prior is set to "only", draws are drawn solely from the priors ignoring the likelihood, which allows among others to generate draws from the prior predictive distribution. In this case, all parameters must have proper priors. An optional stanvars object generated by function stanvar to define additional variables for use in Stan's program blocks. Number of threads to use in within-chain parallelization. For more control over the threading process, threads may also be a brmsthreads object created by threading. Within-chain parallelization is experimental! We recommend its use only if you are experienced with Stan's reduce_sum function and have a slow running model that cannot be sped up by any other means. Optional list containing user specified knot values to be used for basis construction of smoothing terms. See gamm for more details. Other arguments for internal use.

## Value

A named list of objects containing the required data to fit a brms model with Stan.

## Author

Paul-Christian Buerkner paul.buerkner@gmail.com

## Examples

sdata1 <- make_standata(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = "cumulative")
str(sdata1)
#> List of 12
#>  $N : int 572 #>$ Y         : num [1:572(1d)] 1 1 1 1 1 1 1 1 1 1 ...
#>  $nthres : int 3 #>$ K         : int 3
#>  $X : num [1:572, 1:3] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ... #> ..- attr(*, "dimnames")=List of 2 #> .. ..$ : chr [1:572] "1" "2" "3" "4" ...
#>   .. ..$: chr [1:3] "treat" "period" "carry" #>$ Z_1_1     : num [1:572(1d)] 1 1 1 1 1 1 1 1 1 1 ...
#>   ..- attr(*, "dimnames")=List of 1
#>   .. ..$: chr [1:572] "1" "2" "3" "4" ... #>$ disc      : num 1
#>  $J_1 : int [1:572(1d)] 1 2 3 4 5 6 7 8 9 10 ... #>$ N_1       : int 286
#>  $M_1 : int 1 #>$ NC_1      : int 0
#>  $prior_only: int 0 #> - attr(*, "class")= chr [1:2] "standata" "list" sdata2 <- make_standata(count ~ zAge + zBase * Trt + (1|patient), data = epilepsy, family = "poisson") str(sdata2) #> List of 10 #>$ N         : int 236
#>  $Y : num [1:236(1d)] 5 3 2 4 7 5 6 40 5 14 ... #>$ K         : int 5
#>  $X : num [1:236, 1:5] 1 1 1 1 1 1 1 1 1 1 ... #> ..- attr(*, "dimnames")=List of 2 #> .. ..$ : chr [1:236] "1" "2" "3" "4" ...
#>   .. ..$: chr [1:5] "Intercept" "zAge" "zBase" "Trt1" ... #> ..- attr(*, "assign")= int [1:5] 0 1 2 3 4 #> ..- attr(*, "contrasts")=List of 1 #> .. ..$ Trt: num [1:2, 1] 0 1
#>   .. .. ..- attr(*, "dimnames")=List of 2
#>   .. .. .. ..$: chr [1:2] "0" "1" #> .. .. .. ..$ : chr "1"
#>  $Z_1_1 : num [1:236(1d)] 1 1 1 1 1 1 1 1 1 1 ... #> ..- attr(*, "dimnames")=List of 1 #> .. ..$ : chr [1:236] "1" "2" "3" "4" ...
#>  $J_1 : int [1:236(1d)] 1 2 3 4 5 6 7 8 9 10 ... #>$ N_1       : int 59
#>  $M_1 : int 1 #>$ NC_1      : int 0
#>  \$ prior_only: int 0
#>  - attr(*, "class")= chr [1:2] "standata" "list"