Make an emc Object — make

Creates an emc object by combining the data, prior, and model specification into a emc object that is needed in fit().

Usage

make_emc(
  data,
  design,
  model = NULL,
  type = "standard",
  n_chains = 3,
  compress = TRUE,
  rt_resolution = 0.02,
  prior_list = NULL,
  group_design = NULL,
  par_groups = NULL,
  ...
)

Arguments

data: A data frame, or a list of data frames. Needs to have the variable subjects as participant identifier.
design: A list with a pre-specified design, the output of design().
model: A model list. If none is supplied, the model specified in design() is used.
type: A string indicating whether to run a standard group-level, blocked, diagonal, factor, or single (i.e., non-hierarchical) model.
n_chains: An integer. Specifies the number of mcmc chains to be run (has to be more than 1 to compute rhat).
compress: A Boolean, if TRUE (i.e., the default), the data is compressed to speed up likelihood calculations.
rt_resolution: A double. Used for compression, response times will be binned based on this resolution.
prior_list: A named list containing the prior. Default prior created if NULL. For the default priors, see ?get_prior_{type}.
group_design: A design for group-level mappings, made using group_design().
par_groups: A vector. Indicates which parameters are allowed to correlate. Could either be a list of character vectors of covariance blocks. Or a numeric vector, e.g., c(1,1,1,2,2) means the covariances of the first three and of the last two parameters are estimated as two separate blocks.
...: Additional, optional arguments.

Value

An uninitialized emc object

Examples

dat <- forstmann

# function that takes the lR factor (named diff in the following function) and
# returns a logical defining the correct response for each stimulus. In this
# case the match is simply such that the S factor equals the latent response factor.
matchfun <- function(d)d$S==d$lR

# design an "average and difference" contrast matrix
ADmat <- matrix(c(-1/2,1/2),ncol=1,dimnames=list(NULL,"diff"))

# specify design
design_LBABE <- design(data = dat,model=LBA,matchfun=matchfun,
formula=list(v~lM,sv~lM,B~E+lR,A~1,t0~1),
contrasts=list(v=list(lM=ADmat)),constants=c(sv=log(1)))
#> 
#>  Sampled Parameters: 
#> [1] "v"           "v_lMdiff"    "sv_lMTRUE"   "B"           "B_Eneutral" 
#> [6] "B_Eaccuracy" "B_lRright"   "A"           "t0"         
#> 
#>  Design Matrices: 
#> $v
#>     lM v v_lMdiff
#>   TRUE 1      0.5
#>  FALSE 1     -0.5
#> 
#> $sv
#>     lM sv sv_lMTRUE
#>   TRUE  1         1
#>  FALSE  1         0
#> 
#> $B
#>         E    lR B B_Eneutral B_Eaccuracy B_lRright
#>     speed  left 1          0           0         0
#>     speed right 1          0           0         1
#>   neutral  left 1          1           0         0
#>   neutral right 1          1           0         1
#>  accuracy  left 1          0           1         0
#>  accuracy right 1          0           1         1
#> 
#> $A
#>  A
#>  1
#> 
#> $t0
#>  t0
#>   1
#> 

# specify priors
pmean <- c(v=1,v_lMdiff=1,sv_lMTRUE=log(.5), B=log(.5),B_Eneutral=log(1.5),
           B_Eaccuracy=log(2),B_lRright=0, A=log(0.25),t0=log(.2))
psd <- c(v=1,v_lMdiff=0.5,sv_lMTRUE=.5,
         B=0.3,B_Eneutral=0.3,B_Eaccuracy=0.3,B_lRright=0.3,A=0.4,t0=.5)
prior_LBABE <- prior(design_LBABE, type = 'standard',pmean=pmean,psd=psd)

# create emc object
LBABE <- make_emc(dat,design_LBABE,type="standard",  prior=prior_LBABE)
#> Processing data set 1
#> Likelihood speedup factor: 4 (3955 unique trials)