Simulate Data — make

Simulates data based on a model design and a parameter vector (p_vector) by one of two methods:

Creating a fully crossed and balanced design specified by the design, with number of trials per cell specified by the n_trials argument
Using the design of a data frame supplied, which allows creation of unbalanced and other irregular designs, and replacing previous data with simulated data

Usage

make_data(
  parameters,
  design = NULL,
  n_trials = NULL,
  data = NULL,
  expand = 1,
  mapped_p = FALSE,
  hyper = FALSE,
  ...
)

Arguments

parameters: parameter vector used to simulate data. Can also be a matrix with one row per subject (with corresponding row names) or an emc object with sampled parameters (in which case posterior medians of alpha are used to simulate data)
design: Design list created by design()
n_trials: Integer. If data is not supplied, number of trials to create per design cell
data: Data frame. If supplied, the factors are taken from the data. Determines the number of trials per level of the design factors and can thus allow for unbalanced designs
expand: Integer. Replicates the data (if supplied) expand times to increase number of trials per cell.
mapped_p: If TRUE instead returns a data frame with one row per design cell and columns for each parameter specifying how they are mapped to the design cells.
hyper: If TRUE the supplied parameters must be a set of samples, from which the group-level will be used to generate subject level parameters. See also make_random_effects to generate subject-level parameters from a hyper distribution.
...: Additional optional arguments

Value

A data frame with simulated data

Details

To create data for multiple subjects see ?make_random_effects().

Examples

# First create a design
design_DDMaE <- design(factors = list(S = c("left", "right"),
                                           E = c("SPD", "ACC"),
                                           subjects = 1:30),
                            Rlevels = c("left", "right"), model = DDM,
                            formula =list(v~0+S,a~E, t0~1, s~1, Z~1, sv~1, SZ~1),
                            constants=c(s=log(1)))
#> Parameter(s) st0 not specified in formula and assumed constant.
#> 
#>  Sampled Parameters: 
#> [1] "v_Sleft"  "v_Sright" "a"        "a_EACC"   "t0"       "Z"        "sv"      
#> [8] "SZ"      
#> 
#>  Design Matrices: 
#> $v
#>      S v_Sleft v_Sright
#>   left       1        0
#>  right       0        1
#> 
#> $a
#>    E a a_EACC
#>  SPD 1      0
#>  ACC 1      1
#> 
#> $t0
#>  t0
#>   1
#> 
#> $s
#>  s
#>  1
#> 
#> $Z
#>  Z
#>  1
#> 
#> $sv
#>  sv
#>   1
#> 
#> $SZ
#>  SZ
#>   1
#> 
#> $st0
#>  st0
#>    1
#> 
# Then create a p_vector:
parameters <- c(v_Sleft=-2,v_Sright=2,a=log(1),a_EACC=log(2), t0=log(.2),
              Z=qnorm(.5),sv=log(.5),SZ=qnorm(.5))

# Now we can simulate data
data <- make_data(parameters, design_DDMaE, n_trials = 30)

# We can also simulate data based on a specific dataset
design_DDMaE <- design(data = forstmann,model=DDM,
                            formula =list(v~0+S,a~E, t0~1, s~1, Z~1, sv~1, SZ~1),
                            constants=c(s=log(1)))
#> Parameter(s) st0 not specified in formula and assumed constant.
#> 
#>  Sampled Parameters: 
#> [1] "v_Sleft"     "v_Sright"    "a"           "a_Eneutral"  "a_Eaccuracy"
#> [6] "t0"          "Z"           "sv"          "SZ"         
#> 
#>  Design Matrices: 
#> $v
#>      S v_Sleft v_Sright
#>   left       1        0
#>  right       0        1
#> 
#> $a
#>         E a a_Eneutral a_Eaccuracy
#>     speed 1          0           0
#>   neutral 1          1           0
#>  accuracy 1          0           1
#> 
#> $t0
#>  t0
#>   1
#> 
#> $s
#>  s
#>  1
#> 
#> $Z
#>  Z
#>  1
#> 
#> $sv
#>  sv
#>   1
#> 
#> $SZ
#>  SZ
#>   1
#> 
#> $st0
#>  st0
#>    1
#> 
parameters <- c(v_Sleft=-2,v_Sright=2,a=log(1),a_Eneutral=log(1.5),a_Eaccuracy=log(2),
              t0=log(.2),Z=qnorm(.5),sv=log(.5),SZ=qnorm(.5))

data <- make_data(parameters, design_DDMaE, data = forstmann)