Importing data in DDModeling is done by calling the DDRep() function, which needs two arguments model and data. model constitutes a DDModel object (see here for a tutorial). raw specifies the raw data from a given experiment. For a sucessfull import, raw needs to follow a formating convention. This convention will be investigated in the following section.

How to format RAW data

Raw data in the DDModeling package is handled in data.frames containing three coulmns:

• cond (Factor): Specifying the condition under which a given trial was taken
• resp (Numeric): Specifying the binary coded response property of a trial
• time (Numeric): Specifying the time (in rounded ms) for a given trial

The naming convention inside the cond coulmn is free, however you have to make sure that the factor labels correspond to the model inside the DDRep() function, meaning that the conditions specified in the model need to match those in your data! resp on the other hand follows a unified coding scheme: The value 0 represents a trial that resulted in an error (i.e. wrong response) and the value 1 represents a trial that resulted in a success (i.e. correct response). time has to be in the format of rounded milliseconds, meaning only natural numbers are allowed.

For demonstration purposes DDModeling comes equipped with a predefined dataset FLANKER_DATA, which constitutes 64 datasets (i.e. subjects) of a flanker task experiment with 320 trials per condition. If you want to import data into DDModeling your data should look something like this

library(DDModeling)
#> Lade nötiges Paket: data.table
head(FLANKER_DATA[[1]])
#>     cond time resp
#> 1   Cong  554    1
#> 2 Incong  613    0
#> 3   Cong  833    0
#> 4   Cong  433    1
#> 5   Cong  578    1
#> 6 Incong  439    1

How to import data

After you formated your data according to the above conventions and constructed an according DDModel importing it is rather simple.

DSTP <- DDModel(model="DSTP",task = "flanker",CDF_perc = c(0.1,0.3,0.5,0.7,0.9), CAF_perc = c(0.0,0.2,0.4,0.6,0.8,1.0))
Subj <- DDRep(model = DSTP,raw = FLANKER_DATA[[1]])

Finally you can take a look at your imported data simply by calling the object or using the plot() function.

Subj
#> CDF: 
#> $Cong
#>   cond perc time   N
#> 1 Cong  0.1  339  30
#> 2 Cong  0.3  386  90
#> 3 Cong  0.5  435 151
#> 4 Cong  0.7  499 211
#> 5 Cong  0.9  617 271
#> 
#> $Incong
#>     cond perc time   N
#> 1 Incong  0.1  327  30
#> 2 Incong  0.3  376  92
#> 3 Incong  0.5  414 154
#> 4 Incong  0.7  466 215
#> 5 Incong  0.9  579 277
#> 
#> 
#> CAF: 
#> $Cong
#>   cond perc time      acc N_A N_B
#> 1 Cong  0.1  333 0.921875  59   5
#> 2 Cong  0.3  385 0.921875  59   5
#> 3 Cong  0.5  431 0.968750  62   2
#> 4 Cong  0.7  500 0.968750  62   2
#> 5 Cong  0.9  661 0.937500  60   4
#> 
#> $Incong
#>     cond perc time      acc N_A N_B
#> 1 Incong  0.1  326 1.000000  64   0
#> 2 Incong  0.3  377 0.953125  61   3
#> 3 Incong  0.5  415 0.968750  62   2
#> 4 Incong  0.7  472 0.937500  60   4
#> 5 Incong  0.9  631 0.953125  61   3
#> 
#> 
#> Parameter: 
#>   Ter a c mu_t mu_f mu_RS2 mu_SS
#> 1   0 0 0    0    0      0     0
plot(Subj)