Create randomized training blocks for AP krus96, in a format
suitable for the slpEXIT model, and other models that use the
same input representation format.
krus96train(blocks = 15, subjs = 56, ctxt = TRUE, seed = 1)A data frame, where each row is one trial, and the columns contain model input.
Number of training blocks to generate. Omit this
argument to get the same number of blocks (15) as used in
krus96.
Number of simulated subjects to be run.
If TRUE, include a context cue (x7) that
appears on every trial.
Sets the random seed.
René Schlegelmilch, Andy Wills
A data frame is produced, with one row for each trial, and with the following columns:
ctrl - Set to 1 (reset model) for trial 1 of each simulated
subject, set to zero (normal trial) for all other training trials, and
set to 2 for test trials (i.e. those with no feedback).
block - training block
stim - Stimulus code, as described in Kruschke (1996).
x1, x2, ... - symptom representation. Each column represents
one symptom, in the order I1, PC1, PR1, I2, PC2, PR2, context. 1 =
symptom present, 0 = symptom absent
t1, t2, ... - Disease representation. Each column represents
one disease, in the order C1, R1, C2, R2. 1 = disease present. 0 =
disease absent.
Although the trial ordering is random, a random seed is used, so multiple calls of this function with the same parameters should produce the same output. This is usually desirable for reproducibility and stability of non-linear optimization. To get a different order, use the seed argument to set a different seed.
This routine was originally developed to support Wills et al. (n.d.).
Kruschke, J.K. (1996). Base rates in category learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 3-26.
Wills et al. (n.d.). Benchmarks for category learning. Manuscript in preparation.
krus96