# R/nosof88train.R In catlearn: Formal Psychological Models of Categorization and Learning

#### Documented in nosof88train

```nosof88train <- function(condition = 'B',blocks = 3, absval = -1,
subjs = 1, seed = 4182, missing = 'geo') {
set.seed(seed)
n88b <- rbind(
c(1,-2.543,2.641,1,-1,0,0),
c(2,.943,4.341,-1,1,0,0),
c(3,-1.092,1.848,-1,1,0,0),
c(4,1.558,2.902,-1,1,0,0),
c(5,-2.258,.430,1,-1,0,0),
c(6,.194,.572,-1,1,0,0),
c(7,2.806,.202,-1,1,0,0),
c(8,-1.177,-1.038,1,-1,0,0),
c(9,1.543,-1.040,-1,1,0,0),
c(10,-2.775,-3.149,1,-1,0,0),
c(11,.528,-3.766,1,-1,0,0),
c(12,1.709,-3.773,1,-1,0,0)
)
colnames(n88b) <- c('stim','x1','x2','t1','t2','m1','m2')

cond <- switch(condition, B = 1, E2 = 2, E7 = 3)

n88e2 <- rbind(n88b,n88b[2,],n88b[2,],n88b[2,],n88b[2,])

n88e7 <- rbind(n88b,n88b[7,],n88b[7,],n88b[7,],n88b[7,])

n88 <- switch(condition, B = n88b, E2 = n88e2, E7 = n88e7, n88b)

# Run 'subjs' times
finalist <- NULL
for(subj in 1:subjs) {
# Build list
makelist <- NULL
for(blk in 1:blocks) {
block <- rbind(n88,n88,n88,n88)
block <- cbind(cond,blk,block)
block <- block[sample(nrow(block)),]
makelist <- rbind(makelist,block)
}
ctrl <- c(1,rep(0,nrow(makelist)-1))
makelist <- cbind(ctrl,makelist)
finalist <- rbind(finalist,makelist)
}
# If not using the geometric model representation of dimension
# absence (m1, m2, etc), drop these columns
if(missing != 'geo') finalist <- finalist[,1:8]

# If the value for category absence is not -1
# change the list to reflect this
if(absval != -1) {
finalist[finalist[,'t1'] == -1,'t1'] <- absval
finalist[finalist[,'t2'] == -1,'t2'] <- absval
}
return(finalist)
}
```

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catlearn documentation built on April 4, 2023, 5:12 p.m.