# R/shin92oat.R In catlearn: Formal Psychological Models of Categorization and Learning

#### Documented in shin92oat

```shin92oat <- function(dta, xtdo = FALSE) {
# Convert to accuracies
dta\$c2p[dta\$cond == 1 & dta\$stim < 9] <- 1 -
dta\$c2p[dta\$cond == 1 & dta\$stim < 9]
dta\$c2p[dta\$cond == 2 & dta\$stim < 16] <- 1 -
dta\$c2p[dta\$cond == 2 & dta\$stim < 16]
# Calculate size 3 accuracy
s3 <- dta[dta\$cond == 1,]
s3old <- mean(s3\$c2p[s3\$stim %in% c(2:4,10:12)])
s3proto <- mean(s3\$c2p[s3\$stim %in% c(1,9)])
s3new <- mean(s3\$c2p[s3\$stim %in% c(5:8,13:16)])
# Calculate size 10 accuracy
s10 <- dta[dta\$cond == 2,]
s10old <- mean(s10\$c2p[s10\$stim %in% c(2:11,16:26)])
s10proto <- mean(s10\$c2p[s10\$stim %in% c(1,16)])
s10new <- mean(s10\$c2p[s10\$stim %in% c(12:15,27:30)])
# OAT
if(s10new > s3new) oat <- TRUE else oat <- FALSE
# Summary table
res <- cbind(c(s3old,s10old),c(s3proto,s10proto),c(s3new,s10new))
colnames(res) <- c('old','proto','new')
rownames(res) <- c('size3','size10')
# Decide whether to return summary table or OAT boolean
if(xtdo) ret <- res else ret <-oat
return(ret)
}
```

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catlearn documentation built on May 2, 2019, 4:41 p.m.