| key2binary {mirt} | R Documentation |
The key2binary function will convert response pattern data to a
dichotomous format, given a response key.
key2binary(fulldata, key, score_missing = FALSE)
fulldata |
an object of class |
key |
a vector or matrix consisting of the 'correct' response to the items. Each
value/row corresponds to each column in |
score_missing |
logical; should missing data elements be returned as incorrect (i.e., 0)?
If |
Returns a numeric matrix with all the response patterns in dichotomous format
Phil Chalmers rphilip.chalmers@gmail.com
Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06
data(SAT12)
head(SAT12)
## Item.1 Item.2 Item.3 Item.4 Item.5 Item.6 Item.7 Item.8 Item.9 Item.10
## 1 1 4 5 2 3 1 2 1 3 1
## 2 3 4 2 8 3 3 2 8 3 1
## 3 1 4 5 4 3 2 2 3 3 2
## 4 2 4 4 2 3 3 2 4 3 2
## 5 2 4 5 2 3 2 2 1 1 2
## 6 1 4 3 1 3 2 2 3 3 1
## Item.11 Item.12 Item.13 Item.14 Item.15 Item.16 Item.17 Item.18 Item.19
## 1 2 4 2 1 5 3 4 4 1
## 2 2 8 2 1 5 2 4 1 1
## 3 2 1 3 1 5 5 4 1 3
## 4 2 4 2 1 5 2 4 1 3
## 5 2 4 2 1 5 4 4 5 1
## 6 2 3 2 1 5 5 4 4 1
## Item.20 Item.21 Item.22 Item.23 Item.24 Item.25 Item.26 Item.27 Item.28
## 1 4 3 3 4 1 3 5 1 3
## 2 4 3 3 8 1 8 4 1 4
## 3 4 3 3 1 1 3 4 1 3
## 4 4 3 1 5 2 5 4 1 3
## 5 4 3 3 3 1 1 5 1 3
## 6 4 3 3 4 1 1 4 1 4
## Item.29 Item.30 Item.31 Item.32
## 1 1 5 4 5
## 2 5 8 4 8
## 3 4 4 4 1
## 4 4 2 4 2
## 5 1 2 4 1
## 6 2 3 4 3
key <- c(1,4,5,2,3,1,2,1,3,1,2,4,2,1,5,3,4,4,1,4,3,3,4,1,3,5,1,3,1,5,4,5)
dicho.SAT12 <- key2binary(SAT12, key)
head(dicho.SAT12)
## Item.1 Item.2 Item.3 Item.4 Item.5 Item.6 Item.7 Item.8 Item.9 Item.10
## [1,] 1 1 1 1 1 1 1 1 1 1
## [2,] 0 1 0 0 1 0 1 0 1 1
## [3,] 1 1 1 0 1 0 1 0 1 0
## [4,] 0 1 0 1 1 0 1 0 1 0
## [5,] 0 1 1 1 1 0 1 1 0 0
## [6,] 1 1 0 0 1 0 1 0 1 1
## Item.11 Item.12 Item.13 Item.14 Item.15 Item.16 Item.17 Item.18 Item.19
## [1,] 1 1 1 1 1 1 1 1 1
## [2,] 1 0 1 1 1 0 1 0 1
## [3,] 1 0 0 1 1 0 1 0 0
## [4,] 1 1 1 1 1 0 1 0 0
## [5,] 1 1 1 1 1 0 1 0 1
## [6,] 1 0 1 1 1 0 1 1 1
## Item.20 Item.21 Item.22 Item.23 Item.24 Item.25 Item.26 Item.27 Item.28
## [1,] 1 1 1 1 1 1 1 1 1
## [2,] 1 1 1 0 1 0 0 1 0
## [3,] 1 1 1 0 1 1 0 1 1
## [4,] 1 1 0 0 0 0 0 1 1
## [5,] 1 1 1 0 1 0 1 1 1
## [6,] 1 1 1 1 1 0 0 1 0
## Item.29 Item.30 Item.31 Item.32
## [1,] 1 1 1 1
## [2,] 0 0 1 0
## [3,] 0 0 1 0
## [4,] 0 0 1 0
## [5,] 1 0 1 0
## [6,] 0 0 1 0
# multiple scoring keys
key2 <- cbind(c(1,4,5,2,3,1,2,1,3,1,2,4,2,1,5,3,4,4,1,4,3,3,4,1,3,5,1,3,1,5,4,5),
c(2,3,NA,1,rep(NA, 28)))
dicho.SAT12 <- key2binary(SAT12, key2)
# keys from raw character responses
resp <- as.data.frame(matrix(c(
"B","B","D","D","E",
"B","A","D","D","E",
"B","A","D","C","E",
"D","D","D","C","E",
"B","C","A","D","A"), ncol=5, byrow=TRUE))
key <- c("B", "D", "D", "C", "E")
d01 <- key2binary(resp, key)
head(d01)
## V1 V2 V3 V4 V5
## [1,] 1 0 1 0 1
## [2,] 1 0 1 0 1
## [3,] 1 0 1 1 1
## [4,] 0 1 1 1 1
## [5,] 1 0 0 0 0
# score/don't score missing values
resp[1,1] <- NA
d01NA <- key2binary(resp, key) # without scoring
d01NA
## V1 V2 V3 V4 V5
## [1,] NA 0 1 0 1
## [2,] 1 0 1 0 1
## [3,] 1 0 1 1 1
## [4,] 0 1 1 1 1
## [5,] 1 0 0 0 0
d01 <- key2binary(resp, key, score_missing = TRUE) # with scoring
d01
## V1 V2 V3 V4 V5
## [1,] 0 0 1 0 1
## [2,] 1 0 1 0 1
## [3,] 1 0 1 1 1
## [4,] 0 1 1 1 1
## [5,] 1 0 0 0 0