itemNames <- c('GF7', 'P2', 'BP1', 'BP2', 'BP3', 'BP4', 'BP5', 'BP6', 'BP7',
'BP8', 'BP9', 'BP10', 'BP11', 'BP12', 'BP13')
set.seed(6375309)
## Generating random item responses for 8 fake respondents
exampleDat <- t(replicate(8, sample(0:4, size = length(itemNames), replace = TRUE)))
## Making half of respondents missing about 10% of items,
## half missing about 50%.
miss10 <- t(replicate(4, sample(c(0, 9), prob = c(0.9, 0.1),
size = length(itemNames), replace = TRUE)))
miss50 <- t(replicate(4, sample(c(0, 9), prob = c(0.5, 0.5),
size = length(itemNames), replace = TRUE)))
missMtx <- rbind(miss10, miss50)
## Using 9 as the code for missing responses
exampleDat[missMtx == 9] <- 9
exampleDat <- as.data.frame(cbind(ID = paste0('ID', 1:8),
as.data.frame(exampleDat)))
names(exampleDat) <- c('ID', itemNames)
## Returns data frame with scale scores and with original items untouched
scoredDat <- scoreFACT_BP(exampleDat)
names(scoredDat)
scoredDat
## Returns data frame with scale scores, with the appropriate items
## reverse scored, and with item values of 8 and 9 replaced with NA.
## Also illustrates the effect of setting keepNvalid = TRUE.
scoredDat <- scoreFACT_BP(exampleDat, updateItems = TRUE, keepNvalid = TRUE)
names(scoredDat)
## Descriptives of scored scales
summary(scoredDat[, c('BPS')])
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