# generateCovItems

0th

Percentile

##### Generate pairwise comparison data with random correlations between items

If you need access to the correlation matrix used to generate the absolute latent scores then you will need to generate them yourself. This is not difficult. See how in the example.

##### Usage
generateCovItems(df, numItems, th = 0.5, scale = 1, name)
##### Arguments
df

a data frame with pairs of vertices given in columns pa1 and pa2, and item response data in other columns

numItems

how many items to create

th

a vector of thresholds

scale

the scaling constant

name

a vector of item names

##### Details

The pairwise comparison item response model has thresholds and a scale parameter similar to the partial credit model (Masters, 1982). The model is cumbersome to describe in traditional mathematical notation, but the R code is fairly straightforward,

softmax <- function(y) exp(y) / sum(exp(y))cmp_probs <- function(scale, pa1, pa2, thRaw) {
th <- cumsum(thRaw)
diff <- scale * (pa2 - pa1)
unsummed <- c(0, c(diff + rev(th)), c(diff - th), use.names = FALSE)
softmax(cumsum(unsummed))
}


The function cmp_probs takes a scale constant, the latent scores for two objects pa1 and pa2, and a vector of thresholds thRaw. The thresholds are parameterized as the difference from the previous threshold. For example, thresholds c(0.5, 0.5) are not at the same location but are at locations c(0.5, 1.0). Thresholds are symmetric. If there is one thresholds then the model admits three possible response outcomes (e.g. win, tie, and lose). Responses are always stored centered with zero representing a tie. Therefore, it is necessary to add one plus the number of thresholds to response data to index into the vector returned by cmp_probs. For example, if our response data (-1, 0, 1) has one threshold then we would add 2 (1 + 1 threshold) to obtain the indices (1, 2, 3).

Use itemModelExplorer to explore the item model. In this shiny app, the discrimination parameter does what is customary in item response models. However, it is not difficult to show that discrimination is a function of thresholds and scale. That is, discrimination is not an independent parameter and cannot be estimated. In pairwise comparison models, discrimination and measurement error are confounded.

##### References

Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149<U+2013>174. doi: 10.1007/BF02296272

Other item generators: generateFactorItems, generateItem

##### Aliases
• generateCovItems
##### Examples
# NOT RUN {
library(mvtnorm)
df <- twoLevelGraph(letters[1:10], 100)
df <- generateCovItems(df, 3)

# generateCovItems essentially does the same thing as:
numItems <- 3
palist <- unique(c(df$pa1,df$pa2))
trueCor <- cov2cor(rWishart(1, numItems, diag(numItems))[,,1])
theta <- rmvnorm(length(palist), sigma=trueCor)
dimnames(theta) <- list(palist, paste0('i', 3 + 1:numItems))
df <- generateItem(df, theta)

# }

Documentation reproduced from package pcFactorStan, version 0.11, License: GPL (>= 3)

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