CATSimulator: Library for Simulation of Adaptive Item Selection

Description Usage Arguments Value Examples

Calculate the probability of the given item/score combinations for a range of thetas

1	scoreProbability.scores(item_pars, thetas, scores)

`item_pars`	a matrix of doubles, items by params. Must have columns for $PAR_* matching the $MODEL
`thetas`	a vector of doubles. Various ability levels
`scores`	a vector of integers. A score for each item. Must be same length as nrow(items).

a matrix of doubles, items by thetas, containing the probability of the given item/score combinations for a range of thetas

  simulation = readRDS(system.file("example/passage-optimal.rds", package = "CATSimulator"))
  item_pars = as.matrix(simulation$itempool[,startsWith(colnames(simulation$itempool), "PAR_")])
  dimnames(item_pars) = list(simulation$itempool$ITEM_ID, colnames(simulation$itempool)[startsWith(colnames(simulation$itempool), "PAR_")])
  thetas = seq(-2, 2, by = 0.5)
  scores = rep(0, nrow(item_pars)) # sample(0:1, nrow(item_pars), replace = TRUE)
  scoreProb = scoreProbability.scores(item_pars, thetas, scores)
  options(digits=8, scipen=8)
  > scoreProb     -2       -1.5         -1       -0.5           0         0.5           1         1.5           2
  item001 0.58809834 0.50372283 0.41558368 0.32888841 0.249089805 0.180546552 0.125536324 0.084083112 0.054534047
  item002 0.59190964 0.51613679 0.43669503 0.35733888 0.282200703 0.214963841 0.158113716 0.112580468 0.077870790
  item003 0.94513409 0.91771404 0.87678454 0.81655990 0.730749143 0.615846666 0.477205951 0.333137033 0.208115753
  item004 0.83947786 0.79814254 0.74606866 0.68095351 0.600952839 0.506025087 0.399962837 0.291827215 0.194203063
  item005 0.86416067 0.82918801 0.78442626 0.72729225 0.655279966 0.567109542 0.464703815 0.355122538 0.250122827
  item006 ...391 total items/rows