item_residual: Item score residual

In woshikaqia/MIRTutils: MIRT Utility Package

Item score residual

Description

Compute item score residual (i.e., item score - expected item score. Item score for a testlet model item is the summed raw score of all assertions in the testlet, and expected item score is expected raw score calculated with Lord-Wingersky algorithm (See ?lord_wing for details about the algorithm).

Usage

item_residual(
  theta,
  SA_dat = NULL,
  Cluster_dat = NULL,
  SA_parm = NULL,
  Cluster_parm = NULL,
  Dv = 1,
  n.nodes = 21,
  missing_as_incorrect = F
)

Arguments

theta	a scalar or a vector of student ability
SA_dat	For one student, a vector of response to standalone items. For more than one student, a matrix or dataframe of response to standalone items. One assertion per column. Column order must match row order in SA_parm. Use NA for missing responses
Cluster_dat	For one student, a vector of response to cluster items. For more than one student, a matrix or dataframe of response to cluster items. One assertion per column. Column order must match row order in Cluster_parm. Use NA for missing responses.
SA_parm	a matrix or dataframe of item parameters for standalone items, where columns are a (slope), b1, b2, ..., b_k (difficulty or step difficulty), g (guessing), ItemID, and AssertionID. Columns must follow the above order. See example_SA_parm for an example. Use ?example_SA_parm for detailed column descriptions
Cluster_parm	a matrix or dataframe of item parameters for cluster items, where columns are a (slope), b (difficulty), cluster variance, cluster position, ItemID, and AssertionID. Columns must follow the above order. See example_Cluster_parm for an example. Use ?example_Cluster_parm for detailed column descriptions
Dv	scaling factor for IRT model (usually 1 or 1.7)
n.nodes	number of nodes used when integrating out the specific dimension
missing_as_incorrect	by default, missings (NAs) are treated as missing; if TRUE, missings are treated as incorrect

Note

If the test does not have SA items or Cluster items, use default (NULL) for the corresponding data and parameter arguments

Author(s)

Zhongtian Lin [e-mail]

Examples

data(example_SA_parm)
data(example_Cluster_parm)
sigma <- diag(c(1, sqrt(unique(example_Cluster_parm$cluster_var))))
mu <- rep(0, nrow(sigma))
thetas <- MASS::mvrnorm(7,mu,sigma)
thetas[,1] <- seq(-3,3,1) #overall dimension theta values
itmDat <- sim_data(thetas = thetas, SA_parm = example_SA_parm, Cluster_parm = example_Cluster_parm)
SA_dat <- itmDat[,1:20]
Cluster_dat <- itmDat[,-1:-20]
rst <- item_residual(thetas[,1], SA_dat, Cluster_dat, example_SA_parm, example_Cluster_parm, n.nodes = 11)

woshikaqia/MIRTutils documentation built on Aug. 21, 2024, 4:30 p.m.