Theta.EAP {GGUM}

Estimate thetas and their SEs (GUM, GGUM)

Description

Theta.EAP estimates the person theta parameters via EAP.

Usage

Theta.EAP(IP, SE = TRUE, precision = 4, N.nodes = 30)

Arguments

IP

Object of class GGUM. The GUM/ GGUM estimated item parameters via functions GUM()/ GGUM(), respectively.

SE

Logical value: Estimate the standard errors of the theta estimates? Default is TRUE.

precision

Number of decimal places of the results (default = 4).

N.nodes

Number of nodes for numerical integration (default = 30).

Value

If SE = TRUE, the function returns an N\times 2 matrix with two columns (thetas, SEs), where N is the number of rows in the data matrix (i.e., persons). If SE = FALSE, the function returns the theta estimates as a vector of length N.

Details

The EAP procedure used here is based on Roberts, Donoghue, and Laughlin (2000), namely Equation 25 for the \theta estimates and Equation 26 for corresponding standard errors. The EAP estimate is the posterior mean of the \theta distribution for the corresponding response pattern. The standard error is computed as an approximation to the standard deviation of the posterior distribution. See Roberts et al. (2000) for more details.

Author(s)

Jorge N. Tendeiro, [e-mail]

Examples

# For GUM:
# Generate data
#   (toy example: Too few items (due to computation time constraints) for 
#   accurate estimation of person parameters; larger number of items is 
#   required in practice):
gen1 <- GenData.GGUM(400, 5, 3, "GUM", seed = 139)
# Fit the GUM:
fit1 <- GUM(gen1$data, 3)
# Estimate the theta parameters:
Theta.EAP(fit1)
## Not run: 
# For GGUM:
# Generate data:
set.seed(1); C <- sample(3:5, 10, replace = TRUE)
gen2 <- GenData.GGUM(2000, 10, C, "GGUM", seed = 156)
# Fit the GGUM:
fit2 <- GGUM(gen2$data, C)
# Estimate the theta parameters:
Theta.EAP(fit2)

## End(Not run)