gen.data {cdcatR} | R Documentation |
Data generation
Description
This function can be used to generate datasets based on an object of class gen.itembank
.
The user can manipulate the examinees' attribute distribution or provide a matrix of attribute profiles.
Data are simulated using the GDINA::simGDINA
function (Ma & de la Torre, 2020).
Usage
gen.data(
N = NULL,
R = 1,
item.bank = NULL,
att.profiles = NULL,
att.dist = "uniform",
mvnorm.parm = list(mean = NULL, sigma = NULL, cutoffs = NULL),
higher.order.parm = list(theta = NULL, lambda = NULL),
categorical.parm = list(att.prior = NULL),
seed = NULL
)
Arguments
N |
Scalar numeric. Sample size for the datasets |
R |
Scalar numeric. Number of datasets replications. Default is 1 |
item.bank |
An object of class |
att.profiles |
Numeric matrix indicating the true attribute profile for each examinee (N examinees x K attributes). If |
att.dist |
Numeric vector of length 2^K, where K is the number of attributes. Distribution for attribute simulation. It can be |
mvnorm.parm |
A list of arguments for multivariate normal attribute distribution ( |
higher.order.parm |
A list of arguments for higher-order attribute distribution ( |
categorical.parm |
A list of arguments for categorical attribute distribution ( |
seed |
Scalar numeric. A scalar to use with |
Value
gen.data
returns an object of class gen.data
.
- simdat
An array containing the simulated responses (dimensions N examinees x J items x R replicates). If
R = 1
, a matrix is provided- simalpha
An array containing the simulated attribute profiles (dimensions N examinees x K attributes x R replicates). If
R = 1
, a matrix is provided- specifications
A list that contains all the specifications
References
Ma, W. & de la Torre, J. (2020). GDINA: The generalized DINA model framework. R package version 2.7.9. Retrived from https://CRAN.R-project.org/package=GDINA
Examples
####################################
# Example 1. #
# Generate dataset (GDINA item #
# parameters and uniform attribute #
# distribution) #
####################################
Q <- sim180GDINA$simQ
bank <- gen.itembank(Q = Q, mean.IQ = .70, range.IQ = .20, model = "GDINA")
simdata <- gen.data(N = 1000, item.bank = bank)