Generate Data for Latent-Trait MPT Models
Generating a data set with known parameter structure using the Trait-MPT. Useful for simulations and robustness checks.
genTraitMPT( N, numItems, eqnfile, restrictions, mean, mu, sigma, rho, warning = TRUE )
N |
number of participants |
numItems |
number of responses per tree (a named vector with tree labels) |
eqnfile |
The (relative or full) path to the file that specifies the MPT model
(standard .eqn syntax). Note that category labels must start with a letter
(different to multiTree) and match the column names of |
restrictions |
Specifies which parameters should be
(a) constant (e.g., |
mean |
named vector of data-generating group means of the individual MPT parameters
on the probability scale.
If the vector is not named, the internal order of parameters is used
(can be obtained using |
mu |
an alternative way to define the group-level means on the latent-probit scale
(i.e., |
sigma |
(named) vector of group standard deviations of individual MPT parameters on the latent probit scale. Default is zero (no person heterogeneity). |
rho |
(named) correlation matrix for individual MPT parameters on the latent probit scale. Must be symmetric and positive definite (e.g., no correlations of 1 or -1 allowed). Default: a diagonal matrix (i.e., zero correlations). |
warning |
whether to show warning in case the naming of data-generating parameters are unnamed or do not match |
This functions implements a two-step sampling procedure. First, the person
parameters on the latent probit-scale are sampled from the multivariate normal
distribution (based on the mean mu = qnorm(mean), the standard deviations
sigma, and the correlation matrix rho).
These person parameters are then transformed to the probability scale using
the probit-link.
In a last step, observed frequencies are sampled for each person using the MPT equations.
Note that the user can generate more complex structures for the latent person parameters,
and then supply these person parameters to the function genMPT.
a list including the generated frequencies per person (data)
and the sampled individual parameters (parameters) on the probit
and probability scale (thetaLatent and theta, respectively).
Klauer, K. C. (2010). Hierarchical multinomial processing tree models: A latent-trait approach. Psychometrika, 75, 70-98.
# Example: Standard Two-High-Threshold Model (2HTM)
EQNfile <- system.file("MPTmodels/2htm.eqn", package="TreeBUGS")
rho <- matrix(c(1,.8,.2,
.8,1,.1,
.2,.1,1), nrow=3)
colnames(rho) <- rownames(rho) <- c("Do","Dn","g")
genDat <- genTraitMPT(N = 100,
numItems = c(Target=250, Lure=250),
eqnfile = EQNfile,
mean = c(Do=.7, Dn=.7, g=.5),
sigma = c(Do=.3, Dn=.3, g=.15),
rho = rho)
head(genDat$data, 3)
plotFreq(genDat$data, eqn=EQNfile)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.