sim {psych} | R Documentation |
A number of functions in the psych package will generate simulated data. These functions include
link{sim}
(for a simplex), sim.circ
for a circumplex structure, sim.congeneric
(for a one factor factor congeneric model), sim.dichot
(to simulate dichotomous items), sim.hierarchical
(a hierarchical factor model), sim.item
(general item simulations), sim.structural
(general simulation of structural models), and sim.VSS
. These functions are separately documented and are listed here for ease of the help function. See each function for more detailed help.
sim(fx=NULL,Phi=NULL,fy=NULL,n=0,mu=NULL,raw=FALSE)
fx |
The measurement model for x. If NULL, a 4 factor model is generated |
Phi |
The structure matrix of the latent variables |
fy |
The measurement model for y |
mu |
The means structure for the fx factors |
n |
Number of cases to simulate. If n=0 or NULL, the population matrix is returned. |
raw |
if raw=TRUE, raw data are returned as well. |
Simulation of data structures is a very useful tool in psychometric research and teaching. By knowing ``truth" it is possible to see how well various algorithms can capture it.
The default values for sim.structure
is to generate a 4 factor, 12 variable data set with a simplex structure between the factors.
Other simulation functions in psych are:
sim.structure
A function to combine a measurement and structural model into one data matrix. Useful for understanding structural equation models.
sim.congeneric
A function to create congeneric items/tests for demonstrating classical test theory. This is just a special case of sim.structure.
sim.hierarchical
A function to create data with a hierarchical (bifactor) structure.
sim.item
A function to create items that either have a simple structure or a circumplex structure.
sim.circ
Create data with a circumplex structure.
sim.dichot
Create dichotomous item data with a simple or circumplex structure.
William Revelle
Revelle, W. (in preparation) An Introduction to Psychometric Theory with applications in R. Springer. at http://personality-project.org/r/book/
See above
simplex <- sim() round(simplex$model,2) congeneric <- sim.congeneric() round(congeneric,2) R <- sim.hierarchical() R fx <- matrix(c(.9,.8,.7,rep(0,6),c(.8,.7,.6)),ncol=2) fy <- c(.6,.5,.4) Phi <- matrix(c(1,0,.5,0,1,.4,0,0,0),ncol=3) print(sim.structure(fx,Phi,fy,),digits=2)