jacobian {pks} | R Documentation |
Jacobian Matrix for Basic Local Independence Model
Description
Computes the Jacobian matrix for a basic local independence model (BLIM).
Usage
jacobian(object, P.K = rep(1/nstates, nstates),
beta = rep(0.1, nitems), eta = rep(0.1, nitems),
betafix = rep(NA, nitems), etafix = rep(NA, nitems))
Arguments
object |
an object of class |
P.K |
the vector of parameter values for probabilities of knowledge states. |
beta |
the vector of parameter values for probabilities of a careless error. |
eta |
the vector of parameter values for probabilities of a lucky guess. |
betafix , etafix |
vectors of fixed error and guessing parameter values;
|
Details
This is a draft version. It may change in future releases.
Value
The Jacobian matrix. The number of rows equals 2^(number of items) - 1, the number of columns equals the number of independent parameters in the model.
References
Heller, J. (2017). Identifiability in probabilistic knowledge structures. Journal of Mathematical Psychology, 77, 46–57. doi:10.1016/j.jmp.2016.07.008
Stefanutti, L., Heller, J., Anselmi, P., & Robusto, E. (2012). Assessing the local identifiability of probabilistic knowledge structures. Behavior Research Methods, 44(4), 1197–1211. doi:10.3758/s13428-012-0187-z
See Also
blim
, simulate.blim
,
gradedness
.
Examples
data(endm)
m <- blim(endm$K2, endm$N.R)
## Test of identifiability
J <- jacobian(m)
dim(J)
qr(J)$rank