updating {mycaas} | R Documentation |
Updating rules
Description
Multiplicative rule as in Falmagne and Doignon 2010 (Chapter 13, Section 10)
Usage
updating(likelihood, states, q, r_q, beta, eta)
Arguments
likelihood |
A vector of the likelihood distribution on the states in the structure. |
states |
A state-by-problem matrix representing the structure, where an element is one if the item is included in the state, and zero otherwise. |
q |
Last administered item |
r_q |
Observed response to item q |
beta |
Vector of careless error probabilities |
eta |
Vector of lucky guess error probabilities |
Value
The updated likelihood distribution on the knowledge states
Examples
# Let consider the knowledge space and the parameters used in Brancaccio,
# de Chiusole, Stefanutti (2023) in Example 1
states<-matrix(c( 0,0,0,0,0,
0,0,0,0,1,
0,0,1,0,1,
0,0,0,1,1,
0,0,1,1,1,
1,0,1,0,1,
0,1,0,1,1,
1,0,1,1,1,
0,1,1,1,1,
1,1,0,1,1,
1,1,1,1,1), byrow=TRUE, ncol=5)
beta <-c(.004,.03,.02,.01,.007)
eta <-c(5e-06, 5e-05, 4e-05,.007,.08)
likelihood_0 <-rep(1,nrow(states))/nrow(states)
# Item asked
q = 3
# response observed
r_q = 1
likelihood_1 <- updating(likelihood_0,states,q,r_q,beta,eta)
[Package mycaas version 0.0.1 Index]