LTCDM (version 1.0.0)

update_class: Classification update using the Bayes' Theorem

Description

Function to update classifications (attribute profiles) using the Bayes' Theorem

Usage

update_class(cep, K, t, z_t1, z_t2, beta, gamma_01, gamma_10)

Value

a list with elements

post.prob

the corrected and updated posterior probability

att.prevalance

the corrected and updated attribute prevalance

cor.profile

the corrected and updated attribute profiles for different time points

Arguments

cep

estimated classification error probabilities returned from CEP_t. The uncorrected attribute profile (EAP) is also stored in this object.

K

the number of attributes.

t

the number of time points. This package can only handle two time points can for the time being.

z_t1

covariates at Time 1, which has already had the intercept column (1s).

z_t2

covariates at Time 2, which has already had the intercept column (1s).

beta

the estimated regression coefficients at Time 1 (initial state)

gamma_01

the estimated regression coefficients of transition from absence (0) to presence (1) at Time 2

gamma_10

the estimated regression coefficients of transition from absence (0) to presence (1) at Time 2

References

Liang, Q., de la Torre, J., & Law, N. (2023). Latent transition cognitive diagnosis model with covariates: A three-step approach. Journal of Educational and Behavioral Statistics.tools:::Rd_expr_doi("10.3102/10769986231163320")

Examples

if (FALSE) {
#The run is dependent on the output of the step3.est() function and CEP_t() function
#It is not recommended for run it.
t = 2 # the number of time points
K = ncol(Q) # the number of attributes
Z = dat1[, c(1,2)]
z_t1 = cbind(1, Z$gender)  # Covariate at time 1
z_t2 = cbind(1, Z$gender, Z$intervention, apply(Z,1,prod)) # Covariates at time 2
beta = step3.output$beta
gamma_01 = step3.output$gamma_01
gamma_10 = step3.output$gamma_10

# Update classifications using the Bayes' Theorem
updated.class <- update.class(cep = cep, K = K, t = t, z_t1 = z_t1, z_t2 = z_t2,
                              beta = beta, gamma_01 = gamma_01, gamma_10 = gamma_10)

# The corrected and updated attribute prevalance
updated.class$att.prevalance

# The corrected and updated posterior probability
updated.class$post.prob
}