update_class: Classification update using the Bayes' Theorem

In LTCDM: Latent Transition Cognitive Diagnosis Model with Covariates

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)

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

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

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.\Sexpr[results=rd]{tools:::Rd_expr_doi("10.3102/10769986231163320")}

Examples

## Not run: 
#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

## End(Not run)

LTCDM documentation built on June 22, 2024, 12:27 p.m.