R Under development (unstable) (2025-11-24 r89059 ucrt) -- "Unsuffered Consequences"
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> # set.seed(123)
> # 
> # ## for the dataset ECPE
> # library(CDM)
> # Q <- data.ecpe$q.matrix
> # Y <- data.ecpe$data[, -1]
> # 
> # # library(openxlsx)
> # # wb <- createWorkbook()
> # # addWorksheet(wb, "Q_matrix")
> # # writeData(wb, sheet = "Q_matrix", x = Q)
> # # addWorksheet(wb, "Response_Data")
> # # writeData(wb, sheet = "Response_Data", x = Y)
> # # saveWorkbook(wb, file = "ECPE_data.xlsx", overwrite = TRUE)
> # 
> # print(Q)
> # 
> # ######## CDM ########
> # library(Qval)
> # 
> # is.Qident(Q)
> # 
> # CDM.obj <- CDM(Y, Q, model = "GDINA", mono.constraint = TRUE)
> # summary(CDM.obj)
> # 
> # plot(CDM.obj)
> # 
> # 
> # ######## validation with CDM ########
> # Qval.obj <- validation(Y, Q, CDM.obj=CDM.obj,
> #                        method="GDI", iter.level="no")
> # 
> # 
> # ######## GDI ########
> # Qval.GDI.obj <- validation(Y, Q, model="GDINA",
> #                            mono.constraint = TRUE,
> #                            method="GDI", search.method="SSA",
> #                            iter.level="test.att", maxitr=150,
> #                            verbose=1)
> # summary(Qval.GDI.obj)
> # 
> # 
> # ######## Hull ########
> # Qval.Hull.obj <- validation(Y, Q, model="GDINA",
> #                             mono.constraint = TRUE,
> #                             method="Hull", criter="PVAF",
> #                             search.method="ESA",
> #                             iter.level="item", maxitr=150,
> #                             verbose=1)
> # 
> # Q.Hull.sug <- extract(Qval.Hull.obj, "Q.sug")
> # head(Q.Hull.sug)
> # 
> # 
> # ######## Hull Plot ########
> # plot(Qval.Hull.obj, i=1)
> # 
> # 
> # ######## Wald ########
> # Qval.Wald.obj <- validation(Y, Q, model="GDINA",
> #                             mono.constraint = TRUE,
> #                             method="Wald", alpha.level=0.05,
> #                             search.method="stepwise",
> #                             iter.level="no", verbose=1)
> # Q.Wald.sug <- extract(Qval.Wald.obj, "Q.sug")
> # head(Q.Wald.sug)
> # 
> # 
> # ######## fit ########
> # fit.obj1 <- fit(Y, Q, model="GDINA")
> # summary(fit.obj1)
> # 
> # Qval.MLR.obj <- validation(Y, Q, model="GDINA",
> #                            mono.constraint = TRUE,
> #                            method="MLR-B",
> #                            search.method="PAA",
> #                            iter.level="test",
> #                            maxitr=20, verbose=1)
> # Q.MLR.sug <- extract(Qval.MLR.obj, "Q.sug")
> # 
> # fit.obj2 <- fit(Y, Q.MLR.sug, model="GDINA")
> # summary(fit.obj2)
> # 
> # 
> # ######## beta ########
> # Qval.beta.obj <- validation(Y, Q, model="GDINA",
> #                             mono.constraint = TRUE,
> #                             method="beta",
> #                             search.method="PAA",
> #                             criter="AIC",
> #                             iter.level="no", verbose=1)
> # Q.beta.sug <- extract(Qval.beta.obj, "Q.sug")
> # head(Q.beta.sug)
> # 
> # ############################## different fit between Q-matrices ##############################
> # fit.original.obj <- fit(Y, Q, model="GDINA")
> # fit.GDI.obj <- fit(Y, Qval.GDI.obj$Q.sug, model="GDINA")
> # fit.Hull.obj <- fit(Y, Q.Hull.sug, model="GDINA")
> # fit.Wald.obj <- fit(Y, Q.Wald.sug, model="GDINA")
> # fit.MLR.obj <- fit(Y, Q.MLR.sug, model="GDINA")
> # fit.beta.obj <- fit(Y, Q.beta.sug, model="GDINA")
> # 
> # res <- rbind(unlist(fit.original.obj[-c(9, 10)]), unlist(fit.GDI.obj[-c(9, 10)]), 
> #              unlist(fit.Hull.obj[-c(9, 10)]), unlist(fit.Wald.obj[-c(9, 10)]), 
> #              unlist(fit.MLR.obj[-c(9, 10)]), unlist(fit.beta.obj[-c(9, 10)]))
> # print(res)
> # 
> # 
> # ############################# Hull and experts ################################
> # summary(Qval.Hull.obj)
> # 
> # is.Qident(Q.Hull.sug)
> # 
> # ######## PVAF ########
> # PVAF <- get.PVAF(Y, Q.Hull.sug, model="GDINA",
> #                  mono.constraint = TRUE)
> # PVAF <- round(PVAF, 3)
> # head(PVAF)
> # 
> # ######## R2 ########
> # R2 <- get.R2(Y, Q.Hull.sug, model="GDINA", mono.constraint = TRUE)
> # R2 <- round(R2, 3)
> # R2[c(4, 6), ]
> # 
> # ######## beta ########
> # beta <- get.beta(Y, Q.Hull.sug, model="GDINA", mono.constraint = TRUE)
> # beta <- round(beta, 3)
> # beta[c(4, 6), ]
> # 
> # ######## priority ########
> # priority <- get.priority(Y, Q.Hull.sug, model="GDINA", mono.constraint = TRUE)
> # priority <- round(priority, 3)
> # priority[c(4, 6), ]
> # 
> # ######## Wald test ########
> # CDM.obj <- CDM(Y, Q.Hull.sug, model="GDINA", mono.constraint = TRUE)
> # q1 <- c(0, 0, 1)
> # q2 <- c(1, 0, 1)
> # Wald.test(CDM.obj, q1, q2, i=4)
> # Wald.test(CDM.obj, q1, q2, i=6)
> # 
> # ######## att.hierarchy ########
> # hiera.obj <- att.hierarchy(Y, Q, model="GDINA", 
> #                            mono.constraint = TRUE)
> # summary(hiera.obj)
> # 
> # hiera.Hull.obj <- att.hierarchy(Y, Q.Hull.sug, 
> #                                 model="GDINA", 
> #                                 mono.constraint = TRUE)
> # summary(hiera.Hull.obj)
> 
> proc.time()
   user  system elapsed 
   0.15    0.04    0.14