# Posterior predictive checking for nested partially latent class models - pairwise log odds ratio (only for bronze-standard data) At each MCMC iteration, we generate a new data set based on the model and parameter values at that iteration. The sample size of the new data set equals that of the actual data set, i.e. the same number of cases and controls. ```r plot_check_pairwise_SLORD(DIR_NPLCM, slice = 1) ``` ## Arguments - `DIR_NPLCM`: File path to the folder that stores results from npLCM fit. - `slice`: Default is 1, for the first slice of BrS data. ## Returns A figure of posterior predicted log odds ratio compared with the observed log odds ratio for the BrS data. The function generates this figure in your working directory automatically. ## Examples ```r data(data_nplcm_noreg) cause_list <- LETTERS[1:6] J.BrS <- 6 model_options_no_reg <- list( likelihood = list( cause_list = cause_list, k_subclass = 2, Eti_formula = ~-1, # no covariate for the etiology regression FPR_formula = list( MBS1 = ~-1) # no covariate for the subclass weight regression ), use_measurements = c("BrS"), # use bronze-standard data only for model estimation. prior= list( Eti_prior = overall_uniform(1,cause_list), # Dirichlet(1,...,1) prior for the etiology. TPR_prior = list(BrS = list( info = "informative", # informative prior for TPRs input = "match_range", # specify the informative prior for TPRs by specifying a plausible range. val = list(MBS1 = list(up = list(rep(0.99,J.BrS)), # upper ranges: matched to 97.5% quantile of a Beta prior low = list(rep(0.55,J.BrS)))) # lower ranges: matched to 2.5% quantile of a Beta prior ) ) ) ) set.seed(1) # include stratification information in file name: thedir <- paste0(tempdir(),"_no_reg") # create folders to store the model results dir.create(thedir, showWarnings = FALSE) result_folder_no_reg <- file.path(thedir,paste("results",collapse="_")) thedir <- result_folder_no_reg dir.create(thedir, showWarnings = FALSE) # options for MCMC chains: mcmc_options_no_reg <- list( debugstatus = TRUE, n.chains = 1, n.itermcmc = as.integer(200), n.burnin = as.integer(100), n.thin = 1, individual.pred = FALSE, ppd = TRUE, result.folder = thedir, bugsmodel.dir = thedir ) BrS_object_1 <- make_meas_object(patho = LETTERS[1:6], specimen = "MBS", test = "1", quality = "BrS", cause_list = cause_list) clean_options <- list(BrS_objects = make_list(BrS_object_1)) # place the nplcm data and cleaning options into the results folder dput(data_nplcm_noreg,file.path(thedir,"data_nplcm.txt")) dput(clean_options, file.path(thedir, "data_clean_options.txt")) rjags::load.module("glm") nplcm_noreg <- nplcm(data_nplcm_noreg,model_options_no_reg,mcmc_options_no_reg) plot_check_pairwise_SLORD(nplcm_noreg$DIR_NPLCM,slice=1) ``` ## See Also Other visualization functions: `plot.nplcm()`, `plot_BrS_panel()`, `plot_SS_panel()`, `plot_check_common_pattern()`, `plot_etiology_regression()`, `plot_etiology_strat()`, `plot_panels()`, `plot_pie_panel()`, `plot_subwt_regression()`

plot_check_pairwise_SLORD function