/
nplcm.R
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/
nplcm.R
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if(getRversion() >= "2.15.1") utils::globalVariables(c("set_prior_tpr","set_prior_eti"))
#' Fit nested partially-latent class models (highest-level wrapper function)
#'
#' Uses `JAGS` (OSX or Windows) operating system for Bayesian posterior inference
#' (see `README` file for an instruction to install `JAGS`). If running `JAGS` on windows,
#' please go to control panel to add the directory to `JAGS` into ENVIRONMENTAL VARIABLE.
#'
#' @param data_nplcm Cases are on top of controls in the rows of diagnostic
#' test results and the covariate matrix. This is assumed by `baker` to automatically
#' write model files (`.bug`).
#' \itemize{
#' \item `Mobs` A list of measurements of distinct qualities (Bronze-, Silver, and Gold-Standard:
#' `MBS`,`MSS`,`MGS`). The elements of the list
#' should include `MBS`, `MSS`, and `MGS`. If any of the component
#' is not available, please specify it as, e.g., `MGS=NULL`
#' (effectively deleting `MGS` from `Mobs`).
#' \itemize{
#' \item `MBS` a list of data frame of bronze-standard (BrS) measurements.
#' For each data frame (referred to as a 'slice'),
#' rows are subjects, columns are causative agents (e.g., pathogen species).
#' We use `list` here to accommodate the possibility of multiple sets of BrS data.
#' They have imperfect sensitivity/specificity (e.g. nasopharyngeal polymerase chain
#' reaction - NPPCR).
#' \item `MSS` a list of data frame of silver-standard (SS) measurements.
#' Rows are subjects, columns are causative agents measured in specimen (e.g. blood culture).
#' These measurements have perfect specificity but imperfect sensitivity.
#' \item `MGS` a list of data frame of gold-standard (GS) measurements.
#' Rows are subject, columns are measured causative agents
#' These measurements have perfect sensitivity and specificity.
#' }
#'
#' \item `Y` Vector of disease status: `1` for case, `0` for control.
#' \item `X` Covariate matrix. A subset of columns are primary covariates in cause-specific-
#' case-fraction (CSCF) functions and hence must be available for cases, and another subset
#' are covariates that are available in the cases and the controls.
#' The two sets of covariates may be identical, overlapping or completely different.
#' In general, this is not the design matrix for regression models,
#' because for enrollment date in a study which may have non-linear effect,
#' basis expansion is often needed for approximation.
#' }
#'
#' @param model_options A list of model options: likelihood and prior.
#' \describe{
#' \item{`use_measurements`}{
#' A vector of characters strings; can be one or more from `"BrS"`, `"SS"`, `"GS"`.
#' }
#' \item{`likelihood`}{
#' \describe{
#' \item{cause_list}{ The vector of causes (NB: specify);}
#' \item{k_subclass}{ The number of nested subclasses in each
#' disease class (one of case classes or the control class; the same `k_subclass`
#' is assumed for each class) and each slice of BrS measurements.
#' `1` for conditional independence; larger than `1` for conditional dependence.
#' It is only available for BrS measurements. It is a vector of length equal to
#' the number of slices of BrS measurements;}
#' \item{Eti_formula}{ Formula for etiology regressions. You can use
#' [s_date_Eti()] to specify the design matrix for `R` format enrollment date;
#' it will produce natural cubic spline basis. Specify `~ 1` if no regression is intended.}
#' \item{FPR_formula}{formula for false positive rates (FPR) regressions; see [formula()].
#' You can use [s_date_FPR()] to specify part of the design matrix for `R`
#' format enrollment date; it will produce penalized-spline basis (based on B-splines).
#' Specify `~ 1` if no regression is intended. (NB: If `effect="fixed"`, [dm_Rdate_FPR()]
#' will just specify a design matrix with appropriately standardized dates.)}
#' }
#' }
#'
#' \item{`prior`}{
#' \describe{
#' \item{Eti_prior}{Description of etiology prior (e.g., `overall_uniform` -
#' all hyperparameters are `1`; or `0_1` - all hyperparameters are `0.1`);}
#' \item{TPR_prior}{Description of priors for the measurements
#' (e.g., informative vs non-informative). Its length should be the
#' same as `use_measurements` above. Please see examples for how to specify.
#' The package can also handle multiple slices of BrS, SS data, so separate
#' specification of the TPR priors are needed.
#' }
#' }
#' }
#' }
#' @param mcmc_options A list of Markov chain Monte Carlo (MCMC) options.
#' \itemize{
#' \item `debugstatus` Logical - whether to pause WinBUGS after it finishes
#' model fitting; (NB: is this obsolete? Test.)
#' \item `n.chains` Number of MCMC chains;
#' \item `n.burnin` Number of burn-in iterations;
#' \item `n.thin` To keep every other `n.thin` samples after burn-in period;
#' \item `individual.pred` `TRUE` to perform individual prediction (`Icat`
#' variables in the `.bug` file); `FALSE` otherwise;
#' \item `ppd` `TRUE` to simulate new data (`XXX.new`
#' variables in the `.bug` file) from the posterior predictive distribution (ppd);
#' `FALSE` otherwise;
#' \item `get.pEti` `TRUE` for getting posterior samples of individual etiologic fractions;
#' `FALSE` otherwise. For non-regression, or regression models with all discrete predictors,
#' by default this is `TRUE`, so no need to specify this entry. It is only relevant for regression models
#' with non-discrete covariates. Because individuals have distinct CSCFs at their specific covariate values,
#' it's easier to just store the posterior samples of the regression coefficients and reconstruct the pies afterwards,
#' rather than storing them through `JAGS`.
#' \item `result.folder` Path to folder storing the results;
#' \item `bugsmodel.dir` Path to `.bug` model files;
#' \item `jags.dir` Path to where JAGS is installed; if `NULL`, this will be set
#' to `jags.dir=""`.
#' }
#' @return A `JAGS` output result, fitted by function [R2jags::jags2()] from `R2jags`.
#' It is an object of class `nplcm` and `bugs`.
#' Current implemented models follow the hierarchy below:
#' \itemize{
#' \item no regression: Fitted by at low level by [nplcm_fit_NoReg]
#'
#' \item regression:
#' Given disease class (control or a class of cases with the same subset of causative agents):
#' \itemize{
#' \item local independence model for BrS measures:
#' Fitted at lower level by
#' \itemize{
#' \item [nplcm_fit_Reg_NoNest] deals with the setting with two sets of covariates,
#' one for CSCF regression and the other for FPR regression. The two sets of
#' covariates may be identical, overlapping or non-overlapping.
#' This function is called when there exists one or more than one discrete covariate among
#' the union of the two covariate sets. The method implemented by this function
#' directly lets FPR depend upon covariates.
#' This is different from Wu and Chen (2021), which let the subclass
#' weights depend upon covariates. We implemented this function for methods comparison.
#' \item [nplcm_fit_Reg_discrete_predictor_NoNest] deals with the setting
#' with all discrete covariates for FPRs and CSCFs. The strata defined by the two sets of
#' covariates need not be identical, e.g., as a result of distinct sets of covariates. Again,
#' this is directly to let FPR be stratified by covariates, hence different from Wu and Chen (2020+)
#' We implemented this function for methods comparison.
#' }
#' \item local dependence model for BrS measures:
#' Fitted at lower level by [nplcm_fit_Reg_Nest]: This is the method introduced in
#' Wu and Chen (2021): CSCF regression + case/control subclass weight regression.
#' It does not provide a specialized function for the setting with all discrete covariates.
#' }
#' }
#'
#' @examples
#'
#' \donttest{
#' 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 = TRUE, # <- must set to TRUE! <------- NOTE!
#' ppd = FALSE,
#' 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)
#'
#'
#' }
#'
#'
#' @export
nplcm <- function(data_nplcm,model_options,mcmc_options){
mcmc_options$use_jags <- TRUE
Mobs <- data_nplcm$Mobs
Y <- data_nplcm$Y
if (length(rle(Y)[["values"]])>2 | Y[1]!=1) {
stop("==[baker] 'data_nplcm$Y' must have cases on top of controls.
Use 'baker::subset_data_nplcm_by_index()' to shuffle the rows. Then retry.==\n")}
X <- data_nplcm$X
parsed_model <- assign_model(model_options,data_nplcm)
do_reg_vec <- unlist(parsed_model$regression[grep("^do_reg_",names(parsed_model$regression))])
do_discrete_vec <- unlist(parsed_model$regression[grep("^is_discrete_predictor",names(parsed_model$regression))])
do_discrete_and_reg <- do_reg_vec&do_discrete_vec
do_reg <- any(do_reg_vec)
do_discrete <- sum(do_discrete_and_reg)==sum(do_reg_vec)
# only call nplcm_fit_Reg_discrete_predictor_NoNest
# when ALL regressions use discrete predictors!
do_nested <- parsed_model$nested
fitted_type <- NA
if(!do_reg){
res <- nplcm_fit_NoReg(data_nplcm,model_options,mcmc_options)
fitted_type <- "no_reg"
}
if (do_reg & !any(do_nested)){
if (do_discrete){
fitted_type <- "reg_nonest_strat"
# when every regression is a regression upon discrete variables;
# when it is not a regression, the fitting function treats it as a single stratum
# when specifying the model in the .bug file (in the assign_model function, ~1
# is considered not a regression, i.e., covariate independent)
res <- nplcm_fit_Reg_discrete_predictor_NoNest(data_nplcm,model_options,mcmc_options)
} else{
fitted_type <- "reg_nonest"
# a mix of discrete and continuous regressions:
res <- nplcm_fit_Reg_NoNest(data_nplcm,model_options,mcmc_options)
}
}
if (do_reg & any(do_nested)){
if (do_discrete) {fitted_type <- "reg_nest_strat"}else{fitted_type <- "reg_nest"}
res <- nplcm_fit_Reg_Nest(data_nplcm,model_options,mcmc_options)
}
res$DIR_NPLCM <- mcmc_options$result.folder # <--- add info about where results are stored.
res$fitted_type <- fitted_type
class(res) <- c("nplcm","bugs")
return(res)
}
#' Interpret the specified model structure
#'
#' `assign_model` translates options specified by a user (e.g., in
#' `model_options`) into information that can be understood by `baker`.
#'
#' @details `assign_model` will be modified to check if data are conformable
#' to specified model.
#'
#' @param data_nplcm Data. See [nplcm()] function for data structure.
#' @param model_options See [nplcm()] function.
#' @param silent Default is `TRUE` for no messages; `FALSE` otherwise.
#' @return A list of model specifications:
#' \itemize{
#' \item `num_slice` A vector counting the No. of measurement slices for each
#' level of measurement quality (e.g., MBS, MSS, MGS representing
#' Bronze-Standard Measurements - case-control,
#' Silver-Standard Measurements and Gold-Standard
#' Measurements - case-only);
#' \item `nested` Local dependence specification for modeling bronze-standard
#' data. `TRUE` for nested models (conditional dependence given disease class);
#' `FALSE` for non-nested models (conditional independence given disease class).
#' One for each BrS slice.
#' \item `regression`
#' \itemize{
#' \item `do_reg_Eti` `TRUE` for doing etiology regression.
#' It means let the etiology fractions vary with explanatory variables.
#' `FALSE` otherwise;
#' \item `do_reg_FPR` A vector whose names represent the slices
#' of bronze-standard data. For each slice of BrS measurements,
#' `TRUE` does false positive rate regression. It means the false
#' positive rates, estimatable from controls, can vary with
#' covariates; `FALSE` otherwise.
#' \item `is_discrete_predictor` A list of names "Eti", and
#' the names for every slice of bronze-standard data. `TRUE`
#' if all predictors are discrete; `FALSE` otherwise.
#' }
#' }
#'
#' @examples
#' cause_list <- c(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
#' )
#' )
#' )
#' )
#' data("data_nplcm_noreg")
#'
#' assign_model(model_options_no_reg,data_nplcm_noreg)
#'
#' @family specification checking functions
#' @export
assign_model <- function(model_options,data_nplcm, silent=TRUE){
# load options:
likelihood <- model_options$likelihood
use_measurements <- model_options$use_measurements
prior <- model_options$prior
# load data:
Mobs <- data_nplcm$Mobs
Y <- data_nplcm$Y
X <- data_nplcm$X
nested <- likelihood$k_subclass > 1
# test the match between actual data and model_options:
use_data_sources <- c("MBS","MSS","MGS")[lookup_quality(use_measurements)]
input_data_sources <- names(Mobs)
if (!all(use_data_sources%in%input_data_sources)){
stop("==[baker] Please supply actual datasets as specified by 'use_measurements' in 'model_options'.==\n")
}
# get the length of each measurement quality:
num_slice <- rep(0,3)
names(num_slice) <- c("MBS","MSS","MGS")
for (i in seq_along(use_data_sources)){
num_slice[use_data_sources[i]] <- length(Mobs[[use_data_sources[i]]])
}
# specify regression for FPR: (only available for bronze-standard data. Silver-standard data automatically have FPR==0.)
do_reg_FPR <- is_discrete_FPR <- rep(NA,length(likelihood$FPR_formula)) # <---- a regression for each measurement slice?
names(do_reg_FPR) <- names(is_discrete_FPR) <- names(likelihood$FPR_formula)
for (i in seq_along(Mobs$MBS)){
ind_tmp <-which(names(likelihood$FPR_formula) == names(Mobs$MBS)[i])
form_tmp <- stats::as.formula(likelihood$FPR_formula[[ind_tmp]])
if (!length(ind_tmp)) { # don't do regression if no regression formula is found:
do_reg_FPR[i] <- FALSE
} else{ # do regression if there is matched regression formula:
do_reg_FPR[i] <-
parse_nplcm_reg(form_tmp,data_nplcm,silent=silent)
}
is_discrete_FPR[i] <- FALSE
if (!is.null(X)){
is_discrete_FPR[i] <- (!is_intercept_only(form_tmp) &
!is_intercept_only(form_tmp)&
is_discrete(data.frame(X,Y), form_tmp))
}
}
#
# specify regression for TPR: (every measurement slice has it.)
#
# do_reg_TPR <- list() # <---- a regression for each measurement slice?
# for (i in seq_along(Mobs$MBS)){
# ind_tmp <-
# which(names(likelihood$TPR_formula) == names(Mobs$MBS)[i])
# if (!length(ind_tmp)) { # don't do regression if no regression formula is found:
# do_reg_TPR[[i]] <- FALSE
# } else{ # do regression if there is matched regression formula:
# do_reg_TPR[[i]] <-
# parse_nplcm_reg(stats::as.formula(likelihood$TPR_formula[[ind_tmp]]),data_nplcm,silent=silent)
# }
# }
#
# names(do_reg_TPR) <- names(Mobs$MBS)
#
# # if using silver-standard data:
# if ("MSS"%in% use_data_sources){
# for (i in length(Mobs$MBS)+seq_along(Mobs$MSS)){
# ind_tmp <-
# which(names(likelihood$TPR_formula) == names(Mobs$MSS)[i])
# if (!length(ind_tmp)) { # don't do regression if no regression formula is found:
# do_reg_TPR[[i]] <- FALSE
# } else{ # do regression if there is matched regression formula:
# do_reg_TPR[[i]] <-
# parse_nplcm_reg(stats::as.formula(likelihood$TPR_formula[[ind_tmp]]),data_nplcm,silent=silent)
# }
# }
# names(do_reg_TPR) <- c(names(Mobs$MBS),names(Mobs$MSS))
# }
# specify regression for etiology:
form_tmp <- stats::as.formula(likelihood$Eti_formula)
do_reg_Eti <- parse_nplcm_reg(form_tmp,data_nplcm,silent=silent)
is_discrete_Eti <- FALSE
if (!is.null(X)){ # <--- potential problem if a user input more data than needed. need fixing.
is_discrete_Eti <- (!stats::is.empty.model(form_tmp) &
!is_intercept_only(form_tmp) &
is_discrete(data.frame(X,Y)[Y==1,,drop=FALSE], form_tmp))
}
is_discrete_predictor <- list(is_discrete_Eti, is_discrete_FPR)
names(is_discrete_predictor)[1] <- "Eti"
names(is_discrete_predictor)[2] <- "FPR"
regression <- make_list(do_reg_Eti, do_reg_FPR,is_discrete_predictor)#, do_reg_TPR)
# check BrS group:
BrS_grp <- FALSE
prior_BrS <- model_options$prior$TPR_prior$BrS
GBrS_TPR <- length(unique(prior_BrS$grp))
grp_spec <- (!is.null(prior_BrS$grp) && GBrS_TPR >1 )
if (grp_spec) {
for (s in seq_along(prior_BrS$val)){
if (prior_BrS$input=="match_range" &&
(length(prior_BrS$val[[s]]$up)!=GBrS_TPR |
length(prior_BrS$val[[s]]$low)!=GBrS_TPR) ){
stop(paste0("==[baker] ",names(prior_BrS$val)[s])," needs ", GBrS_TPR,
" sets of sensitivity ranges.==\n")
}
}
BrS_grp <- TRUE
}
# check SS group:
SS_grp <- FALSE
prior_SS <- model_options$prior$TPR_prior$SS
grp_spec <- (!is.null(prior_SS$grp) && length(unique(prior_SS$grp)) >1 )
if (grp_spec) {SS_grp <- TRUE}
## <-------- the following are more strict grp specifications (may cause error when running old folders):
# val_spec <- (num_slice["MSS"]>0 && any(lapply(prior_SS$val,length)>1))
#
# if (grp_spec && val_spec){SS_grp <- TRUE}
# if (grp_spec && !val_spec){stop("==Specified TPR group in 'grp' of 'model_options$prior$TPR_prior$SS',
# but either there is no SS data or the length of 'val' does not match the no. of TPR groups. ==")}
# if (!grp_spec && val_spec){stop("==No 'grp' specified in 'model_options$prior$TPR_prior$SS',
# but we have >1 sets of TPRs. ==")}
# return results:
make_list(num_slice, nested, regression,BrS_grp,SS_grp)
}
#' Fit nested partially-latent class model (low-level)
#'
#' This function prepares data, specifies hyperparameters in priors
#' (true positive rates and etiology fractions), initializes the posterior
#' sampling chain, writes the model file (for JAGS or WinBUGS with slight differences
#' in syntax), and fits the model. Features:
#' \itemize{
#' \item no regression;
#' \item no nested subclasses
#' }
#'
#' @inheritParams nplcm
#' @return BUGS fit results.
#'
#' @seealso [write_model_NoReg] for constructing `.bug` model file; This function
#' then put it in the folder `mcmc_options$bugsmodel.dir`.
#'
#' @family model fitting functions
#'
#'
nplcm_fit_NoReg<-
function(data_nplcm,model_options,mcmc_options){
# Record the settings of current analysis:
cat("==[baker] Results stored in: ==","\n",mcmc_options$result.folder,"\n")
#model_options:
dput(model_options,file.path(mcmc_options$result.folder,"model_options.txt"))
#mcmc_options:
dput(mcmc_options,file.path(mcmc_options$result.folder,"mcmc_options.txt"))
# read in data:
Mobs <- data_nplcm$Mobs
Y <- data_nplcm$Y
# read in options:
likelihood <- model_options$likelihood
use_measurements <- model_options$use_measurements
prior <- model_options$prior
#####################################################################
# 1. prepare data (including hyper-parameters):
#####################################################################
# sample sizes:
Nd <- sum(Y==1)
Nu <- sum(Y==0)
# lengths of vectors:
cause_list <- likelihood$cause_list
Jcause <- length(cause_list)
in_data <- in_init <- out_parameter <- NULL
if ("BrS" %in% use_measurements){
#
# BrS measurement data:
#
JBrS_list <- lapply(Mobs$MBS,ncol)
# mapping template (by `make_template` function):
patho_BrS_list <- lapply(Mobs$MBS,colnames)
template_BrS_list <- lapply(patho_BrS_list,make_template,cause_list)
for (s in seq_along(template_BrS_list)){
if (sum(template_BrS_list[[s]])==0){
warning(paste0("==[baker] Bronze-standard slice ", names(data_nplcm$Mobs$MBS)[s], " has no measurements informative of the causes specified in 'cause_list', except 'NoA'!
Please check if you need this measurement slice columns correspond to causes other than 'NoA'.=="))
}
}
MBS.case_list <- lapply(Mobs$MBS,"[",which(Y==1),TRUE,drop=FALSE)
MBS.ctrl_list <- lapply(Mobs$MBS,"[",which(Y==0),TRUE,drop=FALSE)
MBS_list <- list()
for (i in seq_along(MBS.case_list)){
MBS_list[[i]] <- rbind(MBS.case_list[[i]],MBS.ctrl_list[[i]])
}
names(MBS_list) <- names(MBS.case_list)
single_column_MBS <- which(lapply(MBS_list,ncol)==1)
for(i in seq_along(JBrS_list)){
assign(paste("JBrS", i, sep = "_"), JBrS_list[[i]])
xx <- as.matrix_or_vec(MBS_list[[i]]); attr(xx,"dimnames") <- NULL # remove dimnames.
assign(paste("MBS", i, sep = "_"), xx)
assign(paste("templateBS", i, sep = "_"), as.matrix_or_vec(template_BrS_list[[i]]))
}
# setup groupwise TPR for BrS:
BrS_TPR_strat <- FALSE
prior_BrS <- model_options$prior$TPR_prior$BrS
parsed_model <- assign_model(model_options,data_nplcm)
if (parsed_model$BrS_grp){
BrS_TPR_strat <- TRUE
for(i in seq_along(JBrS_list)){
assign(paste("GBrS_TPR", i, sep = "_"), length(unique(prior_BrS$grp)))
assign(paste("BrS_TPR_grp", i, sep = "_"), prior_BrS$grp)
}
}
# add GBrS_TPR_1, or 2 if we want to index by slices:
for (i in seq_along(JBrS_list)){
if (!is.null(prior_BrS$grp)){ # <--- need to change to list if we have multiple slices.
assign(paste("GBrS_TPR", i, sep = "_"), length(unique(prior_BrS$grp))) # <--- need to change to depending on i if grp change wrt specimen.
}
if (is.null(prior_BrS$grp)){ # <--- need to change to list if we have multiple slices.
assign(paste("GBrS_TPR", i, sep = "_"), 1)
}
}
# set BrS measurement priors:
# hyper-parameters for sensitivity:
alpha_mat <- list() # dimension for slices.
beta_mat <- list()
for(i in seq_along(JBrS_list)){
if (likelihood$k_subclass[i] == 1){BrS_tpr_prior <- set_prior_tpr_BrS_NoNest(i,model_options,data_nplcm)}
if (likelihood$k_subclass[i] > 1) {BrS_tpr_prior <- set_prior_tpr_BrS_NoNest(i,model_options,data_nplcm)}
GBrS_TPR_curr <- eval(parse(text = paste0("GBrS_TPR_",i)))
alpha_mat[[i]] <- matrix(NA, nrow=GBrS_TPR_curr,ncol=JBrS_list[[i]])
beta_mat[[i]] <- matrix(NA, nrow=GBrS_TPR_curr,ncol=JBrS_list[[i]])
colnames(alpha_mat[[i]]) <- patho_BrS_list[[i]]
colnames(beta_mat[[i]]) <- patho_BrS_list[[i]]
for (g in 1:GBrS_TPR_curr){
alpha_mat[[i]][g,] <- unlist(BrS_tpr_prior[[1]][[g]]$alpha)
beta_mat[[i]][g,] <- unlist(BrS_tpr_prior[[1]][[g]]$beta)
}
if (GBrS_TPR_curr>1){
assign(paste("alphaB", i, sep = "_"), alpha_mat[[i]]) # <---- input BrS TPR prior here.
assign(paste("betaB", i, sep = "_"), beta_mat[[i]])
}else{
assign(paste("alphaB", i, sep = "_"), c(alpha_mat[[i]])) # <---- input BrS TPR prior here.
assign(paste("betaB", i, sep = "_"), c(beta_mat[[i]]))
}
}
names(alpha_mat) <- names(beta_mat)<- names(Mobs$MBS)
if (!BrS_TPR_strat){
# summarize into one name (for all measurements):
if (length(single_column_MBS)==0){
# if all slices have >2 columns:
in_data <- c(in_data,"Nd","Nu","Jcause","alphaEti",
paste("JBrS",1:length(JBrS_list),sep="_"),
paste("MBS",1:length(JBrS_list),sep="_"),
paste("templateBS",1:length(JBrS_list),sep="_")
# paste("alphaB",1:length(JBrS_list),sep="_"),
# paste("betaB",1:length(JBrS_list),sep="_")
)
} else {
# if there exist slices with 1 column:
in_data <- c(in_data,"Nd","Nu","Jcause","alphaEti",
paste("JBrS",1:length(JBrS_list),sep="_")[-single_column_MBS], # <---- no need to iterate in .bug file for a slice with one column.
paste("MBS",1:length(JBrS_list),sep="_"),
paste("templateBS",1:length(JBrS_list),sep="_")
# paste("alphaB",1:length(JBrS_list),sep="_"),
# paste("betaB",1:length(JBrS_list),sep="_")
)
}
} else{
# summarize into one name (for all measurements):
if (length(single_column_MBS)==0){
# if all slices have >2 columns:
in_data <- c(in_data,"Nd","Nu","Jcause","alphaEti",
paste("JBrS",1:length(JBrS_list),sep="_"),
paste("GBrS_TPR",1:length(JBrS_list),sep="_"), # <-- added for TPR strata.
paste("BrS_TPR_grp",1:length(JBrS_list),sep="_"),# <-- added for TPR strata.
paste("MBS",1:length(JBrS_list),sep="_"),
paste("templateBS",1:length(JBrS_list),sep="_")
# paste("alphaB",1:length(JBrS_list),sep="_"),
# paste("betaB",1:length(JBrS_list),sep="_")
)
} else {
# if there exist slices with 1 column:
in_data <- c(in_data,"Nd","Nu","Jcause","alphaEti",
paste("JBrS",1:length(JBrS_list),sep="_")[-single_column_MBS], # <---- no need to iterate in .bug file for a slice with one column.
paste("GBrS_TPR",1:length(JBrS_list),sep="_"), # <-- added for TPR strata.
paste("BrS_TPR_grp",1:length(JBrS_list),sep="_"),# <-- added for TPR strata.
paste("MBS",1:length(JBrS_list),sep="_"),
paste("templateBS",1:length(JBrS_list),sep="_")
# paste("alphaB",1:length(JBrS_list),sep="_"),
# paste("betaB",1:length(JBrS_list),sep="_")
)
}
}
#
# hyper-parameters:
#
# Set BrS measurement priors:
# hyperparameter for sensitivity (can add for specificity if necessary):
for (s in seq_along(Mobs$MBS)){
#if (likelihood$k_subclass[s] == 1){BrS_tpr_prior <- set_prior_tpr_BrS_NoNest(s,model_options,data_nplcm)}
#if (likelihood$k_subclass[s] > 1){BrS_tpr_prior <- set_prior_tpr_BrS_NoNest(s,model_options,data_nplcm)}
#assign(paste("alphaB", s, sep = "_"), BrS_tpr_prior[[1]]$alpha) # <---- input BrS TPR prior here.
#assign(paste("betaB", s, sep = "_"), BrS_tpr_prior[[1]]$beta)
if (likelihood$k_subclass[s]==1){
out_parameter <- c(out_parameter,paste(c("thetaBS","psiBS"), s, sep="_"))
}else{ # <--- TPR stratification for BrS data not implemented for K>1.
assign(paste("K", s, sep = "_"), likelihood$k_subclass[s])
in_data <- unique(c(in_data,paste0("K_",s))) # <---- No. of subclasses for this slice.
out_parameter <- unique(c(out_parameter,
paste(c("ThetaBS","PsiBS","Lambda","Eta","alphadp0","alphadp0_case"),s,sep="_")
))
}
}
#
# collect in_data, out_parameter together:
#
in_data <- c(in_data,
paste("alphaB",1:length(JBrS_list),sep="_"),
paste("betaB",1:length(JBrS_list),sep="_")
)
out_parameter <- c(out_parameter,"pEti")
}
if ("SS" %in% use_measurements){
#
# 2. SS measurement data:
#
JSS_list <- lapply(Mobs$MSS,ncol)
# mapping template (by `make_template` function):
patho_SS_list <- lapply(Mobs$MSS,colnames)
template_SS_list <- lapply(patho_SS_list,make_template,cause_list)
for (s in seq_along(template_SS_list)){
if (nrow(Mobs$MSS[[s]])==0){stop(paste0("==[baker] Silver-standard (SS) slice ", names(data_nplcm$Mobs$MSS)[s],
" has zero rows of data. Check if you accidentally removed all rows in this SS data slice.==\n"))}
if (sum(template_SS_list[[s]])==0){
warning(paste0("==[baker] Silver-standard slice ", names(data_nplcm$Mobs$MSS)[s],
" has no measurements informative of the causes! Please check if measurements' columns correspond to causes.==\n"))
}
}
MSS_list <- lapply(Mobs$MSS,"[",which(Y==1),TRUE,drop=FALSE)
single_column_MSS <- which(lapply(MSS_list,ncol)==1)
for(i in seq_along(JSS_list)){
assign(paste("JSS", i, sep = "_"), JSS_list[[i]])
assign(paste("MSS", i, sep = "_"), as.matrix_or_vec(MSS_list[[i]]))
assign(paste("templateSS", i, sep = "_"), as.matrix_or_vec(template_SS_list[[i]]))
}
# setup groupwise TPR for SS:
SS_TPR_strat <- FALSE
prior_SS <- model_options$prior$TPR_prior$SS
parsed_model <- assign_model(model_options,data_nplcm)
if (parsed_model$SS_grp){
SS_TPR_strat <- TRUE
for(i in seq_along(JSS_list)){
assign(paste("GSS_TPR", i, sep = "_"), length(unique(prior_SS$grp)))
assign(paste("SS_TPR_grp", i, sep = "_"), prior_SS$grp)
}
}
# add GSS_TPR_1, or 2 if we want to index by slices:
for (i in seq_along(JSS_list)){
if (!is.null(prior_SS$grp)){ # <--- need to change to list if we have multiple slices.
assign(paste("GSS_TPR", i, sep = "_"), length(unique(prior_SS$grp))) # <--- need to change to depending on i if grp change wrt specimen.
}
if (is.null(prior_SS$grp)){ # <--- need to change to list if we have multiple slices.
assign(paste("GSS_TPR", i, sep = "_"), 1)
}
}
SS_tpr_prior <- set_prior_tpr_SS(model_options,data_nplcm)
# set SS measurement priors:
# hyper-parameters for sensitivity:
alpha_mat <- list() # dimension for slices.
beta_mat <- list()
for(i in seq_along(JSS_list)){
GSS_TPR_curr <- eval(parse(text = paste0("GSS_TPR_",i)))
alpha_mat[[i]] <- matrix(NA, nrow=GSS_TPR_curr,ncol=JSS_list[[i]])
beta_mat[[i]] <- matrix(NA, nrow=GSS_TPR_curr,ncol=JSS_list[[i]])
colnames(alpha_mat[[i]]) <- patho_SS_list[[i]]
colnames(beta_mat[[i]]) <- patho_SS_list[[i]]
for (g in 1:GSS_TPR_curr){
alpha_mat[[i]][g,] <- unlist(SS_tpr_prior[[i]][[g]]$alpha)
beta_mat[[i]][g,] <- unlist(SS_tpr_prior[[i]][[g]]$beta)
}
if (GSS_TPR_curr>1){
assign(paste("alphaS", i, sep = "_"), alpha_mat[[i]]) # <---- input SS TPR prior here.
assign(paste("betaS", i, sep = "_"), beta_mat[[i]])
}else{
assign(paste("alphaS", i, sep = "_"), c(alpha_mat[[i]])) # <---- input SS TPR prior here.
assign(paste("betaS", i, sep = "_"), c(beta_mat[[i]]))
}
}
names(alpha_mat) <- names(beta_mat)<- names(Mobs$MSS)
if (!SS_TPR_strat){
if (length(single_column_MSS)==0){
# summarize into one name (for all measurements):
in_data <- unique(c(in_data,"Nd","Jcause","alphaEti",
paste("JSS",1:length(JSS_list),sep="_"),
paste("MSS",1:length(JSS_list),sep="_"),
paste("templateSS",1:length(JSS_list),sep="_"),
paste("alphaS",1:length(JSS_list),sep="_"),
paste("betaS",1:length(JSS_list),sep="_")))
} else{
in_data <- unique(c(in_data,"Nd","Nu","Jcause","alphaEti",
paste("JSS",1:length(JSS_list),sep="_")[-single_column_MSS],
paste("MSS",1:length(JSS_list),sep="_"),
paste("templateSS",1:length(JSS_list),sep="_"),
paste("alphaS",1:length(JSS_list),sep="_"),
paste("betaS",1:length(JSS_list),sep="_")))
}
}else {
if (length(single_column_MSS)==0){
# summarize into one name (for all measurements):
in_data <- unique(c(in_data,"Nd","Jcause","alphaEti",
paste("JSS",1:length(JSS_list),sep="_"),
paste("GSS_TPR",1:length(JSS_list),sep="_"), # <-- added for TPR strata.
paste("SS_TPR_grp",1:length(JSS_list),sep="_"), # <-- added for TPR strata.
paste("MSS",1:length(JSS_list),sep="_"),
paste("templateSS",1:length(JSS_list),sep="_"),
paste("alphaS",1:length(JSS_list),sep="_"),
paste("betaS",1:length(JSS_list),sep="_")))
} else{
in_data <- unique(c(in_data,"Nd","Nu","Jcause","alphaEti",
paste("JSS",1:length(JSS_list),sep="_")[-single_column_MSS],
paste("GSS_TPR",1:length(JSS_list),sep="_"), # <-- added for TPR strata.
paste("SS_TPR_grp",1:length(JSS_list),sep="_"),# <-- added for TPR strata.
paste("MSS",1:length(JSS_list),sep="_"),
paste("templateSS",1:length(JSS_list),sep="_"),
paste("alphaS",1:length(JSS_list),sep="_"),
paste("betaS",1:length(JSS_list),sep="_")))
}
}
out_parameter <- unique(c(out_parameter, paste("thetaSS", seq_along(JSS_list), sep = "_"),
"pEti"))
}
#####################################
# set up initialization function:
#####################################
if ("BrS" %in% use_measurements & !("SS" %in% use_measurements)){
in_init <- function(){
res <- list()
for (s in seq_along(Mobs$MBS)){
res_curr <- list()
if (likelihood$k_subclass[s]==1){
#res_curr[[1]] <- stats::rbeta(JBrS_list[[s]],1,1)
GBrS_TPR_curr <- eval(parse(text = paste0("GBrS_TPR_",s)))
if (GBrS_TPR_curr==1){
res_curr[[1]] <- stats::rbeta(JBrS_list[[s]],1,1)
} else{
res_curr[[1]] <- matrix(stats::rbeta(GBrS_TPR_curr*JBrS_list[[s]],1,1),
nrow=GBrS_TPR_curr,ncol=JBrS_list[[s]])
if (JBrS_list[[s]]==1){
res_curr[[1]] <- c(res_curr[[1]])
}
}
res_curr[[2]] <- stats::rbeta(JBrS_list[[s]],1,1)
names(res_curr) <- paste(c("thetaBS","psiBS"),s,sep="_")
res <- c(res,res_curr)
}
if (likelihood$k_subclass[s] > 1){
K_curr <- likelihood$k_subclass[s]
res_curr[[1]] <- c(rep(.5,K_curr-1),NA)
res_curr[[2]] <- c(rep(.5,K_curr-1),NA)
# for different Eta's:
# res_curr[[2]] <- cbind(matrix(rep(.5,Jcause*(K_curr-1)),
# nrow=Jcause,ncol=K_curr-1),
# rep(NA,Jcause))
res_curr[[3]] <- 1
res_curr[[4]] <- 1 #<---- added together with 'alphadp0_case' below.
names(res_curr) <- paste(c("r0","r1","alphadp0","alphadp0_case"),s,sep="_")
res <- c(res,res_curr)
}
}
res
}
}
if (!("BrS" %in% use_measurements) & "SS" %in% use_measurements){
in_init <- function(){
tmp_thetaSS <- list()
#tmp_psiSS <- list()
tmp_Icat_case <- list()
for(i in seq_along(JSS_list)){
GSS_TPR_curr <- eval(parse(text = paste0("GSS_TPR_",i)))
if (GSS_TPR_curr==1){
tmp_thetaSS[[i]] <- stats::rbeta(JSS_list[[i]],1,1)
} else{
tmp_thetaSS[[i]] <- matrix(stats::rbeta(GSS_TPR_curr*JSS_list[[i]],1,1),
nrow=GSS_TPR_curr,ncol=JSS_list[[i]])
}
if (i==1){
#if (length(JSS_list)>1){
# warning("==[baker] Only the first slice of silver-standard data is used to
# initialize 'Icat' in JAGS fitting. Choose wisely!\n ==")
#}
tmp_Icat_case[[i]] <- init_latent_jags_multipleSS(MSS_list,likelihood$cause_list)
}
}
res <- c(tmp_thetaSS,tmp_Icat_case)
names(res) <- c(paste("thetaSS", seq_along(JSS_list), sep = "_"),"Icat")
res
}
}
if ("BrS" %in% use_measurements & "SS" %in% use_measurements){
in_init <- function(){
res <- list()
for (s in seq_along(Mobs$MBS)){
res_curr <- list()
if (likelihood$k_subclass[s]==1){
#res_curr[[1]] <- stats::rbeta(JBrS_list[[s]],1,1)
GBrS_TPR_curr <- eval(parse(text = paste0("GBrS_TPR_",s)))
if (GBrS_TPR_curr==1){
res_curr[[1]] <- stats::rbeta(JBrS_list[[s]],1,1)
} else{
res_curr[[1]] <- matrix(stats::rbeta(GBrS_TPR_curr*JBrS_list[[s]],1,1),
nrow=GBrS_TPR_curr,ncol=JBrS_list[[s]])
if (JBrS_list[[s]]==1){
res_curr[[1]] <- c(res_curr[[1]])
}
}
res_curr[[2]] <- stats::rbeta(JBrS_list[[s]],1,1)
names(res_curr) <- paste(c("thetaBS","psiBS"),s,sep="_")
res <- c(res,res_curr)
}
if (likelihood$k_subclass[s] > 1){
K_curr <- likelihood$k_subclass[s]
res_curr[[1]] <- c(rep(.5,K_curr-1),NA)
res_curr[[2]] <- c(rep(.5,K_curr-1),NA)
# for different Eta's:
# res_curr[[2]] <- cbind(matrix(rep(.5,Jcause*(K_curr-1)),
# nrow=Jcause,ncol=K_curr-1),
# rep(NA,Jcause))
res_curr[[3]] <- 1
res_curr[[4]] <- 1
names(res_curr) <- paste(c("r0","r1","alphadp0","alphadp0_case"),s,sep="_")
res <- c(res,res_curr)
}
}
tmp_thetaSS <- list()
#tmp_psiSS <- list()
tmp_Icat_case <- list()
for(i in seq_along(JSS_list)){
GSS_TPR_curr <- eval(parse(text = paste0("GSS_TPR_",i)))
if (GSS_TPR_curr==1){
tmp_thetaSS[[i]] <- stats::rbeta(JSS_list[[i]],1,1)
} else{
tmp_thetaSS[[i]] <- matrix(stats::rbeta(GSS_TPR_curr*JSS_list[[i]],1,1),nrow=GSS_TPR_curr,ncol=JSS_list[[i]])
}
if (i==1){
#if (length(JSS_list)>1){
# warning("==[baker] Only the first slice of silver-standard data is
# used to initialize 'Icat' in JAGS fitting. Choose wisely!==\n ")
#}
tmp_Icat_case[[i]] <- init_latent_jags_multipleSS(MSS_list,likelihood$cause_list)
}
}
res2 <- c(tmp_thetaSS,tmp_Icat_case)
names(res2) <- c(paste("thetaSS", seq_along(JSS_list), sep = "_"),"Icat")
#print(res2)
c(res,res2)
}
}
# etiology (measurement independent)
alphaEti <- prior$Eti_prior # <-------- input etiology prior here.
#
# fit model :
#
# special operations:
# get individual prediction:
if (!is.null(mcmc_options$individual.pred) && mcmc_options$individual.pred){out_parameter <- c(out_parameter,"Icat")}
# get posterior predictive distribution of BrS measurments:
if (!is.null(mcmc_options$ppd) && mcmc_options$ppd){out_parameter <- c(out_parameter,paste("MBS.new",seq_along(Mobs$MBS),sep = "_"))}
#
# write the .bug files into mcmc_options$bugsmodel.dir;
# could later set it equal to result.folder.
#
use_jags <- (!is.null(mcmc_options$use_jags) && mcmc_options$use_jags)
model_func <- write_model_NoReg(model_options$likelihood$k_subclass,
data_nplcm$Mobs,
model_options$prior,
model_options$likelihood$cause_list,
model_options$use_measurements,
mcmc_options$ppd,
use_jags)
model_bugfile_name <- "model_NoReg.bug"
filename <- file.path(mcmc_options$bugsmodel.dir, model_bugfile_name)
writeLines(model_func, filename)
#
# run the model:
#
here <- environment()
##JAGS
in_data.list <- lapply(as.list(in_data),get, envir= here)
names(in_data.list) <- in_data
#lapply(names(in_data.list), dump, append = TRUE, envir = here,
# file = file.path(mcmc_options$result.folder,"jagsdata.txt"))
#do.call(file.remove, list(list.files(mcmc_options$result.folder, full.names = TRUE)))
curr_data_txt_file <- file.path(mcmc_options$result.folder,"jagsdata.txt")
if(file.exists(curr_data_txt_file)){file.remove(curr_data_txt_file)}