nplcm_fit_Reg_NoNest  R Documentation 
Fit nested partiallylatent class model with regression (lowlevel)
Description
Fit nested partiallylatent class model with regression (lowlevel)
Usage
nplcm_fit_Reg_NoNest(data_nplcm, model_options, mcmc_options)
Arguments
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 ).

Mobs A list of measurements of distinct qualities (Bronze, Silver, and GoldStandard:
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 ).

MBS a list of data frame of bronzestandard (BrS) measurements.
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).

MSS a list of data frame of silverstandard (SS) measurements.
Rows are subjects, columns are causative agents measured in specimen (e.g. blood culture).
These measurements have perfect specificity but imperfect sensitivity.

MGS a list of data frame of goldstandard (GS) measurements.
Rows are subject, columns are measured causative agents
These measurements have perfect sensitivity and specificity.

Y Vector of disease status: 1 for case, 0 for control.

X Covariate matrix. A subset of columns are primary covariates in causespecific
casefraction (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 nonlinear effect,
basis expansion is often needed for approximation.

model_options 
A list of model options: likelihood and prior.
use_measurements 
A vector of characters strings; can be one or more from "BrS" , "SS" , "GS" .
likelihood 
cause_list The vector of causes (NB: specify);
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;
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.
FPR_formulaformula 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 penalizedspline basis (based on Bsplines).
Specify ~ 1 if no regression is intended. (NB: If effect="fixed" , dm_Rdate_FPR()
will just specify a design matrix with appropriately standardized dates.)
prior 
Eti_priorDescription of etiology prior (e.g., overall_uniform 
all hyperparameters are 1 ; or 0_1  all hyperparameters are 0.1 );
TPR_priorDescription of priors for the measurements
(e.g., informative vs noninformative). Its length should be the same with M_use .
(NB: not sure what M use is...)

mcmc_options 
A list of Markov chain Monte Carlo (MCMC) options.

debugstatus Logical  whether to pause WinBUGS after it finishes
model fitting; (NB: is this obsolete? Test.)

n.chains Number of MCMC chains;

n.burnin Number of burnin iterations;

n.thin To keep every other n.thin samples after burnin period;

individual.pred TRUE to perform individual prediction (Icat
variables in the .bug file); FALSE otherwise;

ppd TRUE to simulate new data (XXX.new
variables in the .bug file) from the posterior predictive distribution (ppd);
FALSE otherwise;

get.pEti TRUE for getting posterior samples of individual etiologic fractions;
FALSE otherwise. For nonregression, 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 nondiscrete 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 .

result.folder Path to folder storing the results;

bugsmodel.dir Path to .bug model files;

jags.dir Path to where JAGS is installed; if NULL , this will be set
to jags.dir="" .

Details
This function prepares data, specifies hyperparameters in priors
(true positive rates and CSCFs), initializes the posterior
sampling chain, writes the model file (for JAGS or WinBUGS with slight
differences in syntax), and fits the model. Features:
regression (not all discrete covariates);
no nested subclasses, i.e. conditional independence of
multivariate measurements given disease class and covariates;
multiple BrS + multiple SS.
Value
BUGS fit results from JAGS
.
See Also
write_model_NoReg for constructing .bug
model file; This function
then puts it in the folder mcmc_options$bugsmodel.dir
.
Other model fitting functions:
nplcm_fit_NoReg()
,
nplcm_fit_Reg_Nest()
,
nplcm_fit_Reg_discrete_predictor_NoNest()