"Nested Partially Latent Class Models"

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Documentation for package ‘baker’ version 1.0.3

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A B C D E G H I J L M N O P R S T U V W

-- A --

add_meas_BrS_case_Nest_Slice add likelihood for a BrS measurement slice among cases (conditional dependence)
add_meas_BrS_case_Nest_Slice_jags add likelihood for a BrS measurement slice among cases (conditional dependence)
add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags add likelihood component for a BrS measurement slice among cases
add_meas_BrS_case_NoNest_reg_Slice_jags add likelihood component for a BrS measurement slice among cases
add_meas_BrS_case_NoNest_Slice add a likelihood component for a BrS measurement slice among cases (conditional independence)
add_meas_BrS_case_NoNest_Slice_jags add a likelihood component for a BrS measurement slice among cases (conditional independence)
add_meas_BrS_ctrl_Nest_Slice add likelihood for a BrS measurement slice among controls (conditional independence)
add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags add a likelihood component for a BrS measurement slice among controls
add_meas_BrS_ctrl_NoNest_reg_Slice_jags add a likelihood component for a BrS measurement slice among controls
add_meas_BrS_ctrl_NoNest_Slice add a likelihood component for a BrS measurement slice among controls (conditional independence)
add_meas_BrS_param_Nest_reg_Slice_jags add parameters for a BrS measurement slice among cases and controls
add_meas_BrS_param_Nest_Slice add parameters for a BrS measurement slice among cases and controls (conditional dependence)
add_meas_BrS_param_Nest_Slice_jags add parameters for a BrS measurement slice among cases and controls (conditional dependence)
add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags add parameters for a BrS measurement slice among cases and controls
add_meas_BrS_param_NoNest_reg_Slice_jags add parameters for a BrS measurement slice among cases and controls
add_meas_BrS_param_NoNest_Slice add parameters for a BrS measurement slice among cases and controls (conditional independence)
add_meas_BrS_param_NoNest_Slice_jags add parameters for a BrS measurement slice among cases and controls (conditional independence)
add_meas_BrS_subclass_Nest_Slice add subclass indicators for a BrS measurement slice among cases and controls (conditional independence)
add_meas_SS_case add likelihood for a SS measurement slice among cases (conditional independence)
add_meas_SS_param add parameters for a SS measurement slice among cases (conditional independence)
as.matrix_or_vec convert one column data frame to a vector
assign_model Interpret the specified model structure

-- B --

baker baker: *B*ayesian *A*nalytic *K*it for *E*tiology *R*esearch
beta_parms_from_quantiles Pick parameters in the Beta distribution to match the specified range
beta_plot Plot beta density
bin2dec Convert a 0/1 binary-coded sequence into decimal digits

-- C --

check_dir_create check existence and create folder if non-existent
clean_combine_subsites Combine subsites in raw PERCH data set
clean_perch_data Clean PERCH data
combine_data_nplcm combine multiple data_nplcm (useful when simulating data from regression models)
compute_logOR_single_cause Calculate marginal log odds ratios
compute_marg_PR_nested_reg compute positive rates for nested model with subclass mixing weights that are the same across 'Jcause' classes for each person (people may have different weights.)
compute_marg_PR_nested_reg_array compute positive rates for nested model with subclass mixing weights that are the same across 'Jcause' classes for each person (people may have different weights.)
create_bugs_regressor_Eti create regressor summation equation used in regression for etiology
create_bugs_regressor_FPR create regressor summation equation used in regression for FPR

-- D --

data_nplcm_noreg Simulated dataset that is structured in the format necessary for an 'nplcm()' without regression
data_nplcm_reg_nest Simulated dataset that is structured in the format necessary for an 'nplcm()' with regression
delete_start_with Deletes a pattern from the start of a string, or each of a vector of strings.
dm_Rdate_Eti Make etiology design matrix for dates with R format.
dm_Rdate_FPR Make FPR design matrix for dates with R format.

-- E --

expit expit function
extract_data_raw Import Raw PERCH Data 'extract_data_raw' imports and converts the raw data to analyzable format

-- G --

get_coverage Obtain coverage status from a result folder
get_direct_bias Obtain direct bias that measure the discrepancy of a posterior distribution of pie and a true pie.
get_fitted_mean_nested get fitted mean for nested model with subclass mixing weights that are the same among cases
get_fitted_mean_no_nested get model fitted mean for conditional independence model
get_individual_data get individual data
get_individual_prediction get individual prediction (Bayesian posterior)
get_latent_seq get index of latent status
get_marginal_rates_nested get marginal TPR and FPR for nested model
get_marginal_rates_no_nested get marginal TPR and FPR for no nested model
get_metric Obtain Integrated Squared Aitchison Distance, Squared Bias and Variance (both on Central Log-Ratio transformed scale) that measure the discrepancy of a posterior distribution of pie and a true pie.
get_pEti_samp get etiology samples by names (no regression)
get_plot_num get the plotting positions (numeric) for the fitted means; 3 positions for each cell
get_plot_pos get a list of measurement index where to look for data
get_postsd Obtain posterior standard deviation from a result folder
get_top_pattern get top patterns from a slice of bronze-standard measurement

-- H --

H Shannon entropy for multivariate discrete data
has_non_basis test if a formula has terms not created by [s_date_Eti() or 's_date_FPR()'

-- I --

I2symb Convert 0/1 coding to pathogen/combinations
Imat2cat Convert a matrix of binary indicators to categorical variables
init_latent_jags_multipleSS Initialize individual latent status (for 'JAGS')
insert_bugfile_chunk_noreg_etiology insert distribution for latent status code chunk into .bug file
insert_bugfile_chunk_noreg_meas Insert measurement likelihood (without regression) code chunks into .bug model file
insert_bugfile_chunk_reg_discrete_predictor_etiology insert etiology regression for latent status code chunk into .bug file; discrete predictors
insert_bugfile_chunk_reg_discrete_predictor_nonest_meas Insert measurement likelihood (with regression; discrete) code chunks into .bug model file
insert_bugfile_chunk_reg_etiology insert etiology regression for latent status code chunk into .bug file
insert_bugfile_chunk_reg_nest_meas Insert measurement likelihood (nested model+regression) code chunks into .bug model file
insert_bugfile_chunk_reg_nonest_meas Insert measurement likelihood (with regression) code chunks into .bug model file
is.error Test for 'try-error' class
is_discrete Check if covariates are discrete
is_intercept_only check if the formula is intercept only
is_jags_folder See if a result folder is obtained by JAGS
is_length_all_one check if a list has elements all of length one

-- J --

jags2_baker Run 'JAGS' from R

-- L --

line2user convert line to user coordinates
loadOneName load an object from .RDATA file
logit logit function
logOR calculate pairwise log odds ratios
logsumexp log sum exp trick
lookup_quality Get position to store in data_nplcm$Mobs:

-- M --

make_filename Create new file name
make_foldername Create new folder name
make_list Takes any number of R objects as arguments and returns a list whose names are derived from the names of the R objects.
make_meas_object Make measurement slice
make_numbered_list Make a list with numbered names
make_template make a mapping template for model fitting
marg_H Shannon entropy for binary data
match_cause Match latent causes that might have the same combo but different specifications
merge_lists For a list of many sublists each of which has matrices as its member, we combine across the many sublists to produce a final list
my_reorder Reorder the measurement dimensions to match the order for display

-- N --

NA2dot convert 'NA' to '.'
nplcm Fit nested partially-latent class models (highest-level wrapper function)
nplcm_fit_NoReg Fit nested partially-latent class model (low-level)
nplcm_fit_Reg_discrete_predictor_NoNest Fit nested partially-latent class model with regression (low-level)
nplcm_fit_Reg_Nest Fit nested partially-latent class model with regression (low-level)
nplcm_fit_Reg_NoNest Fit nested partially-latent class model with regression (low-level)
nplcm_read_folder Read data and other model information from a folder that stores model results.
null_as_zero Convert 'NULL' to zero.

-- O --

order_post_eti order latent status by posterior mean
overall_uniform specify overall uniform (symmetric Dirichlet distribution) for etiology prior

-- P --

parse_nplcm_reg parse regression components (either false positive rate or etiology regression) for fitting npLCM; Only use this when formula is not 'NULL'.
pathogen_category_perch pathogens and their categories in PERCH study (virus or bacteria)
pathogen_category_simulation Hypothetical pathogens and their categories (virus or bacteria)
plot.nplcm 'plot.nplcm' plot the results from 'nplcm()'.
plot_BrS_panel Plot bronze-standard (BrS) panel
plot_case_study visualize the PERCH etiology regression with a continuous covariate
plot_check_common_pattern Posterior predictive checking for the nested partially class models - frequent patterns in the BrS data. (for multiple folders)
plot_check_pairwise_SLORD Posterior predictive checking for nested partially latent class models - pairwise log odds ratio (only for bronze-standard data)
plot_etiology_regression visualize the etiology regression with a continuous covariate
plot_etiology_strat visualize the etiology estimates for each discrete levels
plot_leftmost plotting the labels on the left margin for panels plot
plot_logORmat Visualize pairwise log odds ratios (LOR) for data that are available in both cases and controls
plot_panels Plot three-panel figures for nested partially-latent model results
plot_pie_panel Plot etiology (pie) panel
plot_SS_panel Plot silver-standard (SS) panel
plot_subwt_regression visualize the subclass weight regression with a continuous covariate
print.nplcm 'print.nplcm' summarizes the results from 'nplcm()'.
print.summary.nplcm.no_reg Compact printing of 'nplcm()' model fits
print.summary.nplcm.reg_nest Compact printing of 'nplcm()' model fits
print.summary.nplcm.reg_nest_strat Compact printing of 'nplcm()' model fits
print.summary.nplcm.reg_nonest Compact printing of 'nplcm()' model fits
print.summary.nplcm.reg_nonest_strat Compact printing of 'nplcm()' model fits

-- R --

read_meas_object Read measurement slices
rvbern Sample a vector of Bernoulli variables.

-- S --

set_prior_tpr_BrS_NoNest Set true positive rate (TPR) prior ranges for bronze-standard (BrS) data
set_prior_tpr_SS Set true positive rate (TPR) prior ranges for silver-standard data.
set_strat Stratification setup by covariates
show_dep Show function dependencies
show_individual get an individual's data from the output of 'clean_perch_data()'
simulate_brs Simulate Bronze-Standard (BrS) Data
simulate_latent Simulate Latent Status:
simulate_nplcm Simulate data from nested partially-latent class model (npLCM) family
simulate_ss Simulate Silver-Standard (SS) Data
softmax softmax
subset_data_nplcm_by_index subset data from the output of 'clean_perch_data()'
summarize_BrS summarize bronze-standard data
summarize_SS silver-standard data summary
summary.nplcm 'summary.nplcm' summarizes the results from 'nplcm()'.
symb2I Convert names of pathogen/combinations into 0/1 coding
sym_diff_month get symmetric difference of months from two vector of R-format dates
s_date_Eti Make Etiology design matrix for dates with R format.
s_date_FPR Make false positive rate (FPR) design matrix for dates with R format.

-- T --

tsb generate stick-breaking prior (truncated) from a vector of random probabilities

-- U --

unfactor Convert factor to numeric without losing information on the label
unique_cause get unique causes, regardless of the actual order in combo
unique_month Get unique month from Date

-- V --

visualize_case_control_matrix Visualize matrix for a quantity measured on cases and controls (a single number)
visualize_season visualize trend of pathogen observation rate for NPPCR data (both cases and controls)

-- W --

write.model function to write bugs model (copied from R2WinBUGS)
write_model_NoReg Write .bug model file for model without regression
write_model_Reg_discrete_predictor_NoNest Write .bug model file for regression model without nested subclasses
write_model_Reg_Nest Write '.bug' model file for regression model WITH nested subclasses
write_model_Reg_NoNest Write .bug model file for regression model without nested subclasses