Function reference • baker

All functions

H(): Shannon entropy for multivariate discrete data

I2symb(): Convert 0/1 coding to pathogen/combinations

Imat2cat(): Convert a matrix of binary indicators to categorical variables

NA2dot(): convert 'NA' to '.'

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_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_case_NoNest_reg_Slice_jags(): add likelihood component for a BrS measurement slice among cases

add_meas_BrS_case_NoNest_reg_discrete_predictor_Slice_jags(): add likelihood component for a BrS measurement slice among cases

add_meas_BrS_ctrl_Nest_Slice(): add likelihood for a BrS measurement slice among controls (conditional independence)

add_meas_BrS_ctrl_NoNest_Slice(): add a likelihood component for a BrS measurement slice among controls (conditional independence)

add_meas_BrS_ctrl_NoNest_reg_Slice_jags(): add a likelihood component for a BrS measurement slice among controls

add_meas_BrS_ctrl_NoNest_reg_discrete_predictor_Slice_jags(): add a likelihood component for a BrS measurement slice among 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_Nest_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_param_NoNest_reg_Slice_jags(): add parameters for a BrS measurement slice among cases and controls

add_meas_BrS_param_NoNest_reg_discrete_predictor_Slice_jags(): add parameters for a BrS measurement slice among cases and controls

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

baker: baker: Bayesian Analytic Kit for Etiology Research

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

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

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.

expit(): expit function

extract_data_raw(): Import Raw PERCH Data extract_data_raw imports and converts the raw data to analyzable format

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

has_non_basis(): test if a formula has terms not created by [s_date_Eti() or s_date_FPR()

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

jags2_baker(): Run JAGS from R

line2user(): convert line to user coordinates

loadOneName(): load an object from .RDATA file

logOR(): calculate pairwise log odds ratios

logit(): logit function

logsumexp(): log sum exp trick

lookup_quality(): Get position to store in data_nplcm$Mobs:

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

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_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_fit_Reg_discrete_predictor_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.

order_post_eti(): order latent status by posterior mean

overall_uniform(): specify overall uniform (symmetric Dirichlet distribution) for etiology prior

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_SS_panel(): Plot silver-standard (SS) 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_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

read_meas_object(): Read measurement slices

rvbern(): Sample a vector of Bernoulli variables.

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.

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().

sym_diff_month(): get symmetric difference of months from two vector of R-format dates

symb2I(): Convert names of pathogen/combinations into 0/1 coding

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

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

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)

write.model(): function to write bugs model (copied from R2WinBUGS)

write_model_NoReg(): Write .bug model file for model without regression

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

write_model_Reg_discrete_predictor_NoNest(): Write .bug model file for regression model without nested subclasses