A B C D E G H I J L M N O P R S T U V W
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 |
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 |
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 |
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()' |
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 |
jags2_baker | Run 'JAGS' from R |
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: |
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 |
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. |
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_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 |
read_meas_object | Read measurement slices |
rvbern | Sample a vector of Bernoulli variables. |
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. |
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_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 |