Calculates probabilities and expected counts across hierarchical levels (trial, subset, set) in a structured population. Uses trial probabilities and handles nested sampling with conditional probabilities.
Usage
trial_totals(
mcmodule,
mc_names,
trials_n,
subsets_n = NULL,
subsets_p = NULL,
name = NULL,
prefix = NULL,
combine_prob = TRUE,
all_suffix = NULL,
level_suffix = c(trial = "trial", subset = "subset", set = "set"),
mctable = set_mctable(),
sample_design = set_sample_design(),
agg_keys = NULL,
agg_suffix = NULL,
keep_variates = FALSE,
summary = TRUE,
data_name = NULL
)Arguments
- mcmodule
(mcmodule object). Module containing input data and node structure.
- mc_names
(character vector). Node names to process.
- trials_n
(character). Trial count column name.
- subsets_n
(character, optional). Subset count column name. Default: NULL.
- subsets_p
(character, optional). Subset prevalence column name. Default: NULL.
- name
(character, optional). Custom name for output nodes. Default: NULL.
- prefix
(character, optional). Prefix for output node names. Default: NULL.
- combine_prob
(logical). If TRUE, combine probability of all nodes assuming independence. Default: TRUE.
- all_suffix
(character). Suffix for combined node name. Default: "all".
- level_suffix
(named character vector, optional). Suffixes for each hierarchical level. Default: c(trial="trial", subset="subset", set="set").
- mctable
(data frame, optional). Monte Carlo node definitions. Default: set_mctable().
- sample_design
(matrix, data frame, or list, optional). Sampling design used to create missing input nodes via
matrix_to_mcnodes(). Accepts a matrix/data frame (for example fromsensobol::sobol_matrices()) or a list with elementX(for example, output fromsensitivity::morris()). Defaults toset_sample_design().- agg_keys
(character vector, optional). Column names for aggregation. Default: NULL.
- agg_suffix
(character). Suffix for aggregated node names. Default: "hag".
- keep_variates
(logical). If TRUE, preserve individual variate values. Default: FALSE.
- summary
(logical). If TRUE, include summary statistics. Default: TRUE.
- data_name
(character, optional). Data name used to create trials_n, subsets_n and subsets_p nodes if they don't exist in mcmodule. Default: NULL.
Value
Updated mcmodule object containing combined node probabilities and probabilities/counts at trial, subset, and set levels.
References
Murray N (2004). Handbook on Import Risk Analysis for Animals and Animal Products, Volume 2: Quantitative Risk Assessment. OIE. https://rr-africa.woah.org/app/uploads/2018/03/handbook_on_import_risk_analysis_-_oie_-_vol_ii.pdf
Ross SM (2014). Introduction to Probability Models, 11th ed. Elsevier.
See also
at_least_one() for combining independent probability nodes,
agg_variates() for aggregation across grouping keys, mc_match() for aligning
nodes by keys, and mc_summary() for node summaries.
Examples
imports_mcmodule <- trial_totals(
mcmodule = imports_mcmodule,
mc_names = "no_detect",
trials_n = "animals_n",
subsets_n = "farms_n",
subsets_p = "h_prev",
mctable = imports_mctable
)
print(imports_mcmodule$node_list$no_detect_set$summary)
#> mc_name pathogen origin mean sd Min 2.5%
#> 1 no_detect_set a nord 0.3763954 0.020178697 0.3409928 0.3423215
#> 2 no_detect_set a south 0.2962294 0.061855885 0.1834814 0.1907973
#> 3 no_detect_set a east 0.6026087 0.046134225 0.5217474 0.5258519
#> 4 no_detect_set b nord 0.9877709 0.008068564 0.9685859 0.9702293
#> 5 no_detect_set b south 0.9594140 0.007977300 0.9436892 0.9450355
#> 6 no_detect_set b east 0.9665633 0.020948915 0.9177764 0.9212644
#> 25% 50% 75% 97.5% Max nsv Na's
#> 1 0.3588123 0.3772820 0.3938322 0.4084639 0.4094460 1001 0
#> 2 0.2440137 0.3008714 0.3502099 0.3949743 0.4009907 1001 0
#> 3 0.5627669 0.6032084 0.6451470 0.6751156 0.6793901 1001 0
#> 4 0.9821967 0.9895920 0.9948859 0.9973745 0.9975579 1001 0
#> 5 0.9524984 0.9602765 0.9663747 0.9713169 0.9717365 1001 0
#> 6 0.9514794 0.9727809 0.9846769 0.9915409 0.9921643 1001 0