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(),
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
(list, optional). Suffixes for each hierarchical level. Default: c(trial="trial", subset="subset", set="set").
- mctable
(data frame, optional). Monte Carlo nodes definitions. Default: set_mctable().
- 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.
Examples
imports_mcmodule <- trial_totals(
mcmodule = imports_mcmodule,
mc_names = "no_detect_a",
trials_n = "animals_n",
subsets_n = "farms_n",
subsets_p = "h_prev",
mctable = imports_mctable
)
print(imports_mcmodule$node_list$no_detect_a_set$summary)
#> mc_name pathogen origin mean sd Min 2.5%
#> 1 no_detect_a_set a nord 0.3762045 0.020160639 0.3409745 0.3430895
#> 2 no_detect_a_set a south 0.2979403 0.064452061 0.1836467 0.1886929
#> 3 no_detect_a_set a east 0.6056713 0.045969606 0.5217165 0.5265885
#> 4 no_detect_a_set b nord 0.9874075 0.008345831 0.9685013 0.9698283
#> 5 no_detect_a_set b south 0.9591605 0.008156579 0.9437077 0.9444721
#> 6 no_detect_a_set b east 0.9649050 0.022131368 0.9177085 0.9206649
#> 25% 50% 75% 97.5% Max nsv Na's
#> 1 0.3583038 0.3765279 0.3944965 0.4077648 0.4094651 1001 0
#> 2 0.2396830 0.3012511 0.3567685 0.3965107 0.4009950 1001 0
#> 3 0.5642494 0.6084654 0.6478096 0.6749833 0.6793358 1001 0
#> 4 0.9808651 0.9894472 0.9948130 0.9973653 0.9975456 1001 0
#> 5 0.9523680 0.9600577 0.9665869 0.9709897 0.9717429 1001 0
#> 6 0.9482272 0.9704360 0.9844101 0.9917260 0.9921510 1001 0