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 0.3768013 0.019797220 0.3409688 0.3430743
#> 2 no_detect_a_set a south 0.2992284 0.061972556 0.1830923 0.1919399
#> 3 no_detect_a_set a east 0.6050367 0.046715642 0.5218684 0.5252002
#> 4 no_detect_a_set b nord 0.9875273 0.008102894 0.9683797 0.9703423
#> 5 no_detect_a_set b south 0.9588507 0.008241670 0.9437465 0.9447071
#> 6 no_detect_a_set b east 0.9665867 0.021352014 0.9176600 0.9219238
#> 25% 50% 75% 97.5% Max nsv Na's
#> 1 0.3592537 0.3770267 0.3942529 0.4080394 0.4094767 1001 0
#> 2 0.2473868 0.3008791 0.3537422 0.3962572 0.4012382 1001 0
#> 3 0.5651552 0.6067209 0.6454629 0.6760700 0.6793728 1001 0
#> 4 0.9819670 0.9896832 0.9945147 0.9974028 0.9975371 1001 0
#> 5 0.9516137 0.9596882 0.9662239 0.9711626 0.9717038 1001 0
#> 6 0.9509360 0.9728678 0.9854264 0.9917186 0.9921640 1001 0