MetaRVM (version 1.0.0)

MetaRVMResults: MetaRVM Results Class

Description

R6 class to handle MetaRVM simulation results with comprehensive analysis and visualization methods. This class stores formatted simulation results and provides methods for data summarization, subsetting, and visualization with flexible demographic groupings.

Author

Arindam Fadikar

Public fields

config

MetaRVMConfig object used to generate these results

results

data.table containing formatted simulation results

run_info

List containing run metadata

Methods

Public methods

• MetaRVMResults$new()

• MetaRVMResults$print()

• MetaRVMResults$subset_data()

• MetaRVMResults$summarize()

• MetaRVMResults$clone()

---

Method new()

Initialize a new MetaRVMResults object

Usage

MetaRVMResults$new(
  raw_results,
  config,
  run_info = NULL,
  formatted_results = NULL
)

Arguments

raw_results

Raw simulation results data.table

config

MetaRVMConfig object used for the simulation

run_info

Optional metadata about the run

formatted_results

formatted simulation results data.table

Returns

New MetaRVMResults object (invisible)

---

Method print()

Print summary of results

Usage

MetaRVMResults$print()

Returns

Self (invisible)

---

Method subset_data()

Subset the data based on any combination of parameters

Usage

MetaRVMResults$subset_data(
  ages = NULL,
  races = NULL,
  zones = NULL,
  disease_states = NULL,
  date_range = NULL,
  instances = NULL,
  exclude_p_columns = TRUE
)

Arguments

ages

Vector of age categories to include (default: all)

races

Vector of race categories to include (default: all)

zones

Vector of zones to include (default: all)

disease_states

Vector of disease states to include (default: all, excludes p_ columns)

date_range

Vector of two dates start_date, and end_date for filtering (default: all)

instances

Vector of instance numbers to include (default: all)

exclude_p_columns

Logical, whether to exclude p_ columns (default: TRUE)

Returns

MetaRVMResults object with subset of results

---

Method summarize()

Summarize results across specified demographic characteristics

Usage

MetaRVMResults$summarize(
  group_by,
  disease_states = NULL,
  date_range = NULL,
  stats = c("mean", "median", "sd"),
  quantiles = c(0.25, 0.75),
  exclude_p_columns = TRUE
)

Arguments

group_by

Vector of demographic variables to group by: c("age", "race", "zone")

disease_states

Vector of disease states to include (default: all, excludes p_ columns)

date_range

Optional date range for filtering

stats

Vector of statistics to calculate: c("mean", "median", "sd", "min", "max", "sum", "quantile"). If NULL, returns all instances

quantiles

Vector of quantiles to calculate if "quantile" is in stats (default: c(0.25, 0.75))

exclude_p_columns

Logical, whether to exclude p_ columns (default: TRUE)

Returns

data.table with summarized time series data or all instances if stats = NULL

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MetaRVMResults$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Details

The MetaRVMResults class automatically formats raw simulation output upon initialization, converting time steps to calendar dates and adding demographic attributes. It provides methods for flexible data summarization across any combination of age, race, and geographic zone categories, plus method chaining for streamlined analysis workflows.

Examples

# \donttest{
options(odin.verbose = FALSE)
example_config <- system.file("extdata", "example_config.yaml", package = "MetaRVM")
# Run simulation
results_obj <- metaRVM(example_config)
# Access formatted results
head(results_obj$results)

# Subset data with multiple filters
subset_data <- results_obj$subset_data(
  age = c("18-49", "50-64"), 
  disease_state = c("H", "D"),
  date_range = c(as.Date("2024-01-01"), as.Date("2024-02-01"))
)

# Method chaining for analysis and visualization
results_obj$subset_data(disease_state = "H")$summarize(
  group_by = c("age", "race"), 
  stats = c("median", "quantile"),
  quantiles = c(0.25, 0.75)
)$plot()
# }