STOCfree_data: STOCfree_data class

In AurMad/STOCfree: STOC free model: prediction of probability of freedom from longitudinal data

STOCfree_data class

Description

STOCfree_data class

Usage

STOCfree_data(
  test_data = data.frame(),
  test_herd_col = NULL,
  test_date_col = NULL,
  test_res_col = NULL,
  test_name_col = NULL,
  status_dynamics_scale = "logit",
  risk_factor_data = NULL,
  risk_herd_col = NULL,
  risk_date_col = NULL,
  risk_factor_col = NULL,
  risk_factor_type = c("continuous", "categorical"),
  lag1 = 0,
  lag2 = 0,
  time_interval = c("month", "year", "week"),
  FUN = sum
)

Arguments

test_data	a data.frame containing test results
test_herd_col	name of the column with herd / farm identifiers
test_date_col	name of the column with date of test
test_res_col	name of the column with test results. Test results should be codes as 0 for negative results and 1 for positive results.
test_name_col	when several tests are used, name of the column containing the test names
status_dynamics_scale	scale on which priors for status dynamics are defined. The default is 'proba' and uses Beta priors. If 'logit' is used as an argument, the prior distributions will be normal distributions on the logit scale.
risk_factor_data	a data.frame containing the risk factors
risk_herd_col	name of the column with herd / farm identifiers
risk_date_col	name of the column with date when the risk factors apply
risk_factor_col	name of the column(s) with risk factor values
risk_factor_type	risk factor type. Must be either continuous or categorical
lag1	start of the time interval for risk factor aggregation
lag2	end of the time interval for risk factor aggregation
time_interval	function used for risk factor aggregation. By default sum() is used, the values are added.
FUN	function used when aggregating the data. By default sum() is used, the values are added.

Value

A list of class STOCfree_data. A specific sub-class is defined based on the input.

AurMad/STOCfree documentation built on Sept. 13, 2022, 3:20 a.m.