fit_models: Fit all the models and return a list object suitable for...
In atyre2/unlwnv: Analyse WNV annual case data using UNL method

Description Usage Arguments Details Value

View source: R/fit_models.R

Fit all the models and return a list object suitable for DFMIP.

fit_models(
  x,
  path_2_case_data = here::here("data-raw/wnv_by_county.csv"),
  case_type = c("neuro", "all"),
  match_variable = "fips",
  case_variable = "cases",
  measure = NULL,
  nUnits = NULL,
  startUnit = NULL,
  unit = NULL,
  models = NULL
)

`x`	tibble or data frame that defines the desired prediction targets. Must have at least a FIPS code and year.
`path_2_case_data`	character file path to the case data
`case_type`	character type of case data
`match_variable`	character name of the field to match data on
`case_variable`	character name of the case field in the case data
`measure`	character vector of variables to use for lagging
`nUnits`	integer number of units to lag backwards
`startUnit`	integer unit to start lagging from
`unit`	character name of the variable identifying the lag units
`models`	list of formulas for to use. No checks are performed to ensure the formulas are consistent with other arguments.

This function uses the rows in x to define the prediction targets for the models. There must be at least two fields, the 5 digit fips code to identify the county as a character variable and a year numeric variable to identify the year.

The WNV case data cannot be posted publicly as a part of this package. This package assumes the case data are available in a subdirectory of the working directory called data-raw, in a file called wnv_by_county.csv which has rows identified by the same fips codes and a year variable. Case counts are assumed to be in a variable called "cases". There must be at least one row for each fips code in x.

a list of containing predicted cases for the target in the format expected by DFMIP

atyre2/unlwnv documentation built on Oct. 25, 2020, 10:18 a.m.