argo: Construct ARGO object

In argo: Accurate Estimation of Influenza Epidemics using Google Search Data

Construct ARGO object

Description

Wrapper for ARGO. The real work horse is glmnet package and/or linear model.

Usage

argo(
  data,
  exogen = xts::xts(NULL),
  N_lag = 1:52,
  N_training = 104,
  alpha = 1,
  use_all_previous = FALSE,
  mc.cores = 1,
  schedule = list()
)

Arguments

data	response variable as xts, last element can be NA. If the response is later revised, it should be an xts that resembles upper triangular square matrix, with each column being the data available as of date of column name
exogen	exogenous predictors, default is NULL
N_lag	vector of the AR model lags used, if NULL then no AR lags will be used
N_training	number of training points, if use_all_previous is true, this is the least number of training points required
alpha	penalty between lasso and ridge, alpha=1 represents lasso, alpha=0 represents ridge, alpha=NA represents no penalty
use_all_previous	boolean variable indicating whether to use "all available data" (when TRUE) or "a sliding window" (when FALSE) for training
mc.cores	number of cores to compute argo in parallel
schedule	list to specify prediction schedule. Default to have y_gap as 1, and forecast as 0, i.e., nowcasting with past week ILI available from CDC.

Details

This function takes the time series and exogenous variables (optional) as input, and produces out-of-sample prediction for each time point.

Value

A list of following named objects

• pred An xts object with the same index as input, which contains historical nowcast estimation

• coef A matrix contains historical coefficient values of the predictors.

• parm Parameter values passed to argo function.

• penalfac the value of lambda ratio selected by cross-validation, NULL if lamid is NULL or has only one level.

• penalregion the lambda ratios that has a cross validation error within one standard error of minimum cross validation error

References

Yang, S., Santillana, M., & Kou, S. C. (2015). Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences. <doi:10.1073/pnas.1515373112>.

Examples

GFT_xts <- xts::xts(exp(matrix(rnorm(180), ncol=1)), order.by = Sys.Date() - (180:1))
randomx <- xts::xts(exp(matrix(rnorm(180*100), ncol=100)), order.by = Sys.Date() - (180:1))

argo_result1 <- argo(GFT_xts)
argo_result2 <- argo(GFT_xts, exogen = randomx)

argo documentation built on May 31, 2023, 8:21 p.m.