argo {argo} | R Documentation |
Wrapper for ARGO. The real work horse is glmnet package and/or linear model.
argo( data, exogen = xts::xts(NULL), N_lag = 1:52, N_training = 104, alpha = 1, use_all_previous = FALSE, mc.cores = 1, schedule = list() )
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 |
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 |
mc.cores |
number of cores to compute argo in parallel |
schedule |
list to specify prediction schedule. Default to have |
This function takes the time series and exogenous variables (optional) as input, and produces out-of-sample prediction for each time point.
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
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>.
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)