Package 'argo' reference manual

Title:	Accurate Estimation of Influenza Epidemics using Google Search Data
Description:	Augmented Regression with General Online data (ARGO) for accurate estimation of influenza epidemics in United States on national level, regional level and state level. It replicates the method introduced in paper Yang, S., Santillana, M. and Kou, S.C. (2015) <doi:10.1073/pnas.1515373112>; Ning, S., Yang, S. and Kou, S.C. (2019) <doi:10.1038/s41598-019-41559-6>; Yang, S., Ning, S. and Kou, S.C. (2021) <doi:10.1038/s41598-021-83084-5>.
Authors:	Shaoyang Ning, Shihao Yang, S. C. Kou
Maintainer:	Shihao Yang <[email protected]>
License:	GPL-2
Version:	3.0.2
Built:	2024-11-09 04:38:09 UTC
Source:	https://github.com/cran/argo

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()
)
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

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)

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)

main function for argo

Description

main function that reproduce the results in ARGO paper

Usage

argo_main(save.folder = NULL)
argo_main(save.folder = NULL)

Arguments

save.folder

output folder to save graphics. If NULL then do not output graphics.

Examples


argo_main()


argo_main()

ARGO second step

Description

Wrapper for ARGO second step. Best linear predictor / Bayesian posterior

Usage

argo2(truth, argo1.p, argo.nat.p)
argo2(truth, argo1.p, argo.nat.p)

Arguments

`truth`	prediction target
`argo1.p`	argo first step prediction
`argo.nat.p`	argo national level prediction

References

Shaoyang Ning, Shihao Yang, S. C. Kou. Accurate Regional Influenza Epidemics Tracking Using Internet Search Data. Scientific Reports

Examples

truth <- xts::xts(exp(matrix(rnorm(180*10), ncol=10)), order.by = Sys.Date() - (180:1))
argo1.p <- xts::xts(exp(matrix(rnorm(180*10), ncol=10)), order.by = Sys.Date() - (180:1))
argo.nat.p <- xts::xts(exp(matrix(rnorm(180*10), ncol=10)), order.by = Sys.Date() - (180:1))
argo2result <- argo2(truth, argo1.p, argo.nat.p)
truth <- xts::xts(exp(matrix(rnorm(180*10), ncol=10)), order.by = Sys.Date() - (180:1))
argo1.p <- xts::xts(exp(matrix(rnorm(180*10), ncol=10)), order.by = Sys.Date() - (180:1))
argo.nat.p <- xts::xts(exp(matrix(rnorm(180*10), ncol=10)), order.by = Sys.Date() - (180:1))
argo2result <- argo2(truth, argo1.p, argo.nat.p)

main function for argo2

Description

main function that reproduce the results in ARGO2 paper

Usage

argo2_main(
  gt.folder,
  ili.folder,
  population.file,
  gft.file,
  save.folder = NULL
)
argo2_main(
  gt.folder,
  ili.folder,
  population.file,
  gft.file,
  save.folder = NULL
)

Arguments

`gt.folder`	folder with Google Trends files, which should be thousands of csv file such as "US-MA_fever cough.csv" or "US-NY_cold or flu.csv"
`ili.folder`	folder with ILINet data files: "ILINet_nat.csv" and "ILINet_regional.csv"
`population.file`	file path to population csv file
`gft.file`	file path to Google Flu Trends csv file
`save.folder`	output folder to save graphics. If NULL then do not output graphics.

References

Shaoyang Ning, Shihao Yang, S. C. Kou. Accurate Regional Influenza Epidemics Tracking Using Internet Search Data. Scientific Reports

Examples


## Not run: 
download.file("https://scholar.harvard.edu/files/syang/files/gt2016-10-24.zip",
file.path(tempdir(), "gt2016-10-24.zip"))
unzip(file.path(tempdir(), "gt2016-10-24.zip"), exdir = tempdir())
gt.folder <- file.path(tempdir(), "2016-10-19")
argo2_main(
  gt.folder=gt.folder,
  ili.folder=system.file("regiondata", "ili20161121", package = "argo"),
  population.file=system.file("regiondata", "Population.csv", package = "argo"),
  gft.file=system.file("regiondata", "GFT.txt", package = "argo")
)

## End(Not run)

## Not run: 
download.file("https://scholar.harvard.edu/files/syang/files/gt2016-10-24.zip",
file.path(tempdir(), "gt2016-10-24.zip"))
unzip(file.path(tempdir(), "gt2016-10-24.zip"), exdir = tempdir())
gt.folder <- file.path(tempdir(), "2016-10-19")
argo2_main(
  gt.folder=gt.folder,
  ili.folder=system.file("regiondata", "ili20161121", package = "argo"),
  population.file=system.file("regiondata", "Population.csv", package = "argo"),
  gft.file=system.file("regiondata", "GFT.txt", package = "argo")
)

## End(Not run)

main function for argox

Description

Main function that reproduce the results in ARGOX paper. The datasets are available at Harvard Dataverse <doi:10.7910/DVN/2IVDGK>.

Usage

argox_main(
  gt.folder,
  ili.folder,
  population.file,
  gft.file,
  mix,
  save.folder = NULL,
  NCORES = 8
)
argox_main(
  gt.folder,
  ili.folder,
  population.file,
  gft.file,
  mix,
  save.folder = NULL,
  NCORES = 8
)

Arguments

`gt.folder`	folder with Google Trends files, which should be thousands of csv file such as "US-MA_fever cough.csv" or "US-NY_cold or flu.csv"
`ili.folder`	folder with ILINet data files: "ILINet_nat.csv" and "ILINet_regional.csv"
`population.file`	file path to population csv file
`gft.file`	file path to Google Flu Trends csv file
`mix`	the weighted avarage mixing of raw state-level Google Trends data. Set to be 0 for stand-alone model. Set to be 1/3 for spatial-pooling model.
`save.folder`	output folder to save graphics. If NULL then do not output graphics.
`NCORES`	number of parallel cpu cores to be used.

References

Yang, S., Ning, S. & Kou, S.C. Use Internet search data to accurately track state level influenza epidemics. Sci Rep 11, 4023 (2021)

wrapper for bootstrap relative efficiency confidence interval

Description

This function is used to wrap the bootstrap_relative_efficiency, taking vectorized arguments.

Usage

boot_re(
  pred_data,
  period.all,
  model_good,
  bench.all,
  type,
  truth = "CDC.data",
  l = 50,
  N = 10000,
  sim = "geom",
  conf = 0.95
)
boot_re(
  pred_data,
  period.all,
  model_good,
  bench.all,
  type,
  truth = "CDC.data",
  l = 50,
  N = 10000,
  sim = "geom",
  conf = 0.95
)

Arguments

`pred_data`	A matrix that contains the truth vector and the predictions. It can be data.frame or xts object
`period.all`	vector of the periods to evaluate relative efficiency
`model_good`	The model to evaluate, must be in the column names of pred_data
`bench.all`	vector of the models to compare to, must be in the column names of pred_data
`type`	Must be one of "mse" (mean square error), "mape" (mean absolute percentage error), or "mae" (mean absolute error)
`truth`	the column name of the truth
`l`	stationary bootstrap mean block length
`N`	number of bootstrap samples
`sim`	simulation method, pass to boot::tsboot
`conf`	confidence level

Value

A vector of point estimate and corresponding bootstrap confidence interval

Examples

GFT_xts = xts::xts(exp(matrix(rnorm(500), ncol=5)), order.by = Sys.Date() - (100:1))
names(GFT_xts) <- paste0("col", 1:ncol(GFT_xts))
names(GFT_xts)[1] <- "CDC.data"

boot_re(
  pred_data = GFT_xts,
  period.all = c(paste0(zoo::index(GFT_xts)[1], "/", zoo::index(GFT_xts)[50]),
                 paste0(zoo::index(GFT_xts)[51], "/", zoo::index(GFT_xts)[100])),
  model_good = "col2",
  bench.all = c("col3", "col4"),
  type = "mse",
  truth="CDC.data",
  l = 5,
  N = 20
)

GFT_xts = xts::xts(exp(matrix(rnorm(500), ncol=5)), order.by = Sys.Date() - (100:1))
names(GFT_xts) <- paste0("col", 1:ncol(GFT_xts))
names(GFT_xts)[1] <- "CDC.data"

boot_re(
  pred_data = GFT_xts,
  period.all = c(paste0(zoo::index(GFT_xts)[1], "/", zoo::index(GFT_xts)[50]),
                 paste0(zoo::index(GFT_xts)[51], "/", zoo::index(GFT_xts)[100])),
  model_good = "col2",
  bench.all = c("col3", "col4"),
  type = "mse",
  truth="CDC.data",
  l = 5,
  N = 20
)

bootstrap relative efficiency confidence interval

Description

This function is used to reproduce the ARGO bootstrap confidence interval

Usage

bootstrap_relative_efficiency(
  pred_data,
  model_good,
  model_bench,
  l = 50,
  N = 10000,
  truth = "CDC.data",
  sim = "geom",
  conf = 0.95,
  type = c("mse", "mape", "mae", "mspe", "rmse", "rmspe")
)
bootstrap_relative_efficiency(
  pred_data,
  model_good,
  model_bench,
  l = 50,
  N = 10000,
  truth = "CDC.data",
  sim = "geom",
  conf = 0.95,
  type = c("mse", "mape", "mae", "mspe", "rmse", "rmspe")
)

Arguments

`pred_data`	A matrix that contains the truth vector and the predictions. It can be data.frame or xts object
`model_good`	The model to evaluate, must be in the column names of pred_data
`model_bench`	The model to compare to, must be in the column names of pred_data
`l`	stationary bootstrap mean block length
`N`	number of bootstrap samples
`truth`	the column name of the truth
`sim`	simulation method, pass to boot::tsboot
`conf`	confidence level
`type`	Must be one of "mse" (mean square error), "mape" (mean absolute percentage error), or "mae" (mean absolute error)

Value

A vector of point estimate and corresponding bootstrap confidence interval

Examples

GFT_xts = xts::xts(exp(matrix(rnorm(1000), ncol=5)), order.by = Sys.Date() - (200:1))
names(GFT_xts) <- paste0("col", 1:ncol(GFT_xts))
names(GFT_xts)[1] <- "CDC.data"
bootstrap_relative_efficiency(
  pred_data = GFT_xts,
  model_good = "col2",
  model_bench = "col3",
  truth="CDC.data",
  N = 100
)
GFT_xts = xts::xts(exp(matrix(rnorm(1000), ncol=5)), order.by = Sys.Date() - (200:1))
names(GFT_xts) <- paste0("col", 1:ncol(GFT_xts))
names(GFT_xts)[1] <- "CDC.data"
bootstrap_relative_efficiency(
  pred_data = GFT_xts,
  model_good = "col2",
  model_bench = "col3",
  truth="CDC.data",
  N = 100
)

Parsing each Google Trends file downloaded from Google Trends API

Description

Parsing each Google Trends file downloaded from Google Trends API

Usage

gt.parser.pub.api(gt.folder, f)
gt.parser.pub.api(gt.folder, f)

Arguments

`gt.folder`	folder that contains Google Trends file
`f`	filename for Google Trends file

Parsing each Google Trends file downloaded from website

Description

Parsing each Google Trends file downloaded from website

Usage

gt.parser.pub.web(gt.folder, f)
gt.parser.pub.web(gt.folder, f)

Arguments

`gt.folder`	folder that contains Google Trends file
`f`	filename for Google Trends file

Heatmap plot of ARGO coefficients applied on CDC's ILI data

Description

Heatmap plot of ARGO coefficients applied on CDC's ILI data

Usage

heatmap_argo(argo_coef, lim = 0.1, na.grey = TRUE, scale = 1)
heatmap_argo(argo_coef, lim = 0.1, na.grey = TRUE, scale = 1)

Arguments

`argo_coef`	The coefficient matrix
`lim`	the limit to truncate for large coefficients for better presentation
`na.grey`	whether to plot grey for NA values
`scale`	margin scale

Value

a graph on the default plot window

Examples

cor_coef <- matrix(runif(100, -1, 1), ncol=10)
colnames(cor_coef) <- as.character(Sys.Date() - 10:1)
rownames(cor_coef) <- paste0("row", 1:10)
pdf(file.path(tempdir(), "heatmap_argo.pdf"), height=11,width=12)
heatmap_argo(cor_coef)
dev.off()

cor_coef <- matrix(runif(100, -1, 1), ncol=10)
colnames(cor_coef) <- as.character(Sys.Date() - 10:1)
rownames(cor_coef) <- paste0("row", 1:10)
pdf(file.path(tempdir(), "heatmap_argo.pdf"), height=11,width=12)
heatmap_argo(cor_coef)
dev.off()

Heatmap plot of correlation matrix

Description

Heatmap plot of correlation matrix

Usage

heatmap_cor(cor_heat, lim = 1)
heatmap_cor(cor_heat, lim = 1)

Arguments

`cor_heat`	The coefficient matrix to draw heatmap
`lim`	the limit to truncate for large coefficients for better presentation

Value

a graph on the default plot window

Examples

cor_coef <- matrix(runif(100, -1, 1), ncol=10)
colnames(cor_coef) <- paste0("col", 1:10)
rownames(cor_coef) <- paste0("row", 1:10)
heatmap_cor(cor_coef)

cor_coef <- matrix(runif(100, -1, 1), ncol=10)
colnames(cor_coef) <- paste0("col", 1:10)
rownames(cor_coef) <- paste0("row", 1:10)
heatmap_cor(cor_coef)

Parsing of raw data

Description

Data related to the PNAS paper. Accessed on Nov 14, 2015.

Usage

load_data(type = "extdata", ili.weighted = TRUE)
load_data(type = "extdata", ili.weighted = TRUE)

Arguments

`type`	the type of the data to be loaded. If `type=="extdata"` it loads the data to reproduce the PNAS paper, and if `type=="athdata"` it loads the data to reproduce the CID(?) paper.
`ili.weighted`	logical indicator to specify whether to load weighted ILI or not, if `FALSE` unweighted ILI is loaded.

Details

Parse and load CDC's ILI data, Google Flu Trend data, Google Correlate data trained with ILI as of 2010, Google Correlate data trained with ILI as of 2009, Google Trend data with search terms identified from Google Correlate (2010 version).

Each week ends on the Saturday indicated in the xts object

Google Correlate data is standardized by Google, and we rescale it to 0 – 100 during parsing. Google Trends data is in the scale of 0 – 100.

Value

A list of following named xts objects if type=="extdata"

GC10 Google Correlate trained with ILI available as of 2010. Google Correlate has been deprecated by Google as of Dec 2019 and is no longer publicly available.
GC09 Google Correlate trained with ILI available as of 2009.
GT Google Trends data for search queries identified using Google Correlate. Not directly available online, you have to manually input query terms at https://trends.google.com/trends/
CDC CDC's ILI dataset. Available online at https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html
GFT Google Flu Trend (historical predictions).

A list of following named xts objects if type=="athdata"

GT Google Trends data for search queries identified. Not directly available online, you have to manually input query terms at https://trends.google.com/trends/
CDC CDC's ILI dataset. Available online at https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html
ili_idx the indexing information that includes the week number and year number, the date of ending Saturday, and the season number Available online at https://www.cdc.gov/flu/weekly/
ATH Athenahealth data that includes the proportion of "Flu Visit", "ILI Visit", and "Unspecified Viral or ILI Visit" compared to total number of visit to the Athenahealth partner healthcare providers.
ili_unrevised Historical unrevised ILI activity level. The unrevised ILI published on week ZZ of season XXXX-YYYY is available at www.cdc.gov/flu/weekly/weeklyarchivesXXXX-YYYY/data/senAllregtZZ.html or .htm. For example, original ILI report for week 7 of season 2015-2016 is available at https://www.cdc.gov/flu/weekly/weeklyarchives2015-2016/data/senAllregt07.html, and original ILI report for week 50 of season 2012-2013 is available at https://www.cdc.gov/flu/weekly/weeklyarchives2012-2013/data/senAllregt50.htm

References

Examples

system.file("extdata", "correlate-Influenza_like_Illness_h1n1_CDC_.csv", package = "argo")
system.file("extdata", "correlate-Influenza_like_Illness_CDC_.csv", package = "argo")
system.file("extdata", "GFT.csv", package = "argo")
system.file("extdata", "ILINet.csv", package = "argo")
load_data()

system.file("extdata", "correlate-Influenza_like_Illness_h1n1_CDC_.csv", package = "argo")
system.file("extdata", "correlate-Influenza_like_Illness_CDC_.csv", package = "argo")
system.file("extdata", "GFT.csv", package = "argo")
system.file("extdata", "ILINet.csv", package = "argo")
load_data()

Parsing of raw data for regional ILI estimation

Description

Parsing of raw data for regional ILI estimation

Usage

load_reg_data(
  gt.folder,
  ili.folder,
  population.file,
  gft.file,
  gt.parser = gt.parser.pub.web
)
load_reg_data(
  gt.folder,
  ili.folder,
  population.file,
  gft.file,
  gt.parser = gt.parser.pub.web
)

Arguments

`gt.folder`	folder with all Google Trends data
`ili.folder`	folder with all ILI data
`population.file`	csv file path with state population data
`gft.file`	csv file path for Google Flu Trends
`gt.parser`	Google Trends data parser function, could be 'gt.parser.pub.web' or 'gt.parser.pub.api'

References

Shaoyang Ning, Shihao Yang, S. C. Kou. Accurate Regional Influenza Epidemics Tracking Using Internet Search Data. Scientific Reports

Examples


download.file("https://scholar.harvard.edu/files/syang/files/gt2016-10-24.zip",
file.path(tempdir(), "gt2016-10-24.zip"))
unzip(file.path(tempdir(), "gt2016-10-24.zip"), exdir = tempdir())
gt.folder <- file.path(tempdir(), "2016-10-19")

data_parsed <- load_reg_data(
  gt.folder=gt.folder,
  ili.folder=system.file("regiondata", "ili20161121", package = "argo"),
  population.file=system.file("regiondata", "Population.csv", package = "argo"),
  gft.file=system.file("regiondata", "GFT.txt", package = "argo")
)



download.file("https://scholar.harvard.edu/files/syang/files/gt2016-10-24.zip",
file.path(tempdir(), "gt2016-10-24.zip"))
unzip(file.path(tempdir(), "gt2016-10-24.zip"), exdir = tempdir())
gt.folder <- file.path(tempdir(), "2016-10-19")

data_parsed <- load_reg_data(
  gt.folder=gt.folder,
  ili.folder=system.file("regiondata", "ili20161121", package = "argo"),
  population.file=system.file("regiondata", "Population.csv", package = "argo"),
  gft.file=system.file("regiondata", "GFT.txt", package = "argo")
)

logit function

Description

logit function

Usage

logit(x)
logit(x)

Arguments

`x`	numeric value for logit transformation

Examples

logit(0.5)
logit(0.5)

inverse logit function

Description

inverse logit function

Usage

logit_inv(x)
logit_inv(x)

Arguments

`x`	numeric value for inverse logit transformation

Examples

logit_inv(0)
logit_inv(0)

Parsing of Google Trends data

Description

Parsing of Google Trends data

Usage

parse_gt_weekly(folder)
parse_gt_weekly(folder)

Arguments

folder

folder with weekly Google Trends file

References

Examples



download.file("https://scholar.harvard.edu/files/syang/files/gt2016-10-24.zip",
file.path(tempdir(), "gt2016-10-24.zip"))
unzip(file.path(tempdir(), "gt2016-10-24.zip"), exdir = tempdir())
gt.folder <- file.path(tempdir(), "2016-10-19")
parsed_data <- parse_gt_weekly(gt.folder)


download.file("https://scholar.harvard.edu/files/syang/files/gt2016-10-24.zip",
file.path(tempdir(), "gt2016-10-24.zip"))
unzip(file.path(tempdir(), "gt2016-10-24.zip"), exdir = tempdir())
gt.folder <- file.path(tempdir(), "2016-10-19")
parsed_data <- parse_gt_weekly(gt.folder)

Parsing of unrevised ili from online source

Description

Parsing of unrevised ili from online source

Usage

parse_unrevised_ili(type = "extdata", ili.weighted = TRUE)
parse_unrevised_ili(type = "extdata", ili.weighted = TRUE)

Arguments

`type`	the type of data folder to parse
`ili.weighted`	indicator to use weighted ILI or not

References

Examples



parse_unrevised_ili()


parse_unrevised_ili()

Time series plot of ARGO applied on CDC's ILI data

Description

This function is used to reproduce the ARGO plot.

Usage

plot_argo(GFT_xts, GC_GT_cut_date, model_names, legend_names, zoom_periods)
plot_argo(GFT_xts, GC_GT_cut_date, model_names, legend_names, zoom_periods)

Arguments

`GFT_xts`	dataframe with all predicted values
`GC_GT_cut_date`	cutting date for switching datasets
`model_names`	name of predicting models
`legend_names`	legend for predicting models
`zoom_periods`	vector of periods to zoom into

Value

a graph on the default plot window

Examples

GFT_xts = xts::xts(exp(matrix(rnorm(1000), ncol=5)), order.by = Sys.Date() - (200:1))
names(GFT_xts) <- paste0("col", 1:ncol(GFT_xts))
names(GFT_xts)[1] <- "CDC.data"
zoom_periods = c()
for (i in 0:5){
  zoom_periods = c(
    zoom_periods,
    paste0(zoo::index(GFT_xts)[i*30+1], "/", zoo::index(GFT_xts)[i*30+30])
  )
}
plot_argo(
  GFT_xts = GFT_xts,
  GC_GT_cut_date = zoo::index(GFT_xts)[50],
  model_names = colnames(GFT_xts)[-1],
  legend_names = paste0(colnames(GFT_xts)[-1], "legend"),
  zoom_periods = zoom_periods
)

GFT_xts = xts::xts(exp(matrix(rnorm(1000), ncol=5)), order.by = Sys.Date() - (200:1))
names(GFT_xts) <- paste0("col", 1:ncol(GFT_xts))
names(GFT_xts)[1] <- "CDC.data"
zoom_periods = c()
for (i in 0:5){
  zoom_periods = c(
    zoom_periods,
    paste0(zoo::index(GFT_xts)[i*30+1], "/", zoo::index(GFT_xts)[i*30+30])
  )
}
plot_argo(
  GFT_xts = GFT_xts,
  GC_GT_cut_date = zoo::index(GFT_xts)[50],
  model_names = colnames(GFT_xts)[-1],
  legend_names = paste0(colnames(GFT_xts)[-1], "legend"),
  zoom_periods = zoom_periods
)

performance summary of ARGO applied on CDC's ILI data

Description

performance summary of ARGO applied on CDC's ILI data

Usage

summary_argo(
  GFT_xts,
  model_names,
  legend_names,
  periods,
  whole_period = "2009-03/2015-10"
)
summary_argo(
  GFT_xts,
  model_names,
  legend_names,
  periods,
  whole_period = "2009-03/2015-10"
)

Arguments

`GFT_xts`	dataframe with all predicted values
`model_names`	name of predicting models
`legend_names`	legend for predicting models
`periods`	vector of periods to zoom into
`whole_period`	the whole period duration

Value

A list of summary tables for the input periods, including RMSE, MAE, MAPE, corr

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>. Shaoyang Ning, Shihao Yang, S. C. Kou. Accurate Regional Influenza Epidemics Tracking Using Internet Search Data. Scientific Reports

Examples

GFT_xts = xts::xts(exp(matrix(rnorm(1000), ncol=10)), order.by = Sys.Date() - (100:1))
names(GFT_xts) <- paste0("col", 1:10)
names(GFT_xts)[1] <- "CDC.data"
summary_argo(
  GFT_xts = GFT_xts,
  model_names = colnames(GFT_xts)[-1],
  legend_names = paste0(colnames(GFT_xts)[-1], "legend"),
  periods = c(paste0(zoo::index(GFT_xts)[1], "/", zoo::index(GFT_xts)[49]),
              paste0(zoo::index(GFT_xts)[50], "/", zoo::index(GFT_xts)[100])),
  whole_period="2009-03/"
)

GFT_xts = xts::xts(exp(matrix(rnorm(1000), ncol=10)), order.by = Sys.Date() - (100:1))
names(GFT_xts) <- paste0("col", 1:10)
names(GFT_xts)[1] <- "CDC.data"
summary_argo(
  GFT_xts = GFT_xts,
  model_names = colnames(GFT_xts)[-1],
  legend_names = paste0(colnames(GFT_xts)[-1], "legend"),
  periods = c(paste0(zoo::index(GFT_xts)[1], "/", zoo::index(GFT_xts)[49]),
              paste0(zoo::index(GFT_xts)[50], "/", zoo::index(GFT_xts)[100])),
  whole_period="2009-03/"
)

Package 'argo'

Help Index

Construct ARGO object

Description

Usage

Arguments

Details

Value

References

Examples

main function for argo

Description

Usage

Arguments

Examples

ARGO second step

Description

Usage

Arguments

References

Examples

main function for argo2

Description

Usage

Arguments

References

Examples

main function for argox

Description

Usage

Arguments

References

wrapper for bootstrap relative efficiency confidence interval

Description

Usage

Arguments

Value

Examples

bootstrap relative efficiency confidence interval

Description

Usage

Arguments

Value

Examples

Parsing each Google Trends file downloaded from Google Trends API

Description

Usage

Arguments

Parsing each Google Trends file downloaded from website

Description

Usage

Arguments

Heatmap plot of ARGO coefficients applied on CDC's ILI data

Description

Usage

Arguments

Value

Examples

Heatmap plot of correlation matrix

Description

Usage

Arguments

Value

Examples

Parsing of raw data

Description

Usage

Arguments

Details

Value

References

Examples

Parsing of raw data for regional ILI estimation

Description

Usage

Arguments

References

Examples

logit function

Description