buildRegressionEstimateTable: Run DHS analysis at a top level

In Covid19Wastewater: Prepare, Analyze, and Visualize Covid-19 Wastewater Data

Run DHS analysis at a top level

Description

buildRegressionEstimateTable is used to create a data frame that has the predicted categorization as laid out by the DHS. For each RunOn var supplied It uses the var to create a 5 day lm fit and uses the percent change to bin the results into 5 categories, "major decrease", "moderate decrease", "fluctuating", "moderate increase", and "major increase". If the model P-value if over .3 the category is replaced with "no change"

Usage

buildRegressionEstimateTable(
  DataMod,
  RunOn = "sars_cov2_adj_load_log10",
  SplitOn = "site",
  DaysRegressed = 5,
  verbose = FALSE,
  PSigTest = TRUE
)

Arguments

DataMod	The DF containing the col RunOn + date
RunOn	The col names of the values we wish to run
SplitOn	A category to separate to create independent TS data
DaysRegressed	number of days used in each regression
verbose	Bool on whether it should print out what group it is on
PSigTest	When categorizing if it should reject high pVals

Value

A DF with the associated Date and DHS analysis

Examples

library(dplyr)
data(Example_data, package = "Covid19Wastewater")
Example_data <- Example_data[Example_data$site == 'Janesville',]
Example_log_data <- mutate(Example_data, log_geo_mean = log10(geo_mean + 1))
head(buildRegressionEstimateTable(Example_log_data, SplitOn = "site", 
                                           RunOn = "log_geo_mean"))

Covid19Wastewater documentation built on Aug. 25, 2023, 1:07 a.m.