Covid19Wastewater-package | Covid19Wastewater: A package for running Covid19 wastewater concentration analysis |
Aux_info_data | Auxiliary data |
bagging | Bootstrap aggregating of dataset gen a list of dataframes using row resampling and column downsizing |
buildCaseAnalysisDF | Prep case data into right format |
buildRegressionEstimateTable | Run DHS analysis at a top level |
buildWasteAnalysisDF | Convert wastewater_data data to workset4 shape |
Case_data | Case data |
classifyCaseRegression | Create Case Flags based on regression slope |
classifyQuantileFlagRegression | Classify FlagRegression with rolling Quantile info |
classifyRegressionAnalysis | classifyRegressionAnalysis |
computeJumps | compute first difference Jumps for N1 and N2 |
computeRankQuantiles | computeRankQuantiles |
countFlags | Create counts of flag data |
Covariants_data | Covariants data |
Covid19Wastewater | Covid19Wastewater: A package for running Covid19 wastewater concentration analysis |
createCaseFlag | Create Case flags |
Data_Description | Data Description This package contains a lot of data with many column names, here is a list of them all: [https://github.com/UW-Madison-DSI/Covid19Wastewater/blob/main/docs/data/data_columns_discription.md](https://github.com/UW-Madison-DSI/Covid19Wastewater/blob/main/docs/data/data_columns_discription.md) |
date_distance_calc | date_distance_calc |
date_distance_clamp | remove distances above threshold |
date_distance_remove | remove distances above threshold |
DF_date_vector | DF_date_vector |
Example_data | Example data |
expand_formula | Expand formula for increased info takes a formula with shape A ~ B | C and convert . to its real representation |
expSmoothMod | expSmoothMod Add a column of the smoothed values using exponential smoothing |
factorVecByVec | Get ordering for ploting based on factoring vector |
flagOutliers | Create column with Boolean based on a threshold |
Flag_From_Trend | Flag values as outliers based on error from estimated trend This function can be done within group if the data fed into it was grouped |
gen_INCMSE | get increased mean square error for each column |
gen_OOB_pred | get OOB predictions of the training dataset returns the predictions of each row of the input data using only trees not trained on the row |
heatmapcorfunc | Outputs a heatmap where the color is the r squared of wastewater data and center day + x many future days and y many past days Helps inform Offset Analysis |
HFGCase_data | High frequency case data |
HFGWaste_data | High frequency Waste data |
InterceptorCase_data | Madison interceptor case data |
loessSmoothMod | loessSmoothMod Add a column of the smoothed values using Loess |
makeQuantileColumns | Add many combo of rolling quantile columns to dataframe have info for each quant window combo |
OffsetDFMaker | Returns a dataframe with the multiple ways to analyze how offset the Wastewater is from cases data |
OffsetDF_Plot | Given output from OffsetDFMaker returns a 2x3 grid of all the plots with highlighted values |
OffsetHeatmap | Outputs a heatmap of the offset for variant / time windows and population size / region |
OOB_MSE_num_trees | get OOB MSE vs number of forest in trees |
Pop_data | Sewer shed population data |
predict-method | predict new data from random_linear_forest models |
random_linear_forest | Fitting linear random forest |
random_linear_forest-class | random_linear_forest model class using a random forest of linear forest models |
rankJumps | rankJumps |
removeOutliers | Add column with NA values where the data was flagged |
sgolaySmoothMod | sgolaySmoothMod Add a column of the smoothed values using sgolayfilt |
show-method | display form for random_linear_forest class |
summary-method | summary method for linear forest class |
VariantPlot | Shows each variant in proportion to the others in 2 week time periods |
WasteWater_data | Wastewater data set |