lnres_err: Simulate random errors from a time series

In fawda123/WRTDStidal: Weighted Regression for Water Quality Evaluation in Tidal Waters

Simulate random errors from a time series

Description

Simulate random errors of a water quality time series by modelling the statistical properties of an observed dataset

Usage

lnres_err(dat_in, yr = NULL, comps = FALSE, seed = NULL)

Arguments

dat_in	input data.frame that must include discharge and decimal time columns, see example dataset daydat
yr	numeric year value to use for the stationary model, defaults to the median year
comps	logical indicating if the WRTDS model used to get response error measures is also returned, see value.
seed	optional numeric value for random generation seed

Details

Random errors for a stationary seasonal water quality time series on a daily time step are generated by modelling residuals from an observed dataset. First, a stationary seasonal model is created by fitting a wrtds model and estimating an error distribution the residuals using the auto.arima function. Accumulated standard errors from the regression are also retained for each residual. Random errors using the estimated auto-regressive structures are simulated using arima.sim for the entire year and multiplied by the corresponding standard error estimate from the regression. The entire year is then repeated for every year in the observed time series. The final simulated errors are rescaled to the range of the original residuals that were used to estimate the distribution.

Value

The original data frame with additional columns for the random errors (errs) and the standard error estimates for each residual (scls). If comps = TRUE, a two-element list is returned that also includes the WRTDS model used as a basis for errors and scale values.

See Also

daydat

Examples

## Not run: 
## example data
data(daydat)

## get errors
lnres_err(daydat)

## End(Not run)

fawda123/WRTDStidal documentation built on Oct. 22, 2023, 11:28 p.m.