tidalmean function

Create a tidalmean class object

Prepare water quality data for weighted regression for the mean response by creating a tidalmean class object

tidalmean(
  dat_in,
  ind = c(1, 2, 3, 4),
  reslab = NULL,
  flolab = NULL,
  reslog = TRUE,
  rm_miss = FALSE,
  ...
)

Arguments

• dat_in: Input data frame for a water quality time series with four columns for date (Y-m-d format), response variable, salinity/flow, and detection limit for left-censored data
• ind: four element numeric vector indicating column positions of date, response variable, salinity/flow, and detection limit of input data frame
• reslab: character string or expression for labelling the response variable in plots, defaults to log-chlorophyll in ug/L
• flolab: character string or expression for labelling the flow variable in plots, defaults to Salinity
• reslog: logical indicating if input response variable is already in log-space, default TRUE
• rm_miss: logical indicating if missing observations in the input data are removed
• ...: arguments passed from other methods

Returns

A tidalmean object as a data frame and attributes. The data frame has columns ordered as date, response variable, salinity/flow (rescaled to 0, 1 range), detection limit, logical for detection limit, day number, month, year, and decimal time. The attributes are as follows:

• names: Column names of the data frame
• row.names: Row names of the data frame
• class: Class of the object
• half_wins: List of numeric values used for half-window widths for model fitting, in the same order as the wt_vars argument passed to getwts. Initially will be NULL if wrtds has not been used.
• fits: List with a single element with fits for the WRTDS mean interpolation grid. Initially will be NULL if wrtds has not been used.
• predonobs: A data.frame of predictions using the observed data that were used to fit the model. This is required for wrtdsperf if a novel dataset is used for predictions after fitting the model. Initially will be NULL if respred has not been used.
• bt_fits: List with a single element with back-transformed fits for the WRTDS mean interpolation grid. Initially will be NULL if wrtds has not been used.
• flo_grd: Numeric vector of salinity/flow values that was used for the interpolation grids
• floobs_rng: Two element vector indicating the salinity/flow range of the observed data
• nobs: List with one matrix showing the number of weights greater than zero for each date and salinity/flow combination used to create the fit matrices in fits. Initially will be NULL if wrtds has not been used.
• reslab: expression or character string for response variable label in plots
• flolab: expression or character string for flow variable label in plots

Details

This function is a simple wrapper to structure that is used to create a tidalmean object for use with weighted regression in tidal waters, specifically to model the mean response as compared to a conditional quantile. Input data should be a four-column data.frame with date, response variable, salinity/flow data, and detection limit for each observation of the response. The response data are assumed to be log-transformed, otherwise use reslog = FALSE. Salinity data can be provided as fraction of freshwater or as parts per thousand. The limit column can be entered as a sufficiently small number if all values are above the detection limit or no limit exists. The current implementation of weighted regression for tidal waters only handles left-censored data. Missing observations are also removed.

The tidalmean object structure is almost identical to the tidal object, with the exception of an additional attribute for the back-transformed interpolation grid. This is included to account for retransformation bias of log-transformed variables associated with mean models.

Examples

## raw data

data(chldat)

## format
chldat <- tidalmean(chldat)