tidalmean {WRTDStidal}

Create a tidalmean class object

Description

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

Usage

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

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.

Value

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

Examples

## raw data

data(chldat)

## format
chldat <- tidalmean(chldat)