Description Usage Arguments Details Value Examples

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

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`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 |

`rm_miss` |
logical indicating if missing observations in the input data are removed |

`...` |
arguments passed from other methods |

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.

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

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