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baytrends

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The baytrends package was developed to enable users to evaluate long-term trends in the Chesapeake Bay using a Generalized Additive Modeling (GAM) approach. The model development includes selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporation of hydrologic variability via either a river flow or salinity, the use of an intervention to deal with method or laboratory changes suspected to impact data values, and representation of left- and interval-censored data. This approach, which is fully transferable to other systems, allows for Chesapeake Bay water quality data to be evaluated in a statistically rigorous, yet flexible way to provide insights to a range of management- and research-focused questions.

Installation

You can install the released version of baytrends from CRAN with:

install.packages("baytrends")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("tetratech/baytrends")

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Version

Install

install.packages('baytrends')

Monthly Downloads

429

Version

1.2.1

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/tetratech/baytrends

Maintainer

Erik Leppo

Last Published

March 31st, 2020

Functions in baytrends (1.2.1)

Daily Streamflow

Add Water-Quality Data

analysisOrganizeData

Analysis Organization & Data Preparation

Stream Water Quality

Stream Water Quality

baytrends: Long Term Water Quality Trend Analysis

baseDay2decimal

Multiply-censored Data Conversion

Numeric Representations

appendDateFeatures

Append Date Features

Invoke a Data Viewer

Document Processing Time and Other Session Time

Create Seasonally Detrended Flow Data Set

Describe Censoring

Left-censored Data Conversion

Chesapeake Bay Program Monitoring Data, 1985-2016

Arithmetic Methods for lcens, mcens, and qw objects

Combine Values into a Vector

Water-quality Data Conversion

Probability Plotting Positions

Mathematical Transform Methods for lcens objects

Describe Censoring

detrended.salinity

Create Seasonally Detrended Salinty Data Set

Water-Quality Data

Print out 2nd level header (shortened pandoc.header)

Print out 3rd level header (shortened pandoc.header)

as.character.lcens

Character Representations

Prepare ANOVA table for GAM analysis

Date Conversion

Print out header (shortened pandoc.header)

Expectation maximization function: Normal case, i censured

Print out 1st level header (shortened pandoc.header)

Expectation maximization function: Log-normal case, right censured

createResiduals

Calculate GAM residuals

Prepare table of coefficients for GAM analysis

Impute Detection Limits

Expectation maximization function: Log-normal case, i censured

Print out character vector table in wrapped mode

.appendDateFeatures

Appends date features to data frame

Expectation maximization function: Normal case, right censured

as.data.frame.lcens

Coerce to a Data Frame

Water-Quality Data

Expectation maximization function: Normal case, left censured

Print out text (blended pandoc.emphasis, .verbatim, and .strong)

Expectation maximization function: Log-normal case, left censured

Expectation maximization function: Log-normal case, Cens

Print out figure title (customization of pandoc.emphasis and pandoc.strong )

Fill Missing Values

.initializeResults

#### Initialize stat.gam.result and chng.gam.result

Print out 4th level header (shortened pandoc.header)

Print out 5th level header (shortened pandoc.header)

Check Data Range -- function that checks for allowable values

Expectation maximization function: Normal case

.layerAggregation

Aggregate data layers

Print out table title (customization of pandoc.emphasis and pandoc.strong )

gamPlotDispSeason

Plot censored gam fits vs. time

Perform GAM analysis

Event Processing

Impute Censored Values

loadModelsResid

Load Built-in GAM formulas for calculating residuals

Test whether an object can be treated in a particular way

Perform GAM analysis for Specified Season

Fit normal or log normal distribution; simulation in censored data provided by Elgin Perry phi.left and phi.right are distribution function, e.g., pnorm(q=10,mean=10,sd=1) =0.5 phi.sim is a uniform random number between phi.left and phi.right sim is imputed value

merge flow variable into analysis data frame and update iSpec with variable name

Re-attribute df based on previous df

Create filter weights

Find Recent File Information

Reduce dataframe and parameter list based on user selected parameterFilt

Paragraph (customization of pandoc.p)

imputeLessThans

Impute Censored Values

Retrieve USGS daily flow data in a wide format

Estimate Statistics

Flow Averaged Predictions

Estimate Statistics

Load/Clean Excel sheet

merge salinity into analysis data frame and update iSpec with variable name

Encode in a Common Format

Water-Quality Data

Replace missing values

Load Built-in GAM formulas

Left-censored Data

Length of an Object

Mutliply-censored Data

Estimate Statistics

Arithmetic Mean

Month Abbreviations

Estimate Statistics

Arithmetic Mean

Generate a Range of Colors

Compute and present report on percent different for log-transformed data

print.censQuantile

residuals.censReg

Extract Model Residuals

Root-Mean-Squared and Relative Differences

Parameter Code Information

summary.censReg

Summarizing Linear Model Fits

Show Methods for WSqw objects

print.censStats

Save R object to disk

Estimate Statistics

print.summary.censReg

plots data and gam fit vs. time

Variance-Covariance Matrix

Sample Quantiles

Compute an estimate of difference based on GAM results

Estimate Statistics

Plot censored gam fits vs. time

Variance Inflation Factors

Create Daily Seasonally-adjusted Log Flow Residuals

Restructure Data

Water-quality Data

Plot Parameters

stationMasterList

Chesapeake Bay Program long-term tidal monitoring stations

Display Structure

Load/Clean CSV and TXT Data File

Subset an Object

Water-Quality Data

Possible Equality

Select data for analysis from a larger data frame

Estimate Statistics

Split Leading Qualifiers

Sorting Vectors

Combine Data by Rows

Estimate Statistics

Partial Value Matching

Conversion to class "mcens"

Selected USGS parameter Codes

Historical Reporting Limits

Time-series Plots

Plot Parameters