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baytrends

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.

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Install

install.packages('baytrends')

Monthly Downloads

395

Version

1.1.0

License

GPL-3

Issues

Pull Requests

Stars

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Repository

https://github.com/tetratech/baytrends

Maintainer

Erik Leppo

Last Published

March 14th, 2019

Functions in baytrends (1.1.0)

Water-quality Data Conversion

createResiduals

Calculate GAM residuals

Left-censored Data Conversion

Combine Values into a Vector

Chesapeake Bay Program Monitoring Data, 1985-2016

Expectation maximization function: Log-normal case, i censured

Describe Censoring

Describe Censoring

Water-Quality Data

Water-Quality Data

Expectation maximization function: Normal case, left censured

Expectation maximization function: Normal case, i censured

Document Processing Time and Other Session Time

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

Probability Plotting Positions

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

Expectation maximization function: Log-normal case, Cens

plots data and gam fit vs. time

Expectation maximization function: Log-normal case, left censured

.initializeResults

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

Numeric Representations

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

baseDay2decimal

Reduce dataframe and parameter list based on user selected parameterFilt

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

Prepare ANOVA table for GAM analysis

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

.layerAggregation

Aggregate data layers

Find Recent File Information

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

Expectation maximization function: Log-normal case, right censured

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

baytrends: Long Term Water Quality Trend Analysis

detrended.salinity

Create Seasonally Detrended Salinty Data Set

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

Impute Detection Limits

Multiply-censored Data Conversion

Estimate Statistics

Encode in a Common Format

Estimate Statistics

Compute an estimate of difference based on GAM results

Print out character vector table in wrapped mode

Test whether an object can be treated in a particular way

Date Conversion

Create Seasonally Detrended Flow Data Set

Expectation maximization function: Normal case

Expectation maximization function: Normal case, right censured

Retrieve USGS daily flow data in a wide format

Restructure Data

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

Arithmetic Mean

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

Paragraph (customization of pandoc.p)

Prepare table of coefficients for GAM analysis

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

Event Processing

Print out header (shortened pandoc.header)

print.censStats

Fill Missing Values

Estimate Statistics

.appendDateFeatures

Appends date features to data frame

Re-attribute df based on previous df

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

Left-censored Data

loadModelsResid

Load Built-in GAM formulas for calculating residuals

Load Built-in GAM formulas

Length of an Object

Estimate Statistics

Arithmetic Mean

Water-Quality Data

Water-Quality Data

Impute Censored Values

imputeLessThans

Impute Censored Values

residuals.censReg

Extract Model Residuals

Create Daily Seasonally-adjusted Log Flow Residuals

Check Data Range -- function that checks for allowable values

print.censQuantile

Root-Mean-Squared and Relative Differences

Estimate Statistics

Load/Clean CSV and TXT Data File

Load/Clean Excel sheet

Select data for analysis from a larger data frame

Estimate Statistics

Plot Parameters

Month Abbreviations

print.summary.censReg

Variance Inflation Factors

Display Structure

Create filter weights

Flow Averaged Predictions

Plot censored gam fits vs. time

Parameter Code Information

summary.censReg

Summarizing Linear Model Fits

Perform GAM analysis

Time-series Plots

Conversion to class "mcens"

Replace missing values

Partial Value Matching

Mutliply-censored Data

Estimate Statistics

Plot Parameters

Split Leading Qualifiers

stationMasterList

Chesapeake Bay Program long-term tidal monitoring stations

Combine Data by Rows

Possible Equality

Sample Quantiles

Water-quality Data

Save R object to disk

Estimate Statistics

Show Methods for WSqw objects

Historical Reporting Limits

Variance-Covariance Matrix

Subset an Object

Sorting Vectors

as.data.frame.lcens

Coerce to a Data Frame

analysisOrganizeData

Analysis Organization & Data Preparation

appendDateFeatures

Append Date Features

as.character.lcens

Character Representations

Invoke a Data Viewer

Add Water-Quality Data

Arithmetic Methods for lcens, mcens, and qw objects

Mathematical Transform Methods for lcens objects