Long Term Water Quality Trend Analysis
Expectation maximization function: Log-normal case, Cens
Expectation maximization function: Log-normal case, right censured
Expectation maximization function: Normal case, i censured
Expectation maximization function: Normal case, left censured
Expectation maximization function: Normal case
Expectation maximization function: Normal case, right censured
Print out figure title (customization of pandoc.emphasis and pandoc.st...
Find Recent File Information
Format pvalues
Prepare ANOVA table for GAM analysis
Prepare table of coefficients for GAM analysis
Compute and present report on percent different for log-transformed da...
plots data and gam fit vs. time
Print out header (shortened pandoc.header)
Print out 1st level header (shortened pandoc.header)
Print out 2nd level header (shortened pandoc.header)
Print out 3rd level header (shortened pandoc.header)
Print out 4th level header (shortened pandoc.header)
Print out 5th level header (shortened pandoc.header)
merge flow variable into analysis data frame and update iSpec with var...
merge salinity into analysis data frame and update iSpec with variable...
Paragraph (customization of pandoc.p)
Analysis Organization & Data Preparation
Append Date Features
Base Day
Base Day
baytrends: Long Term Water Quality Trend Analysis
Document Processing Time and Other Session Time
Calculate GAM residuals
Decimal Time
Date Conversion
Create Seasonally Detrended Flow Data Set
Create Seasonally Detrended Salinty Data Set
Appends date features to data frame
Check Data Range -- function that checks for allowable values
Reduce dataframe and parameter list based on user selected parameterFi...
Expectation maximization function: Log-normal case, i censured
Expectation maximization function: Log-normal case, left censured
Re-attribute df based on previous df
Print out table title (customization of pandoc.emphasis and pandoc.str...
Print out text (blended pandoc.emphasis, .verbatim, and .strong)
Print out character vector table in wrapped mode
Event Processing
Fill Missing Values
Create filter weights
Flow Averaged Predictions
Compute an estimate of difference based on GAM results
Plot censored gam fits vs. time
Plot censored gam fits vs. time
Perform GAM analysis
Perform GAM analysis for Specified Season
Retrieve USGS daily flow data in a wide format
Impute Censored Values
Impute Censored Values in dataframes
Aggregate data layers
Load/Clean CSV and TXT Data File
Load/Clean Excel sheet
Load Built-in GAM formulas
Load Built-in GAM formulas for calculating residuals
Convert dataframe to include survival (Surv) objects
Recode Data
Compute the Number of Non-Missing Observations
Save R object to disk
Create Daily Seasonally-adjusted Log Flow Residuals
Select data for analysis from a larger data frame
Converts Surv object into a 3-column matrix
Converts Surv objects in a dataframe to "lo" and "hi" values
Enable users to evaluate long-term trends 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. The approach has been applied to water quality data in the Chesapeake Bay, a major estuary on the east coast of the United States to provide insights to a range of management- and research-focused questions. Methodology described in Murphy (2019) <doi:10.1016/j.envsoft.2019.03.027>.
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