efflux: Easily fit multilple linear regressions over selected data...

In jpshanno/Ecohydro: Functions for ecological flux studies including sap flux and soil/stem C efflux.

Description

This shiny gadget provides an easy interactive method to trim sample data and fit linear regressions to each sample's data. It is specifically designed for soil and stem efflux. It will work with any type of data that has a unique sample ID and dependent/independent variables. Select points to remove from the data by clicking on them or remove multiple points by clicking and dragging a box around the points you wish to remove. Please keep in mind that this step is not for removing data you view as 'outliers'. Points should only be removed due to equipment/sampling errors or to remove efflux 'ramp up' at the beginning of sampling. After you are finished with a sample press 'Save & Next' to advance to the next sample. This will also save information about the model fit and update the output data, removing the data you selected. You can view any sample using the dropdown mean or return to the previous plot with the 'Previous' button (navigating this way will not save any of your selections). Resetting the probe will delete the saved regression information and add all of the sample points back to your plot and the output data. See the efflux web app for example datasets

Usage

efflux(input.data = NULL, run = TRUE)

Arguments

input.data	A dataset containing a response variable and one or more columns that can be used to create a unique ID
run	A logical value indicating if the application should be run, or returned to run later. Used for webapp deployment.

Value

A list consiting of a dataframe of the fitted regressions ("regressions"), a dataframe of the points removed ("removedPoints"), a dataframe of samples that were editted (processedData), a dataframe of the output data (input data merged with any changes) ("finalData"), a list of the final plots by sample ("plots")

Examples

efflux()

jpshanno/Ecohydro documentation built on May 19, 2019, 11:50 p.m.