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climwin

climwin is designed for those interested in understanding the impacts of climate, particularly focussed on biological systems. When seeking to understand the effects of climate it is necessary to select a sampling period over which climate is recorded, a climate window. Often this choice is made arbitrarily, with many studies using seasonal values (e.g. spring temperature, winter precipitation). However, these climate windows may not be the most relevant for the biological system in question. If we fail to find a relationship between climate and the biological response it can be difficult to determine whether this is due to climate insensitivity in the biological response or if the choice of climate window is flawed.

Rather than being required to make a single arbitrary choice of climate window, climwin allows users to test the effectiveness of a wide range of possible climate windows with the aim of identifying the most appropriate climate window for further use. climwin gives users the ability to visualise the results of their climate window analysis, using ggplot2, as a means to best interpret and understand the climate window results.

To install:

  • latest released version: install.packages("climwin")

How to use climwin:

Examples for both climate window analysis and plotting are provided in the package help documentation. See library(help = "climwin") once installed for more detail.

For more detailed insight on how to use climwin to investigate climate data see our introductory vignette using vignette("climwin", package = "climwin"), or our more advanced vignette with vignette("advanced_climwin", package = "climwin").

To access the current beta version please visit our github repository (LiamDBailey/climwin).

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Install

install.packages('climwin')

Monthly Downloads

304

Version

1.2.3

License

GPL-2

Issues

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Maintainer

Liam Bailey

Last Published

May 26th, 2020

Functions in climwin (1.2.3)

pvalue

Determine the probability that a given climate signal is 'true'.
crosswin

Test the correlation between two climate variables.
plotweights

Plot distribution of model weights
medwin

Determine the median start and end time for climate windows
merge_results

Merge two slidingwin analyses.
plotwin

Plot the start and end time of best climate windows
plotcor

Visualise climate cross correlation or autocorrelation.
weightwin

Find a weighted climate window
plotbetas

Plot model beta estimates
wgdev

Calculate within group deviance.
randwin

Climate window analysis for randomised data
explore

Visualise the weight distribution for given parameter values
Size

Average size of red winged fairy wren (Malurus elegans) chicks.
plotall

Visualise climate window data
SizeClimate

Daily climate data from 2006 to 2015.
autowin

Test for auto-correlation in climate.
plotbest

Visualise the best climate window
slidingwin

Test for a climate windows in data.
singlewin

Fit a single climate window
plotdelta

Plot deltaAICc of models
plothist

Create a histogram of randomised deltaAICc values
wgmean

Calculate within group means.
OffspringClimate

Daily climate data since 2009.
Monthly_data

Monthly temperature data
Chaff

Annual laying date of breeding common chaffinch (Fringilla coelebs).
Mass

Chick body mass data since 1979.
Offspring

Reproductive success of birds since 2009.
MassOutput

Example output dataframe from function slidingwin.
MassClimate

Daily climate data since 1979.
ChaffClim

Daily climate data from 1965 to 2012.
MassRand

Example output dataframe from function randwin.