| Title: | Climate Window Analysis |
|---|---|
| Description: | Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response. Please see van de Pol et al. (2016) <doi:10.1111/2041-210X.12590> and Bailey and van de Pol (2016) <doi:10.1371/journal.pone.0167980> for details. |
| Authors: | Liam D. Bailey and Martijn van de Pol |
| Maintainer: | Liam D. Bailey <[email protected]> |
| License: | GPL-2 |
| Version: | 1.2.3 |
| Built: | 2025-01-23 03:24:26 UTC |
| Source: | https://github.com/liamdbailey/climwin |
Tests the correlation between the climate in a specified climate window and other fitted climate windows.
autowin( reference, xvar, cdate, bdate, baseline, range, stat, func, type, refday, cmissing = FALSE, cinterval = "day", upper = NA, lower = NA, binary = FALSE, centre = list(NULL, "both"), cohort = NULL, spatial = NULL, cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL, thresh = NULL )autowin( reference, xvar, cdate, bdate, baseline, range, stat, func, type, refday, cmissing = FALSE, cinterval = "day", upper = NA, lower = NA, binary = FALSE, centre = list(NULL, "both"), cohort = NULL, spatial = NULL, cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL, thresh = NULL )
reference |
Reference climate data to be compared. Generated by functions
|
xvar |
The climate variable of interest. Please specify the parent environment and variable name (e.g. Climate$Temp). |
cdate |
The climate date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. Climate$Date). |
bdate |
The biological date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. Biol$Date). |
baseline |
The baseline model used to fit climate windows. These will be correlated with the reference climate window. |
range |
Two values signifying respectively the furthest and closest number of time intervals (set by cinterval) back from the cutoff date or biological record to include in the climate window search. |
stat |
The aggregate statistic used to analyse the climate data. Can
currently use basic R statistics (e.g. mean, min), as well as slope.
Additional aggregate statistics can be created using the format function(x)
(...). See parameter FUN in |
func |
The function used to fit the climate variable. Can be linear ("lin"), quadratic ("quad"), cubic ("cub"), inverse ("inv") or log ("log"). Not required when a variable is provided for parameter 'centre'. |
type |
"absolute" or "relative", whether you wish the climate window to be relative (e.g. the number of days before each biological record is measured) or absolute (e.g. number of days before a set point in time). |
refday |
If type is "absolute", the day and month respectively of the year from which the absolute window analysis will start. |
cmissing |
cmissing Determines what should be done if there are missing climate data. Three approaches are possible: - FALSE; the function will not run if missing climate data is encountered. An object 'missing' will be returned containing the dates of missing climate. - "method1"; missing climate data will be replaced with the mean climate of the preceding and following 2 days. - "method2"; missing climate data will be replaced with the mean climate of all records on the same date. |
cinterval |
The resolution at which climate window analysis will be conducted. May be days ("day"), weeks ("week"), or months ("month"). Note the units of parameter 'range' will differ with the choice of cinterval. |
upper |
Cut-off value used to determine growing degree days or positive climate thresholds (depending on parameter thresh). Note that when values of lower and upper are both provided, autowin will instead calculate an optimal climate zone. |
lower |
Cut-off value used to determine chill days or negative climate thresholds (determined by parameter thresh). Note that when values of lower and upper are both provided, autowin will instead calculate an optimal climate zone. |
binary |
TRUE or FALSE. Determines whether to use values of upper and lower to calculate binary climate data (binary = TRUE), or to use for growing degree days (binary = FALSE). |
centre |
A list item containing: 1. The variable used for mean centring (e.g. Year, Site, Individual). Please specify the parent environment and variable name (e.g. Biol$Year). 2. Whether the model should include both within-group means and variance ("both"), only within-group means ("mean"), or only within-group variance ("dev"). |
cohort |
A variable used to group biological records that occur in the same biological season but cover multiple years (e.g. southern hemisphere breeding season). By default, autowin will use year (extracted from parameter bdate) as the cohort variable. The cohort variable should be in the same dataset as the variable bdate. |
spatial |
A list item containing: 1. A factor that defines which spatial group (i.e. population) each biological record is taken from. The length of this factor should correspond to the length of the biological dataset. 2. A factor that defines which spatial group (i.e. population) climate data corresponds to. The length of this factor should correspond to the length of the climate dataset. |
cutoff.day, cutoff.month
|
Redundant parameters. Now replaced by refday. |
furthest, closest
|
Redundant parameters. Now replaced by range. |
thresh |
Redundant parameter. Now replaced by binary. |
Will return a data frame showing the correlation between the climate in each fitted window and the chosen reference window.
Liam D. Bailey and Martijn van de Pol
#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] single <- singlewin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") auto <- autowin(reference = single, xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), stat = "mean", func = "lin", type = "relative", cmissing = FALSE, cinterval = "day") ## Not run: # Full example # Test for auto-correlation using 'Mass' and 'MassClimate' data frames data(Mass) data(MassClimate) # Fit a single climate window using the datasets Mass and MassClimate. single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # Test the autocorrelation between the climate in this single window and other climate windows. auto <- autowin(reference = single, xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(365, 0), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # View the output head(auto) # Plot the output plotcor(auto, type = "A") ## End(Not run)#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] single <- singlewin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") auto <- autowin(reference = single, xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), stat = "mean", func = "lin", type = "relative", cmissing = FALSE, cinterval = "day") ## Not run: # Full example # Test for auto-correlation using 'Mass' and 'MassClimate' data frames data(Mass) data(MassClimate) # Fit a single climate window using the datasets Mass and MassClimate. single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # Test the autocorrelation between the climate in this single window and other climate windows. auto <- autowin(reference = single, xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(365, 0), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # View the output head(auto) # Plot the output plotcor(auto, type = "A") ## End(Not run)
Average annual laying date of common chaffinch (Fringilla coelebs) measured over 47 years.
A data frame with 47 rows and 3 variables.
Year of laying date measurement.
Average date of measurement.
Average annual laying date in days after January 1st.
Maximum daily temperature and average rainfall data
from 1965 to 2012. Coincides with biological data
from Chaff.
A data frame with 17,520 rows and 3 variables.
Date when climate was recorded (dd/mm/yyyy).
Average daily rainfall data in mm.
Maximum daily temperature in degrees centigrade.
Test the correlation between two climate variables.
crosswin( xvar, xvar2, cdate, bdate, range, stat, stat2, type, refday, cinterval = "day", cmissing = FALSE, spatial = NULL, cohort = NULL, cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL )crosswin( xvar, xvar2, cdate, bdate, range, stat, stat2, type, refday, cinterval = "day", cmissing = FALSE, spatial = NULL, cohort = NULL, cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL )
xvar |
The first climate variable of interest. Please specify the parent environment and variable name (e.g. Climate$Temp). |
xvar2 |
The second climate variable of interest. Please specify the parent environment and variable name (e.g. Climate$Temp). |
cdate |
The climate date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. Climate$Date). |
bdate |
The biological date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. Biol$Date). |
range |
Two values signifying respectively the furthest and closest number of time intervals (set by cinterval) back from the cutoff date or biological record to include in the climate window search. |
stat |
The aggregate statistic used to analyse the climate data. Can
currently use basic R statistics (e.g. mean, min), as well as slope.
Additional aggregate statistics can be created using the format function(x)
(...). See FUN in |
stat2 |
Second aggregate statistic used to analyse climate data (xvar2). Can
currently use basic R statistics (e.g. mean, min), as well as slope.
Additional aggregate statistics can be created using the format function(x)
(...). See FUN in |
type |
"absolute" or "relative", whether you wish the climate window to be relative (e.g. the number of days before each biological record is measured) or absolute (e.g. number of days before a set point in time). |
refday |
If type is absolute, the day and month respectively of the year from which the absolute window analysis will start. |
cinterval |
The resolution at which climate window analysis will be conducted. May be days ("day"), weeks ("week"), or months ("month"). Note the units of parameter 'range' will differ depending on the choice of cinterval |
cmissing |
cmissing Determines what should be done if there are missing climate data. Three approaches are possible: - FALSE; the function will not run if missing climate data is encountered. An object 'missing' will be returned containing the dates of missing climate. - "method1"; missing climate data will be replaced with the mean climate of the preceding and following 2 days. - "method2"; missing climate data will be replaced with the mean climate of all records on the same date. |
spatial |
A list item containing: 1. A factor that defines which spatial group (i.e. population) each biological record is taken from. The length of this factor should correspond to the length of the biological dataset. 2. A factor that defines which spatial group (i.e. population) climate data corresponds to. This length of this factor should correspond to the length of the climate dataset. |
cohort |
A variable used to group biological records that occur in the same biological season but cover multiple years (e.g. southern hemisphere breeding season). By default, autowin will use year (extracted from parameter bdate) as the cohort variable. The cohort variable should be in the same dataset as the variable bdate. |
cutoff.day, cutoff.month
|
Redundant parameters. Now replaced by refday. |
furthest, closest
|
Redundant parameters. Now replaced by range. |
Will return a dataframe containing the correlation between the two climate variables.
Liam D. Bailey and Martijn van de Pol
#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] cross <- crosswin(xvar = list(Temp = clim_data$Temp), xvar2 = list(Rain = clim_data$Rain), cdate = clim_data$Date, bdate = biol_data$Date, range = c(1, 0), stat = "mean", stat2 = "mean", type = "relative", cmissing = FALSE, cinterval = "day") ## Not run: # Full working example # Test correlation between temperature and rainfall in the MassClimate dataset. data(Mass) data(MassClimate) cross <- crosswin(xvar = list(Temp = MassClimate$Temp), xvar2 = list(Rain = MassClimate$Rain), cdate = MassClimate$Date, bdate = Mass$Date, range = c(365, 0), stat = "mean", stat2 = "mean", type = "relative", cmissing = FALSE, cinterval = "day") # View the output head(cross) # Plot the output plotcor(cross, type = "C") ## End(Not run)#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] cross <- crosswin(xvar = list(Temp = clim_data$Temp), xvar2 = list(Rain = clim_data$Rain), cdate = clim_data$Date, bdate = biol_data$Date, range = c(1, 0), stat = "mean", stat2 = "mean", type = "relative", cmissing = FALSE, cinterval = "day") ## Not run: # Full working example # Test correlation between temperature and rainfall in the MassClimate dataset. data(Mass) data(MassClimate) cross <- crosswin(xvar = list(Temp = MassClimate$Temp), xvar2 = list(Rain = MassClimate$Rain), cdate = MassClimate$Date, bdate = Mass$Date, range = c(365, 0), stat = "mean", stat2 = "mean", type = "relative", cmissing = FALSE, cinterval = "day") # View the output head(cross) # Plot the output plotcor(cross, type = "C") ## End(Not run)
Create a plot of the Weibull or Generalised Extreme Values (GEV) distribution
for given values of shape, scale and location parameters. Used to determine
initial parameter values for weightwin.
explore(shape = 1, scale = 1, loc = 0, weightfunc = "W")explore(shape = 1, scale = 1, loc = 0, weightfunc = "W")
shape |
A parameter that determines the shape of the distribution. Should be greater than 0. |
scale |
A parameter that determines the scale of the distribution. Should be greater than 0. |
loc |
A parameter that determines the location of the distribution. Should be less than or equal to 0. |
weightfunc |
Choose whether to use a weibull ("W") or GEV ("G") distribution. |
explore will return an example plot of the distribution using
given parameter values. This can be used to select the initial parameter
values for weightwin
Martijn van de Pol and Liam D. Bailey
# Test a weibull distribution explore(shape = 3, scale = 0.2, loc = 0, weightfunc = "W") # Test a GEV distribution explore(shape = 3, scale = 5, loc = -5, weightfunc = "G")# Test a weibull distribution explore(shape = 3, scale = 0.2, loc = 0, weightfunc = "W") # Test a GEV distribution explore(shape = 3, scale = 5, loc = -5, weightfunc = "G")
Artificially generated data representing average body mass of bird chicks since 1979.
A data frame with 47 rows and 2 variables
Date of mass measurements (dd/mm/yyyy).
Annual average body mass in grams.
Annual average age of mother in years.
Daily temperature and rainfall data since 1979.
A data frame with 17,532 rows and 3 variables.
Date when climate data was recorded (dd/mm/yyyy).
Daily rainfall data in mm.
Daily temperature data in degrees centigrade.
Output file from slidingwin using temperature and body mass data.
Generated with Mass and MassClimate dataframes.
A data frame with 5,151 rows and 19 variables.
Difference between model AICc of fitted climate window and a null model containing no climate.
The start day of each tested climate window. Furthest from the biological record.
The end day of each tested climate window. Closest to the biological record.
Beta estimate of the relationship between temperature and mass.
Standard error term for linear model betas.
Quadratic beta estimate of the relationship between temperature and mass.
Cubic beta estimate of the relationship between temperature and mass.
Model intercept.
The function used to fit climate (e.g. linear ("lin"), quadratic ("quad"))
Furthest day back considered in slidingwin.
Closest day back considered in slidingwin.
The aggregate statistic used to analyse climate (e.g. mean, max, slope).
Whether "absolute" or "relative" climate windows were tested.
Number of folds used for k-fold cross validation.
Model weight of each fitted climate window.
Sample size (i.e. number of years or sites) used for climate window analysis.
If type is "absolute", the date from which the climate window was tested.
Whether the data was generated using slidingwin or randwin.
Output file from function randwin using temperature and mass data.
Generated with Mass and MassClimate dataframes.
A data frame with 5 rows and 21 variables.
Difference between model AICc of fitted climate window and a null model containing no climate.
The start day of each tested climate window. Furthest from the biological record.
The end day of each tested climate window. Closest to the biological record.
Beta estimate of the relationship between temperature and mass.
Standard error term for linear model betas.
Quadratic beta estimate of the relationship between temperature and mass.
Cubic beta estimate of the relationship between temperature and mass.
Model intercept.
The function used to fit climate (e.g. linear ("lin"), quadratic ("quad"))
Furthest day back considered in slidingwin.
Closest day back considered in slidingwin.
The aggregate statistic used to analyse climate (e.g. mean, max, slope).
Whether "fixed" or "variable" climate windows were tested.
Number of folds used for k-fold cross validation.
Model weight of each fitted climate window.
Sample size (i.e. number of years or sites) used for climate window analysis.
If type is "absolute", the date from which the climate window was tested.
Whether the data was generated using slidingwin or randwin.
The number of randomisations carried out.
Model spread of 95 percent confidence set of models.
Determine the median start and end time for climate windows within a chosen confidence set.
medwin(dataset, cw = 0.95)medwin(dataset, cw = 0.95)
dataset |
Output dataframe of function |
cw |
Cut-off for confidence set (0.95 by default) |
Returns two values representing the median start and end time of climate windows within the confidence set.
Liam D. Bailey and Martijn van de Pol
# Determine median start and end time of MassOutput from the 95% confidence set medwin(MassOutput, cw = 0.95)# Determine median start and end time of MassOutput from the 95% confidence set medwin(MassOutput, cw = 0.95)
Merges outputs of two separate slidingwin analyses.
merge_results(dataset1, dataset2)merge_results(dataset1, dataset2)
dataset1, dataset2
|
The slidingwin outputs to be merged. Note that all elements (i.e. Dataset, BestModel, BestModelData) will be merged and do not need to be specified. |
A list object, identical to that produced by slidingwin,
containing all records from both outputs.
Liam D. Bailey and Martijn van de Pol
#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] output <- slidingwin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") #Merge MassOutput merge_results(output, output) ## Not run: data(Offspring) data(OffspringClimate) # Test a linear functions OffspringWin_lin <- slidingwin(xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, data = Offspring, family = poisson), range = c(150, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") # Test a quadratic functions OffspringWin_quad <- slidingwin(xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, data = Offspring, family = poisson), range = c(150, 0), type = "relative", stat = "mean", func = c("quad"), cmissing = FALSE, cinterval = "day") # Combine these outputs OffspringWin_comb <- merge_results(dataset1 = OffspringWin_lin, dataset2 = OffspringWin_quad) #View analyses contained in the new output OffspringWin_comb$combos #View output from linear analysis head(OffspringWin_comb[[1]]$Dataset) ## End(Not run)#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] output <- slidingwin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") #Merge MassOutput merge_results(output, output) ## Not run: data(Offspring) data(OffspringClimate) # Test a linear functions OffspringWin_lin <- slidingwin(xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, data = Offspring, family = poisson), range = c(150, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") # Test a quadratic functions OffspringWin_quad <- slidingwin(xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, data = Offspring, family = poisson), range = c(150, 0), type = "relative", stat = "mean", func = c("quad"), cmissing = FALSE, cinterval = "day") # Combine these outputs OffspringWin_comb <- merge_results(dataset1 = OffspringWin_lin, dataset2 = OffspringWin_quad) #View analyses contained in the new output OffspringWin_comb$combos #View output from linear analysis head(OffspringWin_comb[[1]]$Dataset) ## End(Not run)
Artificially generated temperature data at a monthly scale. Used for code testing.
A data frame with 576 rows and 2 variables
Date of temperature measurements (dd/mm/yyyy).
Mean monthly temperature
Artificially generated data representing reproductive success of birds since 2009.
A data frame with 1,619 rows and 5 variables.
Total number of offspring produced.
Date of hatching (dd/mm/yyyy).
Order of nest within each season.
Individual ID of female.
Grouping factor designating the breeding season of each record.
Daily temperature and rainfall data since 2009.
Coincides with biological data from Offspring.
A data frame with 2,588 rows and 3 variables.
Date when climate was recorded (dd/mm/yyyy).
Daily rainfall data in mm.
Daily temperature data in degrees centigrade.
Creates a panel of plots to help visualise climate window data.
plotall( dataset, datasetrand = NULL, bestmodel = NULL, bestmodeldata = NULL, cw1 = 0.95, cw2 = 0.5, cw3 = 0.25, title = NULL, arrow = FALSE )plotall( dataset, datasetrand = NULL, bestmodel = NULL, bestmodeldata = NULL, cw1 = 0.95, cw2 = 0.5, cw3 = 0.25, title = NULL, arrow = FALSE )
dataset |
A dataframe containing information on all fitted climate
windows. Output from |
datasetrand |
A dataframe containing information on all fitted climate
windows using randomised data. Output from |
bestmodel |
A model object. The strongest climate window model. Returned
from |
bestmodeldata |
A dataframe containing the biological and climate data
used to fit the strongest climate window model. Output from
|
cw1, cw2, cw3
|
Cumulative weight levels used to visualise model weight
distribution. See |
title |
Title of the plot panel. |
arrow |
TRUE or FALSE. Add arrows to plots to pinpoint best window. |
Will return a panel of 6-8 plots:
DeltaAICc: A colour plot of model deltaAICc values (larger negative values indicate stronger models). DeltaAICc is the difference between AICc of each climate window model and the baseline model containing no climate data.
Model weight: A plot showing the distribution of cumulative model weights. Gradient levels determined by parameters cw1, cw2 and cw3. Darker areas have a higher chance of containing the best climate window. Also returns the percentage of models within the 95
Model betas: A colour plot of model beta estimates. Where applicable, 2nd order coefficients (quadratic) and 3rd order coefficients (cubic) will be plotted separately.
Histogram(s): If datasetrand is provided, plotall will return a
histogram showing the deltaAICc of randomised data.
This can help determine the likelihood of obtaining a deltaAICc value
for a fitted climate window model at random. plotall will also use
pvalue to return values of Pc and PdeltaAICc.
Boxplots: Two boxplots showing the start and end time for a subset of best climate windows. Best climate windows make up the cumulative model weight equivalent to the largest value of cw1, cw2 and cw3. Values above boxplots represent the median values.
Best Model: If bestmodel and bestmodeldata are provided, plotall will create a scatterplot to show the fit of the best model through the data.
Liam D. Bailey and Martijn van de Pol
# Visualise a fixed climate window generated for dataframes Mass and MassClimate data(MassOutput) data(Mass) data(MassClimate) single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") plotall(dataset = MassOutput, bestmodel = single$BestModel, bestmodeldata = single$BestModelData, cw1 = 0.95, cw2 = 0.5, cw3 = 0.25, title = "Mass")# Visualise a fixed climate window generated for dataframes Mass and MassClimate data(MassOutput) data(Mass) data(MassClimate) single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") plotall(dataset = MassOutput, bestmodel = single$BestModel, bestmodeldata = single$BestModelData, cw1 = 0.95, cw2 = 0.5, cw3 = 0.25, title = "Mass")
Create a scatterplot showing the fit of the best climate window model through the biological data.
plotbest(dataset, bestmodel, bestmodeldata)plotbest(dataset, bestmodel, bestmodeldata)
dataset |
A dataframe containing information on all fitted climate
windows. Output from |
bestmodel |
A model object. The strongest climate window model. Output
from |
bestmodeldata |
A dataframe with the data used to
fit the strongest climate window model. Output from |
Returns a scatterplot with a fitted line to show the fit of the best model through the data.
Liam D. Bailey and Martijn van de Pol
# Visualise the best climate window from the datasets Mass and MassClimate data(MassOutput) data(Mass) data(MassClimate) single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") plotbest(dataset = MassOutput, bestmodel = single$BestModel, bestmodeldata = single$BestModelData)# Visualise the best climate window from the datasets Mass and MassClimate data(MassOutput) data(Mass) data(MassClimate) single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") plotbest(dataset = MassOutput, bestmodel = single$BestModel, bestmodeldata = single$BestModelData)
Create colour plots of model beta estimates. Will include quadratic and cubic beta estimates where appropriate.
plotbetas(dataset, arrow = FALSE, plotallenv, plotall = FALSE)plotbetas(dataset, arrow = FALSE, plotallenv, plotall = FALSE)
dataset |
A dataframe containing information on all fitted climate
windows. Output from |
arrow |
TRUE or FALSE. Add arrows to plots to pinpoint best window. |
plotallenv |
Used in conjunction with function |
plotall |
Used in conjunction with function |
Returns colour plots of model beta estimates. Where applicable, 2nd order coefficients (quadratic) and 3rd order coefficients (cubic) will be plotted separately.
Liam D. Bailey and Martijn van de Pol
# Plot model beta estimates for linear models in the Mass dataset data(MassOutput) plotbetas(dataset = MassOutput)# Plot model beta estimates for linear models in the Mass dataset data(MassOutput) plotbetas(dataset = MassOutput)
Create a colour plot to visualise the results of autowin or
crosswin. Displays correlation across all desired climate
windows.
plotcor(cor.output, type, arrow = FALSE)plotcor(cor.output, type, arrow = FALSE)
cor.output |
|
type |
Should be either "A" for data generated by |
arrow |
TRUE or FALSE. Add arrows to plots to pinpoint best window. |
Will generate a colour plot to visualise the correlation data.
Liam D. Bailey and Martijn van de Pol
#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] single <- singlewin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") auto <- autowin(reference = single, xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), stat = "mean", func = "lin", type = "relative", cmissing = FALSE, cinterval = "day") plotcor(auto, type = "A") ## Not run: # Full working example # Visualise climate autocorrelation data(Mass) data(MassClimate) # Fit a single climate window using the datasets Mass and MassClimate. single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # Test the autocorrelation between the climate in this single window and other climate windows. auto <- autowin(reference = single, xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(365, 0), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # Plot the auto-correlation data plotcor(auto, type = "A") ## End(Not run)#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] single <- singlewin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") auto <- autowin(reference = single, xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), stat = "mean", func = "lin", type = "relative", cmissing = FALSE, cinterval = "day") plotcor(auto, type = "A") ## Not run: # Full working example # Visualise climate autocorrelation data(Mass) data(MassClimate) # Fit a single climate window using the datasets Mass and MassClimate. single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # Test the autocorrelation between the climate in this single window and other climate windows. auto <- autowin(reference = single, xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(365, 0), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # Plot the auto-correlation data plotcor(auto, type = "A") ## End(Not run)
Create a colour plot of model deltaAICc values.
plotdelta(dataset, arrow = FALSE, plotall = FALSE, plotallenv, ThreeD = FALSE)plotdelta(dataset, arrow = FALSE, plotall = FALSE, plotallenv, ThreeD = FALSE)
dataset |
A dataframe containing information on all fitted climate
windows. Output from |
arrow |
TRUE or FALSE. Add arrows to plots to pinpoint best window. |
plotall |
Used in conjunction with function |
plotallenv |
Used in conjunction with function |
ThreeD |
TRUE or FALSE. Generate a 3-dimensional plot of the deltaAICc landscape. |
Returns a colour plot of model deltaAICc values (larger negative values indicate stronger models). DeltaAICc is the difference between AICc of each climate window and a null model.
Liam D. Bailey and Martijn van de Pol
# Plot deltaAICc estimates for climate windows in the Mass dataset data(MassOutput) plotdelta(dataset = MassOutput)# Plot deltaAICc estimates for climate windows in the Mass dataset data(MassOutput) plotdelta(dataset = MassOutput)
Create a histogram of deltaAICc values from randomised data.
plothist(dataset, datasetrand)plothist(dataset, datasetrand)
dataset |
A dataframe containing information on all fitted climate
windows from observed data. Output from |
datasetrand |
A dataframe containing information on all fitted climate
windows using randomised data. Output from |
plothist will return a histograms of deltaAICc values from randomised data. Values of PdeltaAICc and Pc will be provided to help determine the likelihood that an observed deltaAICc value would occur by chance.
Liam D. Bailey and Martijn van de Pol
# Plot randomised data for the Mass dataset data(MassOutput) data(MassRand) plothist(datasetrand = MassRand, dataset = MassOutput)# Plot randomised data for the Mass dataset data(MassOutput) data(MassRand) plothist(datasetrand = MassRand, dataset = MassOutput)
Create a plot showing the distribution of cumulative model weights for all fitted climate windows.
plotweights( dataset, cw1 = 0.95, cw2 = 0.5, cw3 = 0.25, arrow = FALSE, plotall = FALSE, plotallenv, ThreeD = FALSE )plotweights( dataset, cw1 = 0.95, cw2 = 0.5, cw3 = 0.25, arrow = FALSE, plotall = FALSE, plotallenv, ThreeD = FALSE )
dataset |
A dataframe containing information on all fitted climate
windows. Output from |
cw1, cw2, cw3
|
Cumulative weight levels used to visualise model weight distribution. Cumulative weights represent the chance that the best model is contained within a set. For example, there is a 95 percent chance that the best climate window model is contained within the cumulative weight level of 0.95. Parameter values must <= 1. |
arrow |
TRUE or FALSE. Add arrows to plots to pinpoint best window. |
plotall |
Used in conjunction with function |
plotallenv |
Used in conjunction with function |
ThreeD |
TRUE or FALSE. Generate a 3-dimensional plot of the model weight landscape. |
Returns a plot showing the distribution of cumulative model weights. Levels determined by parameters cw1,cw2 and cw3.
Liam D. Bailey and Martijn van de Pol
# Plot distribution of model weights for Mass dataset data(MassOutput) plotweights(dataset = MassOutput, cw1 = 0.95, cw2 = 0.75, cw3 = 0.25)# Plot distribution of model weights for Mass dataset data(MassOutput) plotweights(dataset = MassOutput, cw1 = 0.95, cw2 = 0.75, cw3 = 0.25)
Visualise the start and end time for a subset of best climate windows.
plotwin(dataset, cw = 0.95)plotwin(dataset, cw = 0.95)
dataset |
A dataframe containing information on all fitted climate
windows. Output from |
cw |
Cumulative model weight used to subset the group of best models. |
Creates two boxplots showing the start and end time for a subset of best climate windows. Best climate windows make up the cumulative model weight equivalent to the value of cw.
Liam D. Bailey and Martijn van de Pol
# View window limits for climate windows in the top 95% of model weights. data(MassOutput) plotwin(dataset = MassOutput, cw = 0.95)# View window limits for climate windows in the top 95% of model weights. data(MassOutput) plotwin(dataset = MassOutput, cw = 0.95)
Calculate probability that a given climate signal is 'true' using either PDAICc or Pc.
pvalue(dataset, datasetrand, metric, sample.size)pvalue(dataset, datasetrand, metric, sample.size)
dataset |
A dataframe containing information on all fitted climate
windows. Output from |
datasetrand |
A dataframe containing information on all fitted climate
windows using randomised data. Output from |
metric |
"AIC" or "C". Determine whether a value of PDAICc or Pc will be returned. |
sample.size |
Sample size of analysis. |
Returns a value representing the probability that a given climate window result is a false positive.
Liam D. Bailey and Martijn van de Pol
# Calculate PDAICc for the Mass dataset pvalue(datasetrand = MassRand, dataset = MassOutput, metric = "AIC", sample.size = 47) # Calculate Pc for the Mass dataset pvalue(datasetrand = MassRand, dataset = MassOutput, metric = "C", sample.size = 47)# Calculate PDAICc for the Mass dataset pvalue(datasetrand = MassRand, dataset = MassOutput, metric = "AIC", sample.size = 47) # Calculate Pc for the Mass dataset pvalue(datasetrand = MassRand, dataset = MassOutput, metric = "C", sample.size = 47)
Randomises biological data and carries out a climate window analysis. Used to help determine the chance of obtaining an observed result at random.
randwin( exclude = NA, repeats = 5, window = "sliding", xvar, cdate, bdate, baseline, stat, range, func, type, refday, cmissing = FALSE, cinterval = "day", spatial = NULL, cohort = NULL, upper = NA, lower = NA, binary = FALSE, centre = list(NULL, "both"), k = 0, weightfunc = "W", par = c(3, 0.2, 0), control = list(ndeps = c(0.01, 0.01, 0.01)), method = "L-BFGS-B", cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL, thresh = NULL, cvk = NULL )randwin( exclude = NA, repeats = 5, window = "sliding", xvar, cdate, bdate, baseline, stat, range, func, type, refday, cmissing = FALSE, cinterval = "day", spatial = NULL, cohort = NULL, upper = NA, lower = NA, binary = FALSE, centre = list(NULL, "both"), k = 0, weightfunc = "W", par = c(3, 0.2, 0), control = list(ndeps = c(0.01, 0.01, 0.01)), method = "L-BFGS-B", cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL, thresh = NULL, cvk = NULL )
exclude |
Two values (distance and duration) which allow users to exclude short-duration long-lag climate windows from analysis (e.g., windows with a duration of 10 days which occur over a month ago). These windows are often considered to be biologically implausible. |
repeats |
The number of times that data will be randomised and analysed for climate windows. |
window |
Whether randomisations are carried out for a sliding window ("sliding") or weighted window ("weighted") approach. |
xvar |
A list object containing all climate variables of interest. Please specify the parent environment and variable name (e.g. Climate$Temp). |
cdate |
The climate date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. Climate$Date). |
bdate |
The biological date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. Biol$Date). |
baseline |
The baseline model structure used for testing correlation. Currently known to support lm, glm, lmer and glmer objects. |
stat |
If window = "sliding"; The aggregate statistic used to analyse the climate data. Can
currently use basic R statistics (e.g. mean, min), as well as slope.
Additional aggregate statistics can be created using the format function(x)
(...). See FUN in |
range |
Two values signifying respectively the furthest and closest number of time intervals (set by cinterval) back from the cutoff date or biological record to include in the climate window search. |
func |
The functions used to fit the climate variable. Can be linear ("lin"), quadratic ("quad"), cubic ("cub"), inverse ("inv") or log ("log"). |
type |
"absolute" or "relative", whether you wish the climate window to be relative (e.g. the number of days before each biological record is measured) or absolute (e.g. number of days before a set point in time). |
refday |
If type is absolute, the day and month respectively of the year from which the absolute window analysis will start. |
cmissing |
cmissing Determines what should be done if there are missing climate data. Three approaches are possible: - FALSE; the function will not run if missing climate data is encountered. An object 'missing' will be returned containing the dates of missing climate. - "method1"; missing climate data will be replaced with the mean climate of the preceding and following 2 days. - "method2"; missing climate data will be replaced with the mean climate of all records on the same date. |
cinterval |
The resolution at which climate window analysis will be conducted. May be days ("day"), weeks ("week"), or months ("month"). Note the units of parameter 'range' will differ depending on the choice of cinterval. |
spatial |
A list item containing: 1. A factor that defines which spatial group (i.e. population) each biological record is taken from. The length of this factor should correspond to the length of the biological dataset. 2. A factor that defines which spatial group (i.e. population) climate data corresponds to. This length of this factor should correspond to the length of the climate dataset. |
cohort |
A variable used to group biological records that occur in the same biological season but cover multiple years (e.g. southern hemisphere breeding season). Only required when type is "absolute". The cohort variable should be in the same dataset as the variable bdate. |
upper |
Cut-off values used to determine growing degree days or positive climate thresholds (depending on parameter thresh). Note that when values of lower and upper are both provided, climatewin will instead calculate an optimal climate zone. |
lower |
Cut-off values used to determine chill days or negative climate thresholds (depending on parameter thresh). Note that when values of lower and upper are both provided, climatewin will instead calculate an optimal climate zone. |
binary |
TRUE or FALSE. Determines whether to use values of upper and lower to calculate binary climate data (thresh = TRUE), or to use for growing degree days (thresh = FALSE). |
centre |
A list item containing: 1. The variable used for mean centring (e.g. Year, Site, Individual). Please specify the parent environment and variable name (e.g. Biol$Year). 2. Whether the model should include both within-group means and variance ("both"), only within-group means ("mean"), or only within-group variance ("dev"). |
k |
If window = "sliding"; the number of folds used for k-fold cross validation. By default this value is set to 0, so no cross validation occurs. Value should be a minimum of 2 for cross validation to occur. |
weightfunc |
If window = "weighted"; the distribution to be used for optimisation. Can be either a Weibull ("W") or Generalised Extreme Value distribution ("G"). |
par |
If window = "weighted"; the shape, scale and location parameters of the Weibull or GEV weight function used as start weight function. For Weibull : Shape and scale parameters must be greater than 0, while location parameter must be less than or equal to 0. For GEV : Scale parameter must be greater than 0. |
control |
If window = "weighted";
parameters used to determine step size for the optimisation
function. Please see |
method |
If window = "weighted";
the method used for the optimisation function. Please see
|
cutoff.day, cutoff.month
|
Redundant parameters. Now replaced by refday. |
furthest, closest
|
Redundant parameters. Now replaced by range. |
thresh |
Redundant parameter. Now replaced by binary. |
cvk |
Redundant parameter. Now replaced by k. |
Returns a dataframe containing information on the best climate
window from each randomisation. See MassRand as an example.
Liam D. Bailey and Martijn van de Pol
#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] rand <- randwin(repeats = 1, xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") ## Not run: # Full working examples ## EXAMPLE 1 ## # Test climate windows in randomised data using a sliding window approach. data(Mass) data(MassClimate) # Randomise data twice # Note all other parameters are fitted in the same way as the climatewin function. rand <- randwin(repeats = 2, window = "sliding", xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(100, 0), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # View output # head(rand) ## EXAMPLE 2 ## # Test climate windows in randomised data using a weighted window approach. data(Offspring) data(OffspringClimate) # Randomise data twice # Note all other parameters are fitted in the same way as the weightwin function. weightrand <- randwin(repeats = 2, window = "weighted", xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, family = poisson, data = Offspring), range = c(365, 0), func = "quad", type = "relative", weightfunc = "W", cinterval = "day", par = c(3, 0.2, 0), control = list(ndeps = c(0.01, 0.01, 0.01)), method = "L-BFGS-B") # View output head(weightrand) ## End(Not run)#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] rand <- randwin(repeats = 1, xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") ## Not run: # Full working examples ## EXAMPLE 1 ## # Test climate windows in randomised data using a sliding window approach. data(Mass) data(MassClimate) # Randomise data twice # Note all other parameters are fitted in the same way as the climatewin function. rand <- randwin(repeats = 2, window = "sliding", xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(100, 0), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # View output # head(rand) ## EXAMPLE 2 ## # Test climate windows in randomised data using a weighted window approach. data(Offspring) data(OffspringClimate) # Randomise data twice # Note all other parameters are fitted in the same way as the weightwin function. weightrand <- randwin(repeats = 2, window = "weighted", xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, family = poisson, data = Offspring), range = c(365, 0), func = "quad", type = "relative", weightfunc = "W", cinterval = "day", par = c(3, 0.2, 0), control = list(ndeps = c(0.01, 0.01, 0.01)), method = "L-BFGS-B") # View output head(weightrand) ## End(Not run)
Fit a single climate window with a known start and end time.
singlewin( xvar, cdate, bdate, baseline, range, stat, func, type, refday, cmissing = FALSE, cinterval = "day", cohort = NULL, spatial = NULL, upper = NA, lower = NA, binary = FALSE, centre = list(NULL, "both"), cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL, thresh = NULL )singlewin( xvar, cdate, bdate, baseline, range, stat, func, type, refday, cmissing = FALSE, cinterval = "day", cohort = NULL, spatial = NULL, upper = NA, lower = NA, binary = FALSE, centre = list(NULL, "both"), cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL, thresh = NULL )
xvar |
A list object containing all climate variables of interest. Please specify the parent environment and variable name (e.g. Climate$Temp). |
cdate |
The climate date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. Climate$Date). |
bdate |
The biological date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. Biol$Date). |
baseline |
The baseline model structure used for testing correlation. Currently known to support lm, glm, lmer and glmer objects. |
range |
Two values signifying respectively the furthest and closest number of time intervals (set by cinterval) back from the cutoff date or biological record to include in the climate window search. |
stat |
The aggregate statistic used to analyse the climate data. Can
currently use basic R statistics (e.g. mean, min), as well as slope.
Additional aggregate statistics can be created using the format function(x)
(...). See FUN in |
func |
The functions used to fit the climate variable. Can be linear ("lin"), quadratic ("quad"), cubic ("cub"), inverse ("inv") or log ("log"). |
type |
"absolute" or "relative", whether you wish the climate window to be relative (e.g. the number of days before each biological record is measured) or absolute (e.g. number of days before a set point in time). |
refday |
If type is absolute, the day and month respectively of the year from which the absolute window analysis will start. |
cmissing |
cmissing Determines what should be done if there are missing climate data. Three approaches are possible: - FALSE; the function will not run if missing climate data is encountered. An object 'missing' will be returned containing the dates of missing climate. - "method1"; missing climate data will be replaced with the mean climate of the preceding and following 2 days. - "method2"; missing climate data will be replaced with the mean climate of all records on the same date. |
cinterval |
The resolution at which climate window analysis will be conducted. May be days ("day"), weeks ("week"), or months ("month"). Note the units of parameter 'range' will differ depending on the choice of cinterval. |
cohort |
A variable used to group biological records that occur in the same biological season but cover multiple years (e.g. southern hemisphere breeding season). Only required when type is "absolute". The cohort variable should be in the same dataset as the variable bdate. |
spatial |
A list item containing: 1. A factor that defines which spatial group (i.e. population) each biological record is taken from. The length of this factor should correspond to the length of the biological dataset. 2. A factor that defines which spatial group (i.e. population) climate data corresponds to. This length of this factor should correspond to the length of the climate dataset. |
upper |
Cut-off values used to determine growing degree days or positive climate thresholds (depending on parameter thresh). Note that when values of lower and upper are both provided, climatewin will instead calculate an optimal climate zone. |
lower |
Cut-off values used to determine chill days or negative climate thresholds (depending on parameter thresh). Note that when values of lower and upper are both provided, climatewin will instead calculate an optimal climate zone. |
binary |
TRUE or FALSE. Determines whether to use values of upper and lower to calculate binary climate data (thresh = TRUE), or to use for growing degree days (thresh = FALSE). |
centre |
A list item containing: 1. The variable used for mean centring (e.g. Year, Site, Individual). Please specify the parent environment and variable name (e.g. Biol$Year). 2. Whether the model should include both within-group means and variance ("both"), only within-group means ("mean"), or only within-group variance ("dev"). |
cutoff.day, cutoff.month
|
Redundant parameters. Now replaced by refday. |
furthest, closest
|
Redundant parameters. Now replaced by range. |
thresh |
Redundant parameter. Now replaced by binary. |
Will return a list containing two objects:
BestModel, a model object of the fitted climate window model.
BestModelData, a dataframe with the biological and climate data used to fit the climate window model.
Liam D. Bailey and Martijn van de Pol
#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] single <- singlewin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") ## Not run: # Full working example # Fit a known climate window to the datasets Mass and MassClimate data(Mass) data(MassClimate) # Test for a fixed climate window, starting from 20th May # Fit a climate window starting 72 days ago and ending 15 days ago # Fit a linear term for the mean climate # Fit climate windows at the resolution of days single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") ##View data## single$BestModel head(single$BestModelData) ## End(Not run)#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] single <- singlewin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") ## Not run: # Full working example # Fit a known climate window to the datasets Mass and MassClimate data(Mass) data(MassClimate) # Test for a fixed climate window, starting from 20th May # Fit a climate window starting 72 days ago and ending 15 days ago # Fit a linear term for the mean climate # Fit climate windows at the resolution of days single <- singlewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(72, 15), stat = "mean", func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") ##View data## single$BestModel head(single$BestModelData) ## End(Not run)
Average size (using standardised measures of tarsus length, head-bill length and wing length) in red winged fairy wren (Malurus elegans) chicks. Measured over 7 years.
A data frame with 1,003 rows and 5 variables.
Unique nest identifier.
Year of breeding season.
Total number of non-breeding helpers at the nest.
Average offspring size.
Date when offspring size was recorded (dd/mm/yyyy).
Average, maximum and minimum daily temperature data, average rainfall data
from 2006 to 2015.
Coincides with biological data from Size.
A data frame with 3,411 rows and 5 variables.
Date when climate was recorded (dd/mm/yyyy).
Average daily rainfall data in mm.
Average daily temperature data in degrees centigrade.
Maximum daily temperature in degrees centigrade.
Minimum daily temperature in degrees centigrade.
Finds the time period when a biological variable is most strongly affected by climate. Note that climate data and biological data should be loaded as two separate objects. Both objects should contain a date column to designate when the data were recorded (dd/mm/yyyy).
slidingwin( exclude = NA, xvar, cdate, bdate, baseline, type, refday, stat = "mean", func = "lin", range, cmissing = FALSE, cinterval = "day", k = 0, upper = NA, lower = NA, binary = FALSE, centre = list(NULL, "both"), spatial = NULL, cohort = NULL )slidingwin( exclude = NA, xvar, cdate, bdate, baseline, type, refday, stat = "mean", func = "lin", range, cmissing = FALSE, cinterval = "day", k = 0, upper = NA, lower = NA, binary = FALSE, centre = list(NULL, "both"), spatial = NULL, cohort = NULL )
exclude |
Two values (duration and distance) which allow users to exclude short-duration long-lag climate windows from analysis (e.g., windows with a duration of 10 days which occur over a month ago). These windows are often considered to be biologically implausible. |
xvar |
A list object containing all climate variables of interest. Please specify the parent environment and variable name (e.g. climate$Temp). |
cdate |
The climate date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. climate$Date). |
bdate |
The biological date variable (dd/mm/yyyy). Please specify the parent environment and variable name (e.g. Biol$Date). |
baseline |
The baseline model structure used for model testing. Currently known to support lm, glm, lmer, glmer and coxph objects. |
type |
"absolute" or "relative", whether you wish the climate window to be relative (e.g. the number of days before each biological record is measured) or absolute (e.g. number of days before a set point in time). |
refday |
If type is "absolute", the day and month respectively of the year from which the absolute window analysis will start. |
stat |
The aggregate statistics used to analyse the climate data. Can
currently use basic R statistics (e.g. mean, min), as well as slope.
Additional aggregate statistics can be created using the format
function(x) (...). See FUN in |
func |
The functions used to fit the climate variable. Can be linear ("lin"), quadratic ("quad"), cubic ("cub"), inverse ("inv") or log ("log"). |
range |
Two values signifying respectively the furthest and closest number of time intervals (set by cinterval) back from the refday or biological record to include in the climate window search. |
cmissing |
Determines what should be done if there are missing climate data. Three approaches are possible: - FALSE; the function will not run if missing climate data is encountered. An object 'missing' will be returned containing the dates of missing climate. - "method1"; missing climate data will be replaced with the mean climate of the preceding and following 2 records. - "method2"; missing climate data will be replaced with the mean climate of all records on the same date. Note: Other methods are possible. Users should consider those methods most appropriate for their data and apply them manually before using climwin if required. |
cinterval |
The resolution at which climate window analysis will be conducted. May be days ("day"), weeks ("week"), or months ("month"). Note the units of parameter 'range' will differ depending on the choice of cinterval. |
k |
The number of folds used for k-fold cross validation. By default this value is set to 0, so no cross validation occurs. Value should be a minimum of 2 for cross validation to occur. |
upper |
Cut-off values used to determine growing degree days or positive climate thresholds (depending on parameter thresh). Note that when values of lower and upper are both provided, slidingwin will instead calculate an optimal climate zone. |
lower |
Cut-off values used to determine chill days or negative climate thresholds (depending on parameter thresh). Note that when values of lower and upper are both provided, slidingwin will instead calculate an optimal climate zone. |
binary |
TRUE or FALSE. Determines whether to use values of upper and lower to calculate binary climate data (binary = TRUE), or to use for growing degree days (binary = FALSE). |
centre |
A list item containing: 1. The variable used for mean centring (e.g. Year, Site, Individual). Please specify the parent environment and variable name (e.g. Biol$Year). 2. Whether the model should include both within-group means and variance ("both"), only within-group means ("mean"), or only within-group variance ("var"). |
spatial |
A list item containing: 1. A factor that defines which spatial group (i.e. population) each biological record is taken from. The length of this factor should correspond to the length of the biological dataset. 2. A factor that defines which spatial group (i.e. population) climate data corresponds to. This length of this factor should correspond to the length of the climate dataset. |
cohort |
A variable used to group biological records that occur in the same biological season but cover multiple years (e.g. southern hemisphere breeding season). Only required when type is "absolute". The cohort variable should be in the same dataset as the variable bdate. |
Note that slidingwin allows you to test multiple possible parameters with the same code (e.g. func, stat, xvar). See examples for more detail.
Will return a list with an output for each tested set of climate window parameters. Each list item contains three objects:
BestModel, a model object. The strongest climate window model based on AICc.
BestModelData, a dataframe used to fit the strongest climate window model.
Dataset, a dataframe with information on all fitted climate windows.
Ordered using deltaAICc, with most negative deltaAICc values first.
See MassOutput as an example.
In addition, the returned list includes an object 'combos', a summary of all tested sets of climate window parameters.
Liam D. Bailey and Martijn van de Pol
#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] output <- slidingwin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") ## Not run: # Full working examples ##EXAMPLE 1## # Test both a linear and quadratic variable climate window using datasets "Offspring" # and "OffspringClimate". # Load data. data(Offspring) data(OffspringClimate) # Test both linear and quadratic functions with climate variable temperature OffspringWin <- slidingwin(xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, data = Offspring, family = poisson), range = c(150, 0), type = "relative", stat = "mean", func = c("lin", "quad"), cmissing = FALSE, cinterval = "day") # Examine tested combinations OffspringWin$combos # View output for func = "lin" head(OffspringWin[[1]]$Dataset) summary(OffspringWin[[1]]$BestModel) # View output for func = "quad" head(OffspringWin[[2]]$Dataset) summary(OffspringWin[[2]]$BestModel) ##EXAMPLE 2## # Test for an absolute climate window with both 'mean' and 'max' aggregate statistics # using datasets 'Mass' and 'MassClimate'. # Load data. data(Mass) data(MassClimate) # Test an absolute window, starting 20 May (refday = c(20, 5)) # Test for climate windows between 100 and 0 days ago (range = c(100, 0)) # Test both mean and max aggregate statistics (stat = c("mean", "max")) # Fit a linear term (func = "lin") # Test at the resolution of days (cinterval = "day") MassWin <- slidingwin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(100, 0), stat = c("mean", "max"), func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # Examine tested combinations MassWin$combos # View output for mean temperature head(MassWin[[1]]$Dataset) summary(MassWin[[1]]$BestModel) # View output for max temperature head(MassWin[[2]]$Dataset) summary(MassWin[[2]]$BestModel) ## End(Not run)#Simple test example #Create data from a subset of our test dataset #Just use two years biol_data <- Mass[1:2, ] clim_data <- MassClimate[grep(pattern = "1979|1986", x = MassClimate$Date), ] output <- slidingwin(xvar = list(Temp = clim_data$Temp), cdate = clim_data$Date, bdate = biol_data$Date, baseline = lm(Mass ~ 1, data = biol_data), range = c(1, 0), type = "relative", stat = "mean", func = c("lin"), cmissing = FALSE, cinterval = "day") ## Not run: # Full working examples ##EXAMPLE 1## # Test both a linear and quadratic variable climate window using datasets "Offspring" # and "OffspringClimate". # Load data. data(Offspring) data(OffspringClimate) # Test both linear and quadratic functions with climate variable temperature OffspringWin <- slidingwin(xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, data = Offspring, family = poisson), range = c(150, 0), type = "relative", stat = "mean", func = c("lin", "quad"), cmissing = FALSE, cinterval = "day") # Examine tested combinations OffspringWin$combos # View output for func = "lin" head(OffspringWin[[1]]$Dataset) summary(OffspringWin[[1]]$BestModel) # View output for func = "quad" head(OffspringWin[[2]]$Dataset) summary(OffspringWin[[2]]$BestModel) ##EXAMPLE 2## # Test for an absolute climate window with both 'mean' and 'max' aggregate statistics # using datasets 'Mass' and 'MassClimate'. # Load data. data(Mass) data(MassClimate) # Test an absolute window, starting 20 May (refday = c(20, 5)) # Test for climate windows between 100 and 0 days ago (range = c(100, 0)) # Test both mean and max aggregate statistics (stat = c("mean", "max")) # Fit a linear term (func = "lin") # Test at the resolution of days (cinterval = "day") MassWin <- slidingwin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass), range = c(100, 0), stat = c("mean", "max"), func = "lin", type = "absolute", refday = c(20, 5), cmissing = FALSE, cinterval = "day") # Examine tested combinations MassWin$combos # View output for mean temperature head(MassWin[[1]]$Dataset) summary(MassWin[[1]]$BestModel) # View output for max temperature head(MassWin[[2]]$Dataset) summary(MassWin[[2]]$BestModel) ## End(Not run)
Finds the best weighted average of a weather variable over a period that correlates most strongly with a biological variable. Uses weibull or Generalised Extreme Value (GEV) distribution. See references for a full description.
weightwin( n = 1, xvar, cdate, bdate, baseline, range, k = 0, func = "lin", type, refday, nrandom = 0, centre = NULL, weightfunc = "W", cinterval = "day", cmissing = FALSE, cohort = NULL, spatial = NULL, par = c(3, 0.2, 0), control = list(ndeps = c(0.001, 0.001, 0.001)), method = "L-BFGS-B", cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL, grad = FALSE )weightwin( n = 1, xvar, cdate, bdate, baseline, range, k = 0, func = "lin", type, refday, nrandom = 0, centre = NULL, weightfunc = "W", cinterval = "day", cmissing = FALSE, cohort = NULL, spatial = NULL, par = c(3, 0.2, 0), control = list(ndeps = c(0.001, 0.001, 0.001)), method = "L-BFGS-B", cutoff.day = NULL, cutoff.month = NULL, furthest = NULL, closest = NULL, grad = FALSE )
n |
The number of iterations used to run weightwin. If n > 1, iterations will use randomly generated starting parameters. These are stored in the output data frame 'iterations'. |
xvar |
A list object containing all climate variables of interest. Please specify the parent environment and variable name (e.g. Climate$Temp). |
cdate |
The climate date variable. Please specify the parent environment and variable name (e.g. Climate$Date). |
bdate |
The biological date variable. Please specify the parent environment and variable name (e.g. Biol$Date). |
baseline |
The baseline model structure used for testing correlation. Currently known to support lm, lme, glm and glmer objects. |
range |
Two values signifying respectively the furthest and closest number of time intervals (set by cinterval) back from the cutoff date or biological record to include in the climate window search. |
k |
The number of folds used for k-fold cross validation. By default this value is set to 0, so no cross validation occurs. Value should be a minimum of 2 for cross validation to occur. |
func |
The function used to fit the climate variable in the model. Can be linear ("lin"), quadratic ("quad"), cubic ("cub"), inverse ("inv") or log ("log"). |
type |
"absolute" or "relative", whether you wish the climate window to be relative (e.g. the number of days before each biological record is measured) or absolute (e.g. number of days before a set point in time). |
refday |
If type is absolute, the day and month respectively of the year from which the absolute window analysis will start. |
nrandom |
Used when conducting data randomisation, should not be changed manually. |
centre |
A list item containing: 1. The variable used for mean centring (e.g. Year, Site, Individual). Please specify the parent environment and variable name (e.g. Biol$Year). 2. Whether the model should include both within-group means and variance ("both"), only within-group means ("mean"), or only within-group variance ("var"). |
weightfunc |
The distribution to be used for optimisation. Can be either a Weibull ("W") or Generalised Extreme Value distribution ("G"). |
cinterval |
The resolution at which the climate window analysis will be conducted. May be days ("day"), weeks ("week"), or months ("month"). Note the units of parameter 'range' will differ depending on the choice of cinterval. |
cmissing |
Determines what should be done if there are missing climate data. Three approaches are possible: - FALSE; the function will not run if missing climate data is encountered. An object 'missing' will be returned containing the dates of missing climate. - "method1"; missing climate data will be replaced with the mean climate of the preceding and following 2 records. - "method2"; missing climate data will be replaced with the mean climate of all records on the same date. Note: Other methods are possible. Users should consider those methods most appropriate for their data and apply them manually before using climwin if required. |
cohort |
A variable used to group biological records that occur in the same biological season but cover multiple years (e.g. southern hemisphere breeding season). Only required when type is "absolute". The cohort variable should be in the same dataset as the variable bdate. |
spatial |
A list item containing: 1. A factor that defines which spatial group (i.e. population) each biological record is taken from. The length of this factor should correspond to the length of the biological dataset. 2. A factor that defines which spatial group (i.e. population) climate data corresponds to. This length of this factor should correspond to the length of the climate dataset. |
par |
Shape, scale and location parameters of the Weibull or GEV weight function used as start weight function. For Weibull : Shape and scale parameters must be greater than 0, while location parameter must be less than or equal to 0. For GEV : Scale parameter must be greater than 0. |
control |
Parameters used to determine step size for the optimisation
function. Please see |
method |
The method used for the optimisation function. Please see
|
cutoff.day, cutoff.month
|
Redundant parameters. Now replaced by refday. |
furthest, closest
|
Redundant parameters. Now replaced by range. |
grad |
Run the optimisation procedure with a numerically derived gradient function. This can improve model convergence but will increase computational time. |
Produces a constantly updating grid of plots as the optimisation function is running.
Right panel from top to bottom: The three parameters (shape, scale and location) determining the weight function.
Left top panel: The resulting weight function.
Left middle panel: The delta AICc compared to the baseline model.
Left bottom panel: Plotted relationship between the weighted mean of climate and the biological response variable.
Also returns a list containing three objects:
BestModel, a model object. The best weighted window model determined by deltaAICc.
BestModelData, a dataframe. Biological and climate data used to fit the best weighted window model.
WeightedOutput. Parameter values for the best weighted window.
iterations. If n > 1, the starting parameters and deltaAICc values from each iteration of weightwin.
Martijn van de Pol and Liam D. Bailey
van de Pol & Cockburn 2011 Am Nat 177(5):698-707 (doi: 10.1086/659101) "Identifying the critical climatic time window that affects trait expression"
#Simple test example #Create data from a subset of our test dataset biol_data <- Mass[1:5, ] data(MassClimate) weight <- weightwin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = biol_data$Date, baseline = glm(Mass ~ 1, data = biol_data), range = c(100, 0), func = "lin", type = "relative", weightfunc = "W", cinterval = "day", par = c(2.26, 8.45, 0), control = list(ndeps = c(0.01, 0.01, 0.01)), method = "L-BFGS-B") ## Not run: # Full working example # Test for a weighted average over a fixed climate window # using datasets 'Offspring' and 'OffspringClimate' # N.B. THIS EXAMPLE MAY TAKE A MOMENT TO CONVERGE ON THE BEST MODEL. # Load data data(Offspring) data(OffspringClimate) # Test for climate windows between 365 and 0 days ago (range = c(365, 0)) # Fit a quadratic term for the mean weighted climate (func="quad") # in a Poisson regression (offspring number ranges 0-3) # Test a variable window (type = "absolute") # Test at the resolution of days (cinterval="day") # Uses a Weibull weight function (weightfunc="week") weight <- weightwin(xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, family = poisson, data = Offspring), range = c(365, 0), func = "quad", type = "relative", weightfunc = "W", cinterval = "day", par = c(3, 0.2, 0), control = list(ndeps = c(0.01, 0.01, 0.01)), method = "L-BFGS-B") # View output head(weight[[3]]) summary(weight[[1]]) head(weight[[2]]) ## End(Not run)#Simple test example #Create data from a subset of our test dataset biol_data <- Mass[1:5, ] data(MassClimate) weight <- weightwin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date, bdate = biol_data$Date, baseline = glm(Mass ~ 1, data = biol_data), range = c(100, 0), func = "lin", type = "relative", weightfunc = "W", cinterval = "day", par = c(2.26, 8.45, 0), control = list(ndeps = c(0.01, 0.01, 0.01)), method = "L-BFGS-B") ## Not run: # Full working example # Test for a weighted average over a fixed climate window # using datasets 'Offspring' and 'OffspringClimate' # N.B. THIS EXAMPLE MAY TAKE A MOMENT TO CONVERGE ON THE BEST MODEL. # Load data data(Offspring) data(OffspringClimate) # Test for climate windows between 365 and 0 days ago (range = c(365, 0)) # Fit a quadratic term for the mean weighted climate (func="quad") # in a Poisson regression (offspring number ranges 0-3) # Test a variable window (type = "absolute") # Test at the resolution of days (cinterval="day") # Uses a Weibull weight function (weightfunc="week") weight <- weightwin(xvar = list(Temp = OffspringClimate$Temperature), cdate = OffspringClimate$Date, bdate = Offspring$Date, baseline = glm(Offspring ~ 1, family = poisson, data = Offspring), range = c(365, 0), func = "quad", type = "relative", weightfunc = "W", cinterval = "day", par = c(3, 0.2, 0), control = list(ndeps = c(0.01, 0.01, 0.01)), method = "L-BFGS-B") # View output head(weight[[3]]) summary(weight[[1]]) head(weight[[2]]) ## End(Not run)
Calculate within group deviance of a variable. Used to test for 'within individual effects'. See methods outlined in van de Pol and Wright 2009 for more detail.
wgdev(covar, groupvar)wgdev(covar, groupvar)
covar |
Continuous variable for which within group deviance will be calculated. Please specify the dataset in which the variable is found (i.e. data$var). |
groupvar |
Grouping variable within which deviance will be calculated. For example, individual ID or site/plot ID. Please specify the dataset in which the variable is found (i.e. data$var). |
Returns a vector containing numeric values. This can be used to
differentiate within and between group effects in a model.
See function wgmean to calculate within group means.
See van de Pol and Wright 2009 for more detail.
Martijn van de Pol and Jonathan Wright
# Calculate within year deviance in temperature from the MassClimate dataset. data(MassClimate) #Calculate year column library(lubridate) MassClimate$Year <- year(as.Date(MassClimate$Date, format = "%d/%m/%Y")) #Calculate within year deviance of temperature within_yr_dev <- wgdev(MassClimate$Temp, MassClimate$Year) #Add this variable to the original dataset MassClimate$Within_yr_dev <- within_yr_dev# Calculate within year deviance in temperature from the MassClimate dataset. data(MassClimate) #Calculate year column library(lubridate) MassClimate$Year <- year(as.Date(MassClimate$Date, format = "%d/%m/%Y")) #Calculate within year deviance of temperature within_yr_dev <- wgdev(MassClimate$Temp, MassClimate$Year) #Add this variable to the original dataset MassClimate$Within_yr_dev <- within_yr_dev
Calculate group means of a variable. Used to test for 'between individual effects'. See methods outlined in van de Pol and Wright 2009 for more detail.
wgmean(covar, groupvar)wgmean(covar, groupvar)
covar |
Continuous variable for which group means will be calculated. Please specify the dataset in which the variable is found (i.e. data$var). |
groupvar |
Grouping variable within which means will be calculated. For example, individual ID or site/plot ID. Please specify the dataset in which the variable is found (i.e. data$var). |
Returns a vector containing numeric values. This can be used to
differentiate within and between group effects in a model.
See function wgdev to calculate within group deviance.
See van de Pol and Wright 2009 for more details on the method.
Martijn van de Pol and Jonathan Wright
# Calculate mean temperature within years from the MassClimate dataset. data(MassClimate) #Calculate year column library(lubridate) MassClimate$Year <- year(as.Date(MassClimate$Date, format = "%d/%m/%Y")) #Calculate mean temperature within each year within_yr_mean <- wgmean(MassClimate$Temp, MassClimate$Year) #Add this variable to the original dataset MassClimate$Within_yr_mean <- within_yr_mean# Calculate mean temperature within years from the MassClimate dataset. data(MassClimate) #Calculate year column library(lubridate) MassClimate$Year <- year(as.Date(MassClimate$Date, format = "%d/%m/%Y")) #Calculate mean temperature within each year within_yr_mean <- wgmean(MassClimate$Temp, MassClimate$Year) #Add this variable to the original dataset MassClimate$Within_yr_mean <- within_yr_mean