climwin (version 0.1.2)

climatewin: Test for a climate windows in data.

Description

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 seperate objects. Both objects should contain a date column to designate when the data were recorded (dd/mm/yyyy) and a response variable column.

Usage

climatewin(xvar, cdate, bdate, baseline, furthest, closest, type, cutoff.day,
  cutoff.month, stat = "mean", func = "lin", cmissing = FALSE,
  cinterval = "day", cvk = 0, upper = NA, lower = NA, thresh = FALSE,
  centre = NULL)

Arguments

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 and glmer objects.

furthest

The furthest number of time intervals (set by cinterval) back from the cutoff date or biological record that will be included in the climate window search.

closest

The closest number of time intervals (set by cinterval) back from the cutoff date or biological record that will be included in the climate window search.

type

fixed or variable, whether you wish the climate window to be variable (i.e. the number of days before each biological record is measured) or fixed (i.e. number of days before a set point in time).

cutoff.day,cutoff.month

If type is "fixed", the day and month of the year from which the fixed 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").

cmissing

TRUE or FALSE, determines what should be done if there are missing climate data. If FALSE, the function will not run if missing climate data is encountered. If TRUE, any records affected by missing climate data will be removed from climate window analy

cinterval

The resolution at which climate window analysis will be conducted. May be days ("day"), weeks ("week"), or months ("month"). Note the units of parameters 'furthest' and 'closest' will differ depending on the choice of cinterval.

cvk

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, 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.

thresh

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

Variable used for mean centring (e.g. Year, Site, Individual). Please specify the parent environment and variable name (e.g. Biol$Year).

Value

• 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 with the data 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. SeeMassOutputas an example.
  In addition, the returned list includes an object 'combos', a summary of all tested sets of climate window parameters.

Details

Note that climatewin allows you to test multiple possible parameters with the same code (e.g. func, stat, xvar). See examples for more detail.

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 <- climatewin(xvar = list(Temp = OffspringClimate$Temperature),
                          cdate = OffspringClimate$Date,
                          bdate = Offspring$Date,
                          baseline = glm(Offspring ~ 1, data = Offspring, family = poisson),
                          furthest = 150, closest = 0,
                          type = "variables", 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 a fixed climate window with both 'mean' and 'max' aggregate statistics
# using datasets 'Mass' and 'MassClimate'.

# Load data.

data(Mass)
data(MassClimate)

# Test a fixed window, starting 20 May (cutoff.month = 5, cutoff.day = 20)
# Test for climate windows between 100 and 0 days ago (furthest = 100, closest = 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 <- climatewin(xvar = list(Temp = MassClimate$Temp), cdate = MassClimate$Date,
                     bdate = Mass$Date, baseline = lm(Mass ~ 1, data = Mass),
                     furthest = 100, closest = 0,
                     stat = c("mean", "max"), func = "lin",
                     type = "fixed", cutoff.day = 20, cutoff.month = 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)