climwin (version 1.2.3)

weightwin: Find a weighted climate window

Description

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.

Usage

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
)

Arguments

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 optim for more detail.

method

The method used for the optimisation function. Please see optim for more detail.

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.

Value

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.

References

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"

Examples

# 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]])
 
# }
# NOT RUN {
# }