climwin (version 1.2.3)

singlewin: Fit a single climate window

Description

Fit a single climate window with a known start and end time.

Usage

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
)

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 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 apply for more detail.

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.

Value

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.

Examples

# 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)
# }
# NOT RUN {
# }