R: Test for auto-correlation in climate.

autowin {climwin}

R Documentation

Test for auto-correlation in climate.

Description

Tests the correlation between the climate in a specified climate window and other fitted climate windows.

Usage

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
)

Arguments

`reference`	Reference climate data to be compared. Generated by functions `singlewin` or `slidingwin`.
`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 `apply` for more detail.
`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.

Value

Will return a data frame showing the correlation between the climate in each fitted window and the chosen reference window.

Author(s)

Liam D. Bailey and Martijn van de Pol

Examples


#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)

[Package climwin version 1.2.3 Index]