climwin (version 0.1.2)

crosswin: Test the correlation between two climate variables

Description

Test the correlation between two climate variables across all considered climate windows.

Usage

crosswin(xvar, xvar2, cdate, bdate, furthest, closest, stat, stat2, type,
  cutoff.day, cutoff.month, cinterval = "day", cmissing = FALSE)

Arguments

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).

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.

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

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.

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

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

Value

• Will return a dataframe containing the correlation between the two climate variables.

Examples

# 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,
                 furthest = 365, closest = 0,
                 stat = "mean", stat2 = "mean", type = "variable",
                 cmissing = FALSE, cinterval = "day")

# View the output
head(cross)

# Plot the output
plotcor(cross, type = "C")