crosswin {climwin} R Documentation

## Test the correlation between two climate variables.

### Description

Test the correlation between two climate variables.

### Usage

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

### 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). 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. 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 apply for more detail. 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. 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 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. 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. 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. cutoff.day, cutoff.month Redundant parameters. Now replaced by refday. furthest, closest Redundant parameters. Now replaced by range.

### Value

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

### 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$Date), ] cross <- crosswin(xvar = list(Temp = clim_data$Temp),
xvar2 = list(Rain = clim_data$Rain), cdate = clim_data$Date, bdate = biol_data$Date, range = c(1, 0), stat = "mean", stat2 = "mean", type = "relative", cmissing = FALSE, cinterval = "day") ## Not run: # Full working example # Test correlation between temperature and rainfall in the MassClimate dataset. data(Mass) data(MassClimate) cross <- crosswin(xvar = list(Temp = MassClimate$Temp),
xvar2 = list(Rain = MassClimate$Rain), cdate = MassClimate$Date, bdate = Mass\$Date,
range = c(365, 0),
stat = "mean", stat2 = "mean", type = "relative",
cmissing = FALSE, cinterval = "day")

# View the output