This function provides visualisations of how the number of records in the dataset changes over time and how the number of species recorded on a visit changes over time. For each of these an linear model is run to test if there is a significant trend.
Arguments
- taxa
A character vector of taxon names, as long as the number of observations.
- site
A character vector of site names, as long as the number of observations.
- time_period
A numeric vector of user defined time periods, or a date vector, as long as the number of observations.
- plot
Logical, if
TRUEplots and model results will be printed to the console- progress_bar
If
TRUEa progress bar is printed to console
Value
A list of filepaths, one for each species run, giving the location of the output saved as a .rdata file, containing an object called 'out'
Examples
if (FALSE) {
### Diagnostics functions ###
# Create data
n <- 2000 # size of dataset
nyr <- 20 # number of years in data
nSamples <- 200 # set number of dates
useDates <- TRUE
# Create somes dates
first <- as.POSIXct(strptime("2003/01/01", "%Y/%m/%d"))
last <- as.POSIXct(strptime(paste(2003+(nyr-1),"/12/31", sep=''), "%Y/%m/%d"))
dt <- last-first
rDates <- first + (runif(nSamples)*dt)
# taxa are set as random letters
taxa <- sample(letters, size = n, TRUE)
# three sites are visited randomly
site <- sample(c('one', 'two', 'three'), size = n, TRUE)
# the date of visit is selected at random from those created earlier
if(useDates){
time_period <- sample(rDates, size = n, TRUE)
} else {
time_period <- sample(1:nSamples, size = n, TRUE)
}
# Using a date
dataDiagnostics(taxa, site, time_period)
# Using a numeric
dataDiagnostics(taxa, site, as.numeric(format(time_period, '%Y')))
}