Calculate percentage change between two years using Bayesian output

Using the data returned from occDetModel this function models a linear trend between two years for each iteration of the models. The predicted values for the two years are then used to calculates a percentage change. The results is a percentage change estimate for each of the interations of the model. This distribution of the results is used to calculate the mean estimate and the 95

occurrenceChange(firstYear, lastYear, bayesOut, change = "growthrate",
  region = NULL)

Arguments

firstYear	numeric, the first year over which the change is to be estimated
lastYear	numeric, the last year over which the change is to be estimated
bayesOut	occDet object as returned from occDetModel
change	A character string that specifies the type of change to be calculated, the default is annual growth rate. See details for options.
region	A character string specifying the region name if change is to be determined regional estimates of occupancy. Region names must match those in the model output.

Value

A list giving the mean, median, credible intervals and raw data from the estimations.

Details

change is used to specify which change measure to be calculated. There are four options to choose from: difference, percentdif, growthrate and lineargrowth.

difference calculates the simple difference between the first and last year.

percentdif calculates the percentage difference between the first and last year.

growthrate calculates the annual growth rate across years.

lineargrowth calculates the linear growth rate from a linear model.

Examples

# NOT RUN {
#' # Create data
n <- 15000 #size of dataset
nyr <- 20 # number of years in data
nSamples <- 100 # set number of dates
nSites <- 50 # set number of sites

# Create somes dates
first <- as.Date(strptime("1980/01/01", "%Y/%m/%d"))
last <- as.Date(strptime(paste(1980+(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(paste('A', 1:nSites, sep=''), size = n, TRUE)

# the date of visit is selected at random from those created earlier
survey <- sample(rDates, size = n, TRUE)

# run the model with these data for one species
results <- occDetModel(taxa = taxa,
                       site = site,
                       survey = survey,
                       species_list = c('a','m','g'),
                       write_results = FALSE,
                       n_iterations = 1000,
                       burnin = 10,
                       thinning = 2)

 # estimate the change for one species      
 change <- occurrenceChange(firstYear = 1990,
                            lastYear = 1999,
                            bayesOut = results$a)
# }