View source: R/reportingRateModel.r
reportingRateModel | R Documentation |
Run reporting rate models to assess the change in species occurrence over time.
reportingRateModel(
taxa,
site,
time_period,
list_length = FALSE,
site_effect = FALSE,
species_to_include = unique(taxa),
overdispersion = FALSE,
verbose = FALSE,
family = "Binomial",
print_progress = FALSE
)
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. |
list_length |
Logical, if |
site_effect |
Logical, if |
species_to_include |
A character vector giving the name of species to model. By default all species will be modelled |
overdispersion |
This option allows modelling overdispersion ( |
verbose |
This option, if |
family |
The type of model to be use. Can be |
print_progress |
Logical, if |
A dataframe of results are returned to R. Each row gives the results for a
single species, with the species name given in the first column, species_name
.
For each of the following columns the prefix (before ".") gives the covariate and the
sufix (after the ".") gives the parameter of that covariate.
number_observations
gives the number of visits where the species of interest
was observed. If any of the models encountered an error this will be given in the
column error_message
. If model do encounter errors the the values for most
columns will be NA
The data.frame has a number of attributes:
intercept_year
- The year used for the intercept (i.e. the
year whose value is set to 0). Setting the intercept to the median year helps
to increase model stability
min_year
and max_year
- The earliest and latest year
in the dataset (after years have been centered on intercept_year
nVisits
- The total number of visits that were in the dataset
model_formula
- The model used, this will vary depending on the
combination of arguments used
Roy, H.E., Adriaens, T., Isaac, N.J.B. et al. (2012) Invasive alien predator causes rapid declines of native European ladybirds. Diversity & Distributions, 18: 717-725.
Isaac, N.J.B. et al. (2014) Extracting robust trends in species' distributions from unstructured opportunistic data: a comparison of methods. bioRXiv 006999, https://doi.org/10.1101/006999.
## Not run:
# Create data
n <- 3000 #size of dataset
nyr <- 10 # number of years in data
nSamples <- 30 # set number of dates
nSites <- 15 # set number of sites
# Create somes dates
first <- as.POSIXct(strptime("2010/01/01", "%Y/%m/%d"))
last <- as.POSIXct(strptime(paste(2010+(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
time_period <- sample(rDates, size = n, TRUE)
# combine this to a dataframe (adding a final row of 'bad' data)
df <- data.frame(taxa = c(taxa,'bad'),
site = c(site,'A1'),
time_period = c(time_period, as.POSIXct(strptime("1200/01/01", "%Y/%m/%d"))))
# Run the model
RR_out <- reportingRateModel(df$taxa, df$site, df$time_period, print_progress = TRUE)
head(RR_out)
## End(Not run)
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