Package 'ladybird'

Fit a base model to a species

Description

Fit a base model to a species

Usage

base_model(
  species = "Harm_axyr",
  min_occurrences = 1000,
  min_species = 3,
  first_order = TRUE,
  center_year = 2001
)

Arguments

species	Name of the species.
min_occurrences	The minimum number of occurrences per species.
min_species	The minimum number of species recorded at the combination of location and year.
first_order	Use first (TRUE) or second (FALSE) order random walk for the year component. Defaults to TRUE.
center_year	The year to center to. Defaults to 2001.

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Fit a model to a species using the cumulative predictions for a secundary species

Description

Fit a model to a species using the cumulative predictions for a secundary species

Usage

cumulative_model(
  species = "Adal_bipu",
  min_occurrences = 1000,
  min_species = 3,
  secondary,
  first_order = TRUE,
  center_year = 2001
)

Arguments

species	Name of the species.
min_occurrences	The minimum number of occurrences per species.
min_species	The minimum number of species recorded at the combination of location and year.
secondary	The output of base_model() for a different species.
first_order	Use first (TRUE) or second (FALSE) order random walk for the year component. Defaults to TRUE.
center_year	The year to center to. Defaults to 2001.

---

Fit a model to a species using the predictions for a secondary species

Description

Fit a model to a species using the predictions for a secondary species

Usage

fit_model(first_order = TRUE, base_data, trend_prediction, base_prediction)

Arguments

first_order	Use first (TRUE) or second (FALSE) order random walk for the year component. Defaults to TRUE.
base_data	A dataframe with the base data.
trend_prediction	A dataframe with the timestamps to predict the trend.
base_prediction	A dataframe with the locations and timestamps to predict.

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Import and standardise the raw data

Description

Import and standardise the raw data

Usage

import_data(belgium, output, strict = TRUE)

Arguments

belgium	path to the CSV file with the Belgian data.
output	path to the root of the data package
strict	What to do when the metadata changes. strict = FALSE overwrites the data and the metadata with a warning listing the changes, strict = TRUE returns an error and leaves the data and metadata as is. Defaults to TRUE.

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Load the relevant occurrence data

Description

Load the relevant occurrence data

Usage

load_relevant(min_occurrences = 1000, min_species = 3)

Arguments

min_occurrences	The minimum number of occurrences per species.
min_species	The minimum number of species recorded at the combination of location and year.

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Display a leaflet map with the occurrences for a given species

Description

Display a leaflet map with the occurrences for a given species

Usage

occurrence_map()

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Fit a model to a species using the predictions for a secondary species

Description

Fit a model to a species using the predictions for a secondary species

Usage

probability_model(
  species = "Adal_dece",
  min_occurrences = 1000,
  min_species = 3,
  secondary,
  first_order = TRUE,
  center_year = 2001
)

Arguments

species	Name of the species.
min_occurrences	The minimum number of occurrences per species.
min_species	The minimum number of species recorded at the combination of location and year.
secondary	The output of base_model() for a different species.
first_order	Use first (TRUE) or second (FALSE) order random walk for the year component. Defaults to TRUE.
center_year	The year to center to. Defaults to 2001.

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