Fuzzy Similarity in Species Distributions
Append data
Compute a bias layer
Biotic threat of a stronger over a weaker species based on their favou...
Clean coordinates
Select among correlated variables based on a given criterion
Distance matrix for spatial coordinates
(Inverse) distance to the nearest presence
Degree-minute-second to decimal degree coordinates
(Fuzzy) entropy
Favourability (probability without the effect of sample prevalence)
Classify favourability into 3 categories (low, intermediate, high)
False Discovery Rate
Fuzzy similarity
Fuzzy consensus among model predictions
Overlay operations based on fuzzy logic
Range change based on continuous (fuzzy) values
Fuzzy Similarity in Species Distributions
Get model predictions
Get region
Grid (or thin) point occurrence records to the resolution of a raster ...
Classify integer columns
Trim off non-significant variables from a model
Overall overlap between model predictions
Multiple conversion
GLMs with variable selection for multiple species
Analyse multicollinearity in a dataset, including VIF
Trend Surface Analysis for multiple species
Pairwise intersection (and union) of range maps
Partial response plot(s) for probability or favourability
Percent test data
Prevalence
Pairwise similarity between rangemaps
(Fuzzy) rarity
Select (spatially biased) absence rows.
Shared favourability for two competing species
Calculate similarity from set operations
Pair-wise (fuzzy) similarity matrix
Obtain unique abbreviations of species names
Convert a species list to a presence-absence table
Compare model predictions along a stepwise variable selection process
Stepwise regression
Model summary with Wald (instead of z) test statistics
Timer
Transpose (part of) a matrix or dataframe
Triangular matrix indices
(Fuzzy) vulnerability
Functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence-independent favourability obtained from probability of presence), as well as pair-wise fuzzy similarity (based on fuzzy logic versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model consensus and comparison (overlap and fuzzy similarity, fuzzy loss, fuzzy gain), and for data preparation, such as obtaining unique abbreviations of species names, defining the background region, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting among (pseudo)absences to address survey bias, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity. Initially described in Barbosa (2015) <doi:10.1111/2041-210X.12372>.