# Wraps the biodecrypt.cross analysis to compare the performance of biodecrypt among different parameters. The function biodecrypt.wrap wraps the biodecrypt.cross analysis by using all possible combinations of a series of distance ratio, alpha and buffer values to compare their resulting MIR, NIR and NUR. ```r biodecrypt.wrap(mat,id,alpha=c(1,5,10,15),alphamat=NULL,ratio=c(2,3,4,5), buffer=c(0,40,80,120,160), checkdist=T, polygon=NULL, minimum=7, map=NULL,xlim=NULL,ylim=NULL,main=NULL,save=T,name="res_cross.txt",runs=10) ``` ## Arguments - `mat`: A matrix for longitude and latitude (in decimal degrees) for all records. - `id`: A vector indicating species membership of each record (in the same order of mat). Identified records are indicated with 1,2..n, unidentified records with 0. - `alpha`: A vector indicating the initial alpha values. It will be the same for all species - `alphamat`: A matrix indicating different alpha values for different species (optional). - `ratio`: The values of ratio. - `buffer`: The values of buffer. - `checkdist`: Logical, if TRUE cases attributed to a given species based on relative distance from hulls but closer to an identified record of another species are not attributed. - `polygon`: A SpatialPolygonsDataFrame with area of interests (ground or sea). Typically obtained from Natural Earth (https://www.naturalearthdata.com/). If NULL no removal is applied - `minimum`: The minimum number of specimens required to build alpha hulls. If the number of identified specimens is lower, convex hulls are calculated to improve procedure stability. - `map`: A map to be plotted during the procedure to show the separation progress. - `xlim`: Longitude boudaries for the map. - `ylim`: latitude boudaries for the map. - `main`: The name to be plotted on the graph - `save`: Logical, if TRUE a result table is saved after each biodecrypt.cross run - `name`: The name of the saved file - `runs`: The number of runs among which the cases are randomly assigned as non-attributed records ## Details The resulting table can be passed to biodecrypt.optimise to compute the best combination of alpha, buffer and ratio. ## Returns - **table**: The result table indicating for each cross validation test the MIR, NIR and NUR values together with the used ratio, buffer and alpha values. ## References Platania L. et al. Assigning occurrence data to cryptic taxa improves climatic niche assessments: biodecrypt, a new tool tested on European butterflies. Glocal Ecology and Biogeography (2020). ## Author(s) Leonardo Dapporto ## Examples ```r # Create an example for a dataset mat<-rbind(cbind(rnorm(n = 20, mean = 1, sd = 4), rnorm(n = 20, mean = 40, sd = 3)), cbind(rnorm(n = 20, mean = 7, sd = 5), rnorm(n = 20, mean = 45, sd = 2))) id<-c(rep(1,20),rep(2,20)) id[sample(c(1:40))[1:10]]<-0 ## Not run: wrap_data_fast<-biodecrypt.wrap(mat,id, alpha=c(1,4), ratio=2, buffer=20, runs=2) ## End(Not run) ## Not run: parameters<-biodecrypt.optimise(wrap_data_fast$table,penalty=10) #Make the example with default 10 runs and more values ## Not run: wrap_data<-biodecrypt.wrap(mat,id, alpha=c(1,4), ratio=c(2,4), buffer=c(20,50)) ## End(Not run) ## Not run: parameters<-biodecrypt.optimise(wrap_data$table) #inspect the optimised parameters ## Not run: parameters #Use different alpha for the two species #alpha for first ## Not run: alpha1<-c(1,3) #alpha for second ## Not run: alpha2<-c(1,5) ## Not run: alphamat<-cbind(alpha1,alpha2) ## Not run: wrap_data<-biodecrypt.wrap(mat,id, alphamat=alphamat, ratio=c(2,4), buffer=c(20,50)) ## End(Not run) ## Not run: parameters<-biodecrypt.optimise(wrap_data$table, penalty=20) #inspect the optimised parameters ## Not run: parameters ```

biodecrypt.wrap function