Description Usage Arguments Value Examples
This function prepares and compiles the necessary components to execute subsequent optimization functions, and must be run first. To run the example data, download the data from GitHub (url)
1 | Optim.prep(Response, n.Pops, ASCII.dir, CS_Point.File, CS.exe, Neighbor.Connect, Constrained.Max, Initial.shape, Bootstrap, boot.iters, Sample_Proportion)
|
Response |
This must be a square distance matrix without row or column names |
n.Pops |
The number of populations in your study |
ASCII.dir |
The directory containing the continuous ASCII (.asc) files to be optimized. It is recommended that these files be stored their own directory. |
CS_Point.File |
The path to the Circuitscape formatted point file. See Circuitscape documentation for help. |
CS.exe |
The path to the CIRCUITSCAPE executable file (cs_run.exe). For example: '"C:/Program Files/Circuitscape/cs_run.exe"' ***NOTE: Double quotation used*** |
Neighbor.Connect |
Select 4 or 8 to designate the connection scheme to use in CIRCUITSCAPE (Default = 8) |
Constrained.Max |
The fixed maximum resistance value to use during the initial constrained grid search (Default = 100) |
Initial.shape |
The initial shape parameters to test in constrained grid search (Default = c(seq(0.2,1,by=0.2),seq(1.25,10.75,by=0.75))) |
Bootstrap |
Logical,if TRUE, specify the number of bootstrap iterations for 'boot.iters' |
boot.iters |
Number of bootstrap iterations (Default = 10,000) |
Sample_Proportion |
Proportion of samples to be included in each bootstrap iteration (Default = 0.75) |
Results in an object with the necessary components to run other the other functions
1 2 3 4 | # Example.dir <- "C:/Example/"
# Optim.input<-Optim.prep(Response=read.csv(paste0(Example.dir,"RESPONSE_mat.csv"),header=F),n.Pops=64,ASCII.dir=Example.dir,CS_Point.File=paste0(Example.dir,"samples64.txt"),CS.exe='"C:/Program Files/Circuitscape/4.0/Circuitscape/cs_run.exe"',Neighbor.Connect=8,Results.dir=paste0(Example.dir,"Results/"),Constrained.Max=100,Initial.shape=c(seq(0.1,1,0.4),seq(1.5,10,1.5)),Bootstrap=TRUE,boot.iters=10000,Sample_Proportion=0.75)
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