View source: R/manage_landscape_sim.R
manage_landscape_sim | R Documentation |
Runs a series of simulations, using iterate.graph
, allows changing the simulations parameters in several sequential simulations.
manage_landscape_sim(par_df, parameters_spom, full.output)
par_df |
Arguments data frame to be used by iterate.graph (each row of this data frame is a set of Arguments). The data frame has to have the following columns in this order (the name of the column is not relevant):
|
parameters_spom |
Parameters data frame, as given by |
full.output |
Creates a folder named 'output' to which it saves the full results of the simulations made with the parameters in each row of 'par_df'. It will generate as many objects as the number of rows in this data frame. |
For details regarding the arguments see the respective functions.
Returns a data frame with the parameters used for the simulations and the results (mean occupation, mean number of patches, mean turnover, mean distance and mean area).
Depending on computing capacity, this function can take from several hours to several days to run.
Frederico Mestre and Fernando Canovas
rland.graph
, span.graph
, species.graph
, spom
#Setup the parameters for each simulation: PAR1_SPAN2 <- rep("ncsd",820)#parameter 1 for the span function PAR2_SPAN2 <- rep(seq(from=0,to=80,by=2), each=20)#parameter 2 for the span function PAR3_SPAN2 <- rep(seq(from=0,to=80,by=2),20)#parameter 3 for the span function PAR4_SPAN2 <- rep(2,820)#parameter 4 for the span function PAR5_SPAN2 <- rep(2,820)#parameter 5 for the span function NSEW_SPECIES2 <- rep("none",820)#where to start populating the landscape PARM_SPECIES2 <- rep(5,820)#parameter for the species function METHOD_SPECIES2 <- rep("percentage",820)#method for populating the landscape MAPSIZE2 <- rep(10000,820)#dimension of the landscape SPAN2 <- rep(100,820)#number of time steps of each simulation ITER2 <- rep(5,820)#number of iterations of each simulation NPATCH2 <- rep(800,820)#number of patches AREA_M2 <- rep(0.45,820)#mean area AREA_SD2 <- rep(0.2,820)#area sd MDST2 <- rep(0,820)#minimum distance between KERN <- rep("op1",820)#kernel CONN <- rep("op1",820)#connectivity function COLNZ <- rep("op1",820)#colonization function EXT <- rep("op1",820)#extinction function BETA1 <- rep("NULL",820) B <- rep(1,820) C1 <- rep("NULL",820) C2 <- rep("NULL",820) Z <- rep("NULL",820) R2 <- rep("NULL",820) DISPERSAL2 <- rep(800,820)#mean dispersal ability of the species SUCC <- rep("early",820) #Build parameter data frame (keep the order of the parameters): simulation <- data.frame(MDST2,NPATCH2,AREA_M2,AREA_SD2, MAPSIZE2,SPAN2,ITER2,PAR1_SPAN2,PAR2_SPAN2,PAR3_SPAN2,PAR4_SPAN2,PAR5_SPAN2, NSEW_SPECIES2,PARM_SPECIES2,METHOD_SPECIES2,KERN,CONN,COLNZ,EXT,BETA1,B,C1,C2,Z,R2,DISPERSAL2,SUCC) #Delete vectors used for data frame creation: rm('PAR1_SPAN2','PAR2_SPAN2','PAR3_SPAN2','PAR4_SPAN2','PAR5_SPAN2', 'NSEW_SPECIES2','PARM_SPECIES2','METHOD_SPECIES2','MAPSIZE2','SPAN2','ITER2', 'NPATCH2','AREA_M2','AREA_SD2','MDST2','KERN','CONN','COLNZ','EXT', 'BETA1','B','C1','C2','Z','R2','DISPERSAL2','SUCC') ## Not run: data(param1) ms2 <- manage_landscape_sim(par_df=simulation,parameters_spom=param1) ## End(Not run)
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