multsame2: Runs and summarizes multiple INA simulations with the same...
In GarrettLab/INApreliminary: Impact Network Analysis

Description Usage Arguments Details Examples

This function runs and summarizes multiple INA simulations with the same parameter values. (It uses INA functions estinfo, genlocs, initvals, setup2, genmovnet, spreadstep, makedec, estab, and ntsteps2.)

multsame2(
  nreals2,
  usethreshman2,
  ntimesteps2,
  geocoords2,
  readgeocoords2,
  maneffdir2,
  maneffmean2,
  maneffsd2,
  maneffthresh2,
  sampeffort2,
  xrange2,
  yrange2,
  numnodes2,
  randgeo2,
  readinitinfo2,
  initinfo2,
  initinfo.dist2,
  initinfo.n2,
  initinfo.norp2,
  initinfo.p2,
  readinitbio2,
  initbio2,
  initbio.dist2,
  initbio.n2,
  initbio.norp2,
  initbio.p2,
  readseam2,
  seam2,
  seamdist2,
  seampla2,
  seamplb2,
  seamrandp2,
  readbpam2,
  bpam2,
  bpamdist2,
  bpampla2,
  bpamplb2,
  bpamrandp2,
  probadoptmean2,
  probadoptsd2,
  probadoptvec2,
  readprobadoptvec2,
  probestabmean2,
  probestabsd2,
  readprobestabvec2,
  probestabvec2,
  doplot2 = F
)

`nreals2`	number of realizations to be evaluated
`usethreshman2`	if T, use the threshold for the management effect size estimate; thus, information is never present anywhere unless the management effect estimate exceeds the threshold
`ntimesteps2`	number of time steps to be evaluated
`geocoords2`	matrix of x,y coordinates of nodes, read in if readgeocoords2=T
`readgeocoords2`	if T, read in geocoords2 - otherwise, generate it in each realization
`maneffdir2`	if maneffdir2='decrease_estab', the management reduces the probability of establishment; if maneffdir2='increase_estab', the management reduces the probability that establishment does NOT occur
`maneffmean2`	(estinfo, estab) the underlying mean change in establishment probability (as a proportion)
`maneffsd2`	(estab) sd of management effect (same as maneffsd2 to function estinfo)
`maneffthresh2`	(estinfo) the threshold effect size for communicating about management (if maneffthresh2 = 0 there is no threshold so communication can always occur)
`sampeffort2`	(estinfo) sampling effort, where greater samping effort reduces the error in estimating the management effect
`xrange2`	(genlocs) range of x coordinates
`yrange2`	(genlocs) range of y coordinates
`numnodes2`	(genlocs) the number of nodes
`randgeo2`	(genlocs) if TRUE then locations are randomly generated
`readinitinfo2`	info (setup2) if T, the initial values for the vector of starting locations for the presence of information are read in rather than generated
`initinfo.dist2`	info (initvals) the pattern of locations where initial presence occurs: 'random' indicates all equally likely, 'upedge' indicates that nodes closest to the upper edge have presence, 'rightedge' indicates that nodes closest to the right edge have presence
`initinfo.n2`	info (initvals) the number of initial locations for presence
`initinfo.norp2`	info (initvals) 'num' indicates initial number for presence, 'prop' indicates initial proportion
`initinfo.p2`	info (initvals) the proportion of initial locations for presence
`readinitbio2`	bio (setup2) if T, the initial values for the vector of starting locations for the presence of the bioentity are read in rather than generated
`initbio2`	bio (setup2) the vector of initial values read in if readinitbio2 == T, a vector with lenth equal to the number of nodes and entries 1s or 0s with 1s indicating the initial presence of the bioentity
`initbio.dist2`	estab (initvals) the type of locations where initial presence occurs: 'random' indicates all equally likely, 'upedge' indicates that nodes closest to the upper edge have presence, 'rightedge' indicates that nodes closest to the right edge have presence
`initbio.n2`	estab (initvals) the number of initial locations for presence
`initbio.norp2`	estab (initvals) 'num' indicates initial number for presence, 'prop' indicates initial proportion
`initbio.p2`	estab (initvals) the proportion of initial locations for presence
`readseam2`	if T, the communication adjacency matrix is read in rather than being generated from the outset
`seam2`	communication adjacency matrix, read in if readseam2=T
`seamdist2`	(genmovnet) the function of distance used to estimate movement probability - 'random' (not related to distance) or 'powerlaw'
`seampla2`	(genmovnet) power law parameter a in ad^(-b)
`seamplb2`	(genmovnet) power law parameter b in ad^(-b)
`seamrandp2`	(genmovnet) random case, probability of link
`readbpam2`	if T, the dispersal adjacency matrix is read in rather than being generated from the outset
`bpam2`	dispersal adjacency matrix, read in if readbpam2=T
`bpamdist2`	(genmovnet) the function of distance used to estimate movement probability - 'random' (not related to distance) or 'powerlaw'
`bpampla2`	(genmovnet) power law parameter a in ad^(-b)
`bpamplb2`	(genmovnet) power law parameter b in ad^(-b)
`bpamrandp2`	(genmovnet) random case, probably of link
`probadoptmean2`	(makedec) mean probability of adopting management if informed
`probadoptsd2`	(makedec) sd in truncated normal distribution of probability of adoption
`probadoptvec2`	vector of probabilities of adoption if informed for nodes in the network (`readprobadoptvec2` determines whether `probadoptvec2` is read in to `makedec` or generated within `makedec` based on `probadoptmean2` and `probadoptsd2`)
`readprobadoptvec2`	if T, then a VECTOR of probability of adoption values is read in; if F, `probadoptmean2` and `probadoptsd2` are used to generate the vector
`probestabmean2`	(estab) mean probability of establishment (new or CONTINUED) in absence of management
`probestabsd2`	(estab) sd in truncated normal distribution for probability of establishment in absence of management
`readprobestabvec2`	(estab) if T, then a vector `probestabvec2` is read in; otherwise the vector is generated using `probestabmean2` and `probestabsd2`
`probestabvec2`	(estab) vector of probabilities of establishment (read in or generated when the function is run, depending on `readprobestabvec2` equal to T or F)
`doplot2`	if true plots of resulting presence of information and species are generated
`initinfo.s2`	info (setup2) the vector of initial values read in if readinitinfo2 == T, a vector with lenth equal to the number of nodes and entries 1s or 0s with 1s indicating the initial presence of information

Updated 2020-09-05

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x5 <- multsame2(nreals2=10, ntimesteps2=3, usethreshman2=F, readgeocoords2=T, geocoords2=matrix(c(1,1, 1,2, 1,3, 2,1, 2,2, 2,3),byrow=T,ncol=2), maneffdir2='decrease_estab', maneffmean2=0.5, maneffsd2=0.1, maneffthresh2=0.5, sampeffort2=1, xrange2=NA, yrange2=NA, numnodes2=NA, randgeo2=NA, readinitinfo2=F, initinfo.dist2='random', initinfo.n2=5, initinfo.norp2='num', initinfo.p2=NA, readinitbio2=F, initbio.dist2='upedge', initbio.n2=5, initbio.norp2='num', initbio.p2=NA, readseam2=F, seam2=NA, seamdist2='powerlaw', seampla2=1, seamplb2=1, seamrandp2=NA, readbpam2=F, bpam2=NA, bpamdist2='powerlaw', bpampla2=1, bpamplb2=1, bpamrandp2=NA, probadoptmean2=0.5, probadoptsd2=0.1, probadoptvec2=NA, readprobadoptvec2=F, probestabmean2=0.5, probestabsd2=0.1, readprobestabvec2=F, probestabvec2=NA, doplot2=F)
x3 <- multsame2(nreals2=2,ntimesteps=3, usethreshman2=F, readgeocoords2=T, geocoords2=matrix(runif(n=100)*100,byrow=T,ncol=2), maneffdir2='decrease_estab', maneffmean2=0.5, maneffsd2=0.1, maneffthresh2=0.5, sampeffort2=1, xrange2=NA, yrange2=NA, numnodes2=NA, randgeo2=NA, readinitinfo2=F, initinfo.dist2='random', initinfo.n2=5, initinfo.norp2='num', initinfo.p2=0.05, readinitbio2=F, initbio.dist2='upedge', initbio.n2=5, initbio.norp2='num', initbio.p2=0.05, readseam2=F, seam2=NA, seamdist2='powerlaw', seampla2=1, seamplb2=1, seamrandp2=NA, readbpam2=F, bpam2=NA, bpamdist2='powerlaw', bpampla2=1, bpamplb2=1, bpamrandp2=NA, probadoptmean2=0.5, probadoptsd2=c(0,1), probadoptvec2=NA, readprobadoptvec2=F, probestabmean2=0.5, probestabsd2=0.1, readprobestabvec2=F, probestabvec2=NA, doplot2=F)