Skip to contents

Simulation of Network evolution

Source: R/f_sims.R
ts_sims.Rd

ts_sims simulates the network evolution nsims times given the existing network net1, the defined evaluation function ts_eval(), the included network statistics (taken from ans, myeff, or statistics) and the corresponding parameter estimates (or starting values) taken from ans, myeff, or statistics.

Usage

ts_sims(
  ans = NULL,
  mydata = NULL,
  myeff = NULL,
  startvalues = NULL,
  net1 = NULL,
  ccovar = NULL,
  statistics = NULL,
  nsims = 1000,
  p2step = c(1, 0, 0),
  dist1 = NULL,
  dist2 = NULL,
  modet1 = "degree",
  modet2 = "degree",
  chain = FALSE,
  verbose = TRUE,
  parallel = FALSE
)

ts_sim(
  ans = NULL,
  mydata = NULL,
  myeff = NULL,
  startvalues = NULL,
  net1 = NULL,
  ccovar = NULL,
  statistics = NULL,
  p2step = c(1, 0, 0),
  dist1 = NULL,
  dist2 = NULL,
  modet1 = "degree",
  modet2 = "degree",
  chain = FALSE,
  verbose = TRUE
)

Arguments

ans

Results of class sienaFit, produced by a call to RSiena::siena07()

mydata

Siena data object created by a call to RSiena::sienaDataCreate()

myeff

Siena effects object created by a call to RSiena::getEffects()

startvalues

if not provided manually, taken from results of ans$theta, or if ans is not provided from summary(myeff)$initialValue.

net1

adjacency matrix, the adjacency matrix representing the relations between actors at Time=1. Valid values are 0 and 1. If not provided manually, retrieved from ans or mydata.

ccovar

data frame with named time-constant covariates. If not provided manually, retrieved from ans or mydata.

statistics

list of statistics of RsienaTwoStep, see: ts_degree() and DETAILS. If not provided manually, retrieved from ans or myeff.

nsims

numeric, number of simulations.

p2step

numeric vector of length 3, setting the ratio of ministep, twostep and twoministeps. This parameter is passed to ts_sims().

dist1

numeric, minimal path length between ego1 and ego2 at time1 in order to be allowed to start a cooperation. If NULL all dyads are allowed to start a cooperation. This parameter is passed to ts_sims().

dist2

numeric, minimal path length between ego1 and ego2 at time2 in order for twostep to be counted as cooperation. This parameter is passed to ts_sims().

modet1

string, indicating the type of ties being evaluated at time1. "degree" considers all ties as undirected. "outdegree" only allows directed paths starting from ego1 and ending at ego2. "indegree" only allows directed paths starting from ego2 and ending at ego2. This parameter is passed to ts_sims().

modet2

string, indicating the type of ties being evaluated at time2. "degree" considers all ties as undirected. "outdegree" only allows directed paths starting from ego1 and ending at ego2. "indegree" only allows directed paths starting from ego2 and ending at ego2. This parameter is passed to ts_sims().

chain

TRUE/FALSE, set to TRUE if you want to save all the subsequent networks (after the ministep or twostep) during the simulation. If FALSE only the end network is saved.

verbose

TRUE/FALSE. If set to true it shows the iteration steps and some results.

parallel

Boolean. TRUE/FALSE.

Value

If chain=FALSE a list (of length nsims) of adjacency matrices representing the final network after the simulated evolution. If chain=TRUE a list of lists of adjacency matrices. Each inner list represents the complete network evolution of one simulation. The outer list refers to the simulation run (with length nsims).

Details

For examples on how to use ts_sims see: vignette("1.Introduction_RsienaTwoStep", package="RsienaTwoStep") or the package website. Before you set parallel to TRUE make sure to set-up a cluster with the package doParallel (see Examples).

  • p2step==c(1,0,0): ministep

  • p2step==c(0,1,0) & dist1==NULL & dist2==NULL: twostep-simultaneity

  • p2step==c(0,1,0) & dist1!=NULL & dist2==NULL: twostep-strict coordination

  • p2step==c(0,1,0) & dist1!=NULL & dist2!=NULL: twostep-weak coordination

  • p2step==c(0,0,1): two-ministeps

See also

ts_estim(), ts_alternatives_ministep(), ts_alternatives_twostep(), ts_alternatives_simstep(), ts_eval()

Examples

ts_sims(
  net = ts_net2,
  nsims = 2,
  parallel = FALSE,
  statistics = list(ts_degree, ts_recip),
  startvalues = c(3, -2, 1),
  p2step = c(0, 1, 0)
)
#> [1] "nsim: 1"
#> [1] "nsim: 2"
#> [[1]]
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    0    0    0
#> [2,]    0    0    0    0    0
#> [3,]    0    0    0    0    0
#> [4,]    1    0    0    0    0
#> [5,]    0    0    0    0    0
#> 
#> [[2]]
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    1    0    0
#> [2,]    1    0    0    0    0
#> [3,]    0    0    0    0    0
#> [4,]    0    0    0    0    0
#> [5,]    0    0    0    0    0
#>