socialInductionWeighted: Social induction model with weights

Description Usage Arguments Examples

View source: R/SocialInductionModels.R

Description

This function runs the social induction model of network formation (see Ilany et al. 2016), with the addition of weighted edges.

Usage

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socialInductionWeighted(obsNet = startingNet, Pn, Pr, Pb, En1, En2, Er1, Er2,
  maxE, iter)

Arguments

obsNet

Starting network. This must be a weighted and non-directed igraph network.

Pn

Probability of forming ties with a mother's partner.

Pr

Probability of forming ties with a non mother's partner.

Pb

Probability of forming ties with mother.

En1

Alpha parameter for effort into a mother's partner. Where effort is defined by a beta distribution (0,1) using alpah and beta.

En2

Beta parameter for effort into a mother's partner. Where effort is defined by a beta distribution (0,1) using alpah and beta.

Er1

Alpha parameter for effort into a non mother's partner. Where effort is defined by a beta distribution (0,1) using alpah and beta.

Er2

Beta parameter for effort into a non mother's partner. Where effort is defined by a beta distribution (0,1) using alpah and beta.

maxE

Maximum grooming effort used to scale the beta distribution. This choice should represent an upper limit of effort.

iter

Number of iteration, where each iteration removes one node and introduces a new one.

Examples

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startingNet = erdos.renyi.game(n=10, type = c("gnm"), p.or.m = 15)
E(startingNet)$weight<-c(2,25,1,1,1,1,4,5,6,9,2,1,2,1,2)
predWeightedNet<-socialInductionWeighted(startingNet, Pn=0.4,Pr=0.1,Pb=1,En1=0.1,En2=4,Er1=0.1,Er2=4,maxE=100,iter=100)
plot(startingNet, edge.width=E(startingNet)$weight)
plot(predWeightedNet, edge.width=E(predWeightedNet)$weight)

tbonne/SocialIntegrationNet documentation built on July 31, 2018, 4:11 a.m.