Model fit testing.R
In dth2/EpiModelHIV_SHAMP: Epidemic Modeling of HIV Transmission among MSM, MSMF and Heterosexual Populations
##Running network simulations with EPIModelHIV and ergm.ego.
#Load required packages.
library(EpiModelHIV)
library(EpiModelHPC)
#devtools::install_github("statnet/ergm.ego-private",ref="3.7-compat", auth_token ="")
#devtools::install_github("statnet/tergmLite")
#devtools::install_github("statnet/ergm.ego")
library(ergm.ego)
library(tergmLite)
#Load the ego and alter data frames and look at them
load("~/SHAMP/egonet/data/all.egodata.rda")
str(all.egodata)
data.params<-list ()
all.egodata$egos$role.class<-ifelse(all.egodata$egos$sex=="M","I","R")
new_data<-input_shamp(all.egodata, data.params, immigration=TRUE, msm.msmf=FALSE)
data.params<-as.list(new_data[1])
##Make the three ergm.ego objects.
ego.obj_c<-as.egodata(new_data[[2]]$egos,alters=new_data[[2]]$altersCohab,egoIDcol="ego")
ego.obj_p<-as.egodata(new_data[[2]]$egos,alters=new_data[[2]]$altersPers,egoIDcol="ego")
ego.obj_i<-as.egodata(new_data[[2]]$egos,alters=new_data[[2]]$altersOT,egoIDcol="ego")
###cohab partnership network.
summary(ego.obj_c ~edges +
nodefactor("race.sex.pers",base=9) +
nodefactor("race") +
nodematch("race",diff=TRUE) +
nodefactor("agecat", base=1) +
absdiff("sqrt.age"))
fit.c.3<-ergm.ego(ego.obj_m ~edges +
nodefactor("race.sex.pers",base=9) +
nodefactor("race",base=5) +
nodematch("race",diff=TRUE) +
nodefactor("agecat", base=1) +
absdiff("sqrt.age") +
offset(nodematch("sex", diff=FALSE)),
offset.coef = -Inf,
control=control.ergm.ego(ppopsize=50000, stats.est="asymptotic",
ergm.control = control.ergm(MCMC.interval=7000,
MCMC.samplesize=7000,
MCMC.burnin = 7000,
MPLE.max.dyad.types = 1e5,
init.method = "MPLE",
MCMLE.maxit = 250)))
summary(fit.c.3)
sum.fit.c.3<-summary(fit.c.3)
modelfits.cohab.3 <- list(fit.c.3, sum.fit.c.3)
save(modelfits.cohab.3, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits.cohab.3.rda")
plot(gof(modelsXXX, GOF="model"))
####Casual partnership network.
summary(ego.obj_p ~edges +nodefactor("race.sex.cohab",base=0) +
nodematch("race",diff=TRUE) + nodefactor("agecat", base=1) +
absdiff("sqrt.age"))
fit.p.3<-ergm.ego(ego.obj_p ~edges +
nodefactor("race.sex.cohab",base=9) +
nodefactor("race",base=5) +
nodematch("race",diff=TRUE) +
nodefactor("agecat", base=1) +
absdiff("sqrt.age") +
offset(nodematch("sex", diff=FALSE)),
offset.coef = -Inf,
control=control.ergm.ego(ppopsize=50000, stats.est="asymptotic",
ergm.control = control.ergm(MCMC.interval=5000,
MCMC.samplesize=5000,
MCMC.burnin = 5000,
MPLE.max.dyad.types = 1e6,
init.method = "MPLE",
MCMLE.maxit = 250)))
summary(fit.p.3)
sum.fit.p.3<-summary(fit.p.3)
modelfits.pers.3 <- list(fit.p.3, sum.fit.p.3)
save(modelfits.pers.3, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits.pers.3.rda")
######One time partnerships.
summary(ego.obj_i ~edges +nodefactor("race.sex.cohab",base=0) + nodefactor("race.sex.pers",base=0) +
nodematch("race",diff=TRUE) + nodefactor("agecat", base=1) +
absdiff("sqrt.age"))
fit.i.3<-ergm.ego(ego.obj_i ~edges +
nodefactor("race.sex.cohab",base=9) +
nodefactor("race.sex.pers",base=9) +
nodematch("race",diff=TRUE) +
nodefactor("agecat", base=1) +
absdiff("sqrt.age") +
offset(nodematch("sex", diff=FALSE)),
offset.coef = -Inf,
control=control.ergm.ego(ppopsize=50000, stats.est="asymptotic",
ergm.control = control.ergm(MCMC.interval=5000,
MCMC.samplesize=5000,
MCMC.burnin = 5000,
MPLE.max.dyad.types = 1e6,
init.method = "MPLE",
MCMLE.maxit = 250)))
summary(fit.i.3)
sum.fit.i.3<-summary(fit.i.3)
modelfits.inst.3 <- list(fit.i.3, sum.fit.i.3)
save(modelfits.inst.3, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits.inst.3.rda")
load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits_cohab.rda")
load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits_casual.rda")
load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits_inst.rda")
#Create additional required elements for est
# Time unit for simulation, relative to 1 day
time.unit <- 7
method<-1
#Simulation size.
sim.size<-38145
# Mean durations
diss_c = ~offset(edges)
diss_p = ~offset(edges)
#Mortality
ages <- 18:59
age.unit <- 52
asmr.B.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1 / age.unit),
1)
asmr.BI.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1 / age.unit),
1)
asmr.H.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1/age.unit),
1)
asmr.HI.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1/age.unit),
1)
asmr.W.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1/age.unit),
1)
asmr.B.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1 / age.unit),
1)
asmr.BI.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1 / age.unit),
1)
asmr.H.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1/age.unit),
1)
asmr.HI.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1/age.unit),
1)
asmr.W.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1/age.unit),
1)
exp.mort <- (mean(asmr.B.f[ages]) + mean(asmr.BI.f[ages]) + mean(asmr.H.f[ages])
+ mean(asmr.HI.f[ages]) + mean(asmr.W.f[ages]) + mean(asmr.B.m[ages])
+ mean(asmr.BI.m[ages]) + mean(asmr.H.m[ages])
+ mean(asmr.HI.m[ages]) + mean(asmr.W.m[ages]) ) / 10
coef.diss_c <- dissolution_coefs(dissolution = diss_c,
duration = data.params[[1]]$durs_c / time.unit,
d.rate = exp.mort)
coef.diss_p <- dissolution_coefs(dissolution = diss_p,
duration = data.params[[1]]$durs_p / time.unit,
d.rate = exp.mort)
coef.diss_i <-dissolution_coefs(~offset(edges), 1)
target.stats_c<-as.numeric(fit.c.ego$target.stats[2:length(fit.c.ego$target.stats)])
target.stats_p<-as.numeric(fit.p.ego$target.stats[2:length(fit.p.ego$target.stats)])
target.stats_i<-as.numeric(fit.i.ego$target.stats[2:length(fit.i.ego$target.stats)])
target.stats.names_c<- names(fit.c.ego$target.stats[2:length(fit.c.ego$target.stats)])
target.stats.names_p<- names(fit.p.ego$target.stats[2:length(fit.p.ego$target.stats)])
target.stats.names_i<- names(fit.i.ego$target.stats[2:length(fit.i.ego$target.stats)])
coef.form.crude_c<-fit.c.ego$coef[2:length(fit.c.ego$coef)]
coef.form_c<-coef.form.crude_c
coef.form_c[1]<-coef.form_c[1]+fit.c.ego$coef[1]
coef.form_c[1]<- coef.form_c[1]- coef.diss_c$coef.adj
constraints_c <- ~.
coef.form.crude_p<-fit.p.ego$coef[2:length(fit.p.ego$coef)]
coef.form_p<-coef.form.crude_p
coef.form_p[1]<-coef.form_p[1]+fit.p.ego$coef[1]
coef.form_p[1]<- coef.form_p[1]- coef.diss_p$coef.adj
constraints_p <- ~.
coef.form.crude_i<-fit.i.ego$coef[2:length(fit.i.ego$coef)]
coef.form_i<-coef.form.crude_i
coef.form_i[1]<-coef.form_i[1]+fit.i.ego$coef[1]
coef.form_i[1]<- coef.form_i[1]
constraints_i <- ~.
nw<-network.initialize(sim.size, directed = FALSE, hyper = FALSE, loops = FALSE,
multiple = FALSE, bipartite = FALSE)
fit.c <- list(fit= fit.c.ego, formation=fit.c.ego$formula, target.stats= target.stats_c,
target.stats.names= target.stats.names_c, coef.form = coef.form_c,
coef.form.crude= coef.form.crude_c, coef.diss=coef.diss_c, constraints= constraints_c,
edapprox=TRUE)
fit.p <- list(fit= fit.p.ego, formation=fit.p.ego$formula, target.stats= target.stats_p,
target.stats.names= target.stats.names_p, coef.form = coef.form_p,
coef.form.crude= coef.form.crude_p, coef.diss=coef.diss_p, constraints= constraints_p,
edapprox=TRUE)
fit.i <- list(fit= fit.i.ego, formation=fit.i.ego$formula, target.stats= target.stats_i,
target.stats.names= target.stats.names_i, coef.form = coef.form_i,
coef.form.crude= coef.form.crude_i, coef.diss=coef.diss_i, constraints= constraints_i,
edapprox=TRUE)
param <- param_shamp(data.params)
init <- init_shamp()
control <- control_shamp(nsteps = 5)
est <- list(fit.m, fit.c, fit.i)
#save(est, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/fit.rda")
save(est, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/fitsmall.rda")
save(data.params, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/data.params.rda")
#load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/fit.rda")
load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/fitsmall.rda")
sim<-netsim(est, param, init, control)
save(sim, file = "~/EpiModelHIV_shamp_modeling/scenarios/sim.rda")
demog.table<-as.data.frame(rbind(demog4,demog52,demog104,demog208,demog312,demog416,demog520))
save(demog.table, file = "~/EpiModelHIV_shamp_modeling/scenarios/demogs.rda")
library(xlsx) #load the package
write.xlsx(x = demog.table, file = "~/EpiModelHIV_shamp_modeling/scenarios/demogs.xlsx",
sheetName = "SHAMP Demog", row.names = FALSE)
netsim(est, param, init, control)
dth2/EpiModelHIV_SHAMP documentation built on May 15, 2019, 4:56 p.m.
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##Running network simulations with EPIModelHIV and ergm.ego.
#Load required packages.
library(EpiModelHIV)
library(EpiModelHPC)
#devtools::install_github("statnet/ergm.ego-private",ref="3.7-compat", auth_token ="")
#devtools::install_github("statnet/tergmLite")
#devtools::install_github("statnet/ergm.ego")
library(ergm.ego)
library(tergmLite)
#Load the ego and alter data frames and look at them
load("~/SHAMP/egonet/data/all.egodata.rda")
str(all.egodata)
data.params<-list ()
all.egodata$egos$role.class<-ifelse(all.egodata$egos$sex=="M","I","R")
new_data<-input_shamp(all.egodata, data.params, immigration=TRUE, msm.msmf=FALSE)
data.params<-as.list(new_data[1])
##Make the three ergm.ego objects.
ego.obj_c<-as.egodata(new_data[[2]]$egos,alters=new_data[[2]]$altersCohab,egoIDcol="ego")
ego.obj_p<-as.egodata(new_data[[2]]$egos,alters=new_data[[2]]$altersPers,egoIDcol="ego")
ego.obj_i<-as.egodata(new_data[[2]]$egos,alters=new_data[[2]]$altersOT,egoIDcol="ego")
###cohab partnership network.
summary(ego.obj_c ~edges +
nodefactor("race.sex.pers",base=9) +
nodefactor("race") +
nodematch("race",diff=TRUE) +
nodefactor("agecat", base=1) +
absdiff("sqrt.age"))
fit.c.3<-ergm.ego(ego.obj_m ~edges +
nodefactor("race.sex.pers",base=9) +
nodefactor("race",base=5) +
nodematch("race",diff=TRUE) +
nodefactor("agecat", base=1) +
absdiff("sqrt.age") +
offset(nodematch("sex", diff=FALSE)),
offset.coef = -Inf,
control=control.ergm.ego(ppopsize=50000, stats.est="asymptotic",
ergm.control = control.ergm(MCMC.interval=7000,
MCMC.samplesize=7000,
MCMC.burnin = 7000,
MPLE.max.dyad.types = 1e5,
init.method = "MPLE",
MCMLE.maxit = 250)))
summary(fit.c.3)
sum.fit.c.3<-summary(fit.c.3)
modelfits.cohab.3 <- list(fit.c.3, sum.fit.c.3)
save(modelfits.cohab.3, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits.cohab.3.rda")
plot(gof(modelsXXX, GOF="model"))
####Casual partnership network.
summary(ego.obj_p ~edges +nodefactor("race.sex.cohab",base=0) +
nodematch("race",diff=TRUE) + nodefactor("agecat", base=1) +
absdiff("sqrt.age"))
fit.p.3<-ergm.ego(ego.obj_p ~edges +
nodefactor("race.sex.cohab",base=9) +
nodefactor("race",base=5) +
nodematch("race",diff=TRUE) +
nodefactor("agecat", base=1) +
absdiff("sqrt.age") +
offset(nodematch("sex", diff=FALSE)),
offset.coef = -Inf,
control=control.ergm.ego(ppopsize=50000, stats.est="asymptotic",
ergm.control = control.ergm(MCMC.interval=5000,
MCMC.samplesize=5000,
MCMC.burnin = 5000,
MPLE.max.dyad.types = 1e6,
init.method = "MPLE",
MCMLE.maxit = 250)))
summary(fit.p.3)
sum.fit.p.3<-summary(fit.p.3)
modelfits.pers.3 <- list(fit.p.3, sum.fit.p.3)
save(modelfits.pers.3, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits.pers.3.rda")
######One time partnerships.
summary(ego.obj_i ~edges +nodefactor("race.sex.cohab",base=0) + nodefactor("race.sex.pers",base=0) +
nodematch("race",diff=TRUE) + nodefactor("agecat", base=1) +
absdiff("sqrt.age"))
fit.i.3<-ergm.ego(ego.obj_i ~edges +
nodefactor("race.sex.cohab",base=9) +
nodefactor("race.sex.pers",base=9) +
nodematch("race",diff=TRUE) +
nodefactor("agecat", base=1) +
absdiff("sqrt.age") +
offset(nodematch("sex", diff=FALSE)),
offset.coef = -Inf,
control=control.ergm.ego(ppopsize=50000, stats.est="asymptotic",
ergm.control = control.ergm(MCMC.interval=5000,
MCMC.samplesize=5000,
MCMC.burnin = 5000,
MPLE.max.dyad.types = 1e6,
init.method = "MPLE",
MCMLE.maxit = 250)))
summary(fit.i.3)
sum.fit.i.3<-summary(fit.i.3)
modelfits.inst.3 <- list(fit.i.3, sum.fit.i.3)
save(modelfits.inst.3, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits.inst.3.rda")
load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits_cohab.rda")
load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits_casual.rda")
load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/modelfits_inst.rda")
#Create additional required elements for est
# Time unit for simulation, relative to 1 day
time.unit <- 7
method<-1
#Simulation size.
sim.size<-38145
# Mean durations
diss_c = ~offset(edges)
diss_p = ~offset(edges)
#Mortality
ages <- 18:59
age.unit <- 52
asmr.B.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1 / age.unit),
1)
asmr.BI.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1 / age.unit),
1)
asmr.H.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1/age.unit),
1)
asmr.HI.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1/age.unit),
1)
asmr.W.f <- c(rep(0, 17),
1-(1-c(rep(0.000405376, 12),
rep(0.000661066, 10),
rep(0.001378053, 10),
rep(0.003065837, 10)))^(1/age.unit),
1)
asmr.B.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1 / age.unit),
1)
asmr.BI.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1 / age.unit),
1)
asmr.H.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1/age.unit),
1)
asmr.HI.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1/age.unit),
1)
asmr.W.m <- c(rep(0, 17),
1-(1-c(rep(0.000853417, 12),
rep(0.001084014, 10),
rep(0.001982864, 10),
rep(0.005400669, 10)))^(1/age.unit),
1)
exp.mort <- (mean(asmr.B.f[ages]) + mean(asmr.BI.f[ages]) + mean(asmr.H.f[ages])
+ mean(asmr.HI.f[ages]) + mean(asmr.W.f[ages]) + mean(asmr.B.m[ages])
+ mean(asmr.BI.m[ages]) + mean(asmr.H.m[ages])
+ mean(asmr.HI.m[ages]) + mean(asmr.W.m[ages]) ) / 10
coef.diss_c <- dissolution_coefs(dissolution = diss_c,
duration = data.params[[1]]$durs_c / time.unit,
d.rate = exp.mort)
coef.diss_p <- dissolution_coefs(dissolution = diss_p,
duration = data.params[[1]]$durs_p / time.unit,
d.rate = exp.mort)
coef.diss_i <-dissolution_coefs(~offset(edges), 1)
target.stats_c<-as.numeric(fit.c.ego$target.stats[2:length(fit.c.ego$target.stats)])
target.stats_p<-as.numeric(fit.p.ego$target.stats[2:length(fit.p.ego$target.stats)])
target.stats_i<-as.numeric(fit.i.ego$target.stats[2:length(fit.i.ego$target.stats)])
target.stats.names_c<- names(fit.c.ego$target.stats[2:length(fit.c.ego$target.stats)])
target.stats.names_p<- names(fit.p.ego$target.stats[2:length(fit.p.ego$target.stats)])
target.stats.names_i<- names(fit.i.ego$target.stats[2:length(fit.i.ego$target.stats)])
coef.form.crude_c<-fit.c.ego$coef[2:length(fit.c.ego$coef)]
coef.form_c<-coef.form.crude_c
coef.form_c[1]<-coef.form_c[1]+fit.c.ego$coef[1]
coef.form_c[1]<- coef.form_c[1]- coef.diss_c$coef.adj
constraints_c <- ~.
coef.form.crude_p<-fit.p.ego$coef[2:length(fit.p.ego$coef)]
coef.form_p<-coef.form.crude_p
coef.form_p[1]<-coef.form_p[1]+fit.p.ego$coef[1]
coef.form_p[1]<- coef.form_p[1]- coef.diss_p$coef.adj
constraints_p <- ~.
coef.form.crude_i<-fit.i.ego$coef[2:length(fit.i.ego$coef)]
coef.form_i<-coef.form.crude_i
coef.form_i[1]<-coef.form_i[1]+fit.i.ego$coef[1]
coef.form_i[1]<- coef.form_i[1]
constraints_i <- ~.
nw<-network.initialize(sim.size, directed = FALSE, hyper = FALSE, loops = FALSE,
multiple = FALSE, bipartite = FALSE)
fit.c <- list(fit= fit.c.ego, formation=fit.c.ego$formula, target.stats= target.stats_c,
target.stats.names= target.stats.names_c, coef.form = coef.form_c,
coef.form.crude= coef.form.crude_c, coef.diss=coef.diss_c, constraints= constraints_c,
edapprox=TRUE)
fit.p <- list(fit= fit.p.ego, formation=fit.p.ego$formula, target.stats= target.stats_p,
target.stats.names= target.stats.names_p, coef.form = coef.form_p,
coef.form.crude= coef.form.crude_p, coef.diss=coef.diss_p, constraints= constraints_p,
edapprox=TRUE)
fit.i <- list(fit= fit.i.ego, formation=fit.i.ego$formula, target.stats= target.stats_i,
target.stats.names= target.stats.names_i, coef.form = coef.form_i,
coef.form.crude= coef.form.crude_i, coef.diss=coef.diss_i, constraints= constraints_i,
edapprox=TRUE)
param <- param_shamp(data.params)
init <- init_shamp()
control <- control_shamp(nsteps = 5)
est <- list(fit.m, fit.c, fit.i)
#save(est, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/fit.rda")
save(est, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/fitsmall.rda")
save(data.params, file = "~/EpiModelHIV_shamp_modeling/scenarios/est/data.params.rda")
#load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/fit.rda")
load(file = "~/EpiModelHIV_shamp_modeling/scenarios/est/fitsmall.rda")
sim<-netsim(est, param, init, control)
save(sim, file = "~/EpiModelHIV_shamp_modeling/scenarios/sim.rda")
demog.table<-as.data.frame(rbind(demog4,demog52,demog104,demog208,demog312,demog416,demog520))
save(demog.table, file = "~/EpiModelHIV_shamp_modeling/scenarios/demogs.rda")
library(xlsx) #load the package
write.xlsx(x = demog.table, file = "~/EpiModelHIV_shamp_modeling/scenarios/demogs.xlsx",
sheetName = "SHAMP Demog", row.names = FALSE)
netsim(est, param, init, control)
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