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simnets.rmd
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---
title: "Network Simulation Models"
output: html_notebook
date: '2022-07-18'
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Libraries
```{r libraries}
# require(EpiModel)
require(igraph)
# require(reticulate)
require(dplyr)
require(ggplot2)
require(desplot)
require(patchwork)
require(purrr)
require(furrr)
require(tidyverse)
require(RColorBrewer)
# require(gganimate)
require(ggforce)
require(ggpubr)
plan("multisession", workers = availableCores() / 4)
set.seed(1000)
```
## Parameters
```{r params}
N <- 20 #network population/ graph order
model = "ER"
eprob <- 0.1 #edge formation probability for ER model
pow <- 1 #preferential attachment power for BA model
nb <- 20 # neighborhood parameter for small-world model
rprob <- 0.05 #rewiring probability for small-world model
phiv <- 0.1 # underlying hiv prevalence
PrEP1 <- 0.2 # PrEP assignment coverage in control treatment (a)
PrEP2 <-
0.4 # PrEP assignment coverage in counterfactual treatment (a*)
#HIV risk by contact and PrEP allocation
p1 <- 0.2 #P(HIV|Contact and -PrEP)
p2 <- 0.1 #P(HIV|Contact and PrEP)
nsim <- 200
plots = T
```
## Simulation Function
```{r simulation}
sim <-
function(N = 20,
phiv = 0.1,
PrEP1 = 0.1,
PrEP2 = 0.2,
p1 = 0.2,
p2 = 0.1,
plots = F,
model = c("ER", "BA", "WS"),
eprob = 0.1,
pow = 1,
nb = 5,
rprob = 0.05) {
#parameter check
model <- match.arg(model, several.ok = FALSE)
#store arguments
args <- c(N, phiv, PrEP1, PrEP2, p1, p2, model, eprob, pow, nb, rprob)
names(args) <-
c("N",
"phiv",
"PrEP1",
"PrEP2",
"p1",
"p2",
"model",
"eprob",
"pow",
"nb",
"rprob")
#control scenario graph, PrEP1% assignment prob
g <-
if (model == "ER") {
sample_gnp(N, eprob)
} else if (model == "BA") {
sample_pa(N, power = pow, directed = FALSE)
} else if (model == "WS") {
sample_smallworld(
dim = 1,
size = N,
nei = nb,
p = rprob
)
}
l_hiv_g <- round((gorder(g) * phiv), 0)
l_PrEP1_g <- round(((gorder(g) - l_hiv_g) * PrEP1), 0)
l_sus_g <- round((gorder(g) - (l_hiv_g + l_PrEP1_g)), 0)
vertex_attr(g) <-
list(color = c(
rep("red", l_hiv_g),
rep("blue", l_PrEP1_g),
rep("black", l_sus_g)
))
if (plots) {
coords = layout_nicely(g)
plot(
g,
vertex.size = 10,
vertex.label.cex = 1,
vertex.label.dist = 2,
main = "Control",
layout = coords
)
}
inf_contact_g <-
setdiff(unlist(adjacent_vertices(g, v = V(g)[color == "red"])), V(g)[color ==
"red"])
#compute P(HIV|PrEP)
treat_g <- V(g)[V(g)$color == "blue"]
treat_inf_contact_g <-
V(g)[inf_contact_g][which(V(g)[inf_contact_g]$color == "blue")]
g <-
set_vertex_attr(g,
name = "color",
index = inf_contact_g,
value = "orange")
g <-
set_vertex_attr(g,
name = "color",
index = treat_inf_contact_g,
value = "purple")
if (plots) {
plot(
g,
vertex.size = 10,
vertex.label.cex = 1,
vertex.label.dist = 2,
main = "Control",
layout = coords
)
}
hiv_given_prep_g <-
ifelse(length(treat_g) != 0, (length(treat_inf_contact_g) * p2) / length(treat_g), 0)
#Compute P(HIV|-PrEP)
no_treat_g <- V(g)[V(g)$color %in% c("black", "orange")]
no_treat_inf_contact_g <- V(g)[V(g)$color == "orange"]
hiv_given_no_prep_g <-
ifelse(length(no_treat_g) != 0, (length(no_treat_inf_contact_g) * p1) /
length(no_treat_g), 0)
#Randomly assign PrEP2% overall (shuffle attributes)
l_hiv_h <- round((gorder(g) * phiv), 0)
l_PrEP2_h <- round(((gorder(g) - l_hiv_h) * PrEP2), 0)
l_sus_h <- round((gorder(g) - (l_hiv_h + l_PrEP2_h)), 0)
h <-
set_vertex_attr(g, "color", value = c(rep("red", l_hiv_h), sample(c(
rep("blue", l_PrEP2_h), rep("black", l_sus_h)
))))
if (plots) {
plot(
h,
vertex.size = 10,
vertex.label.cex = 1,
vertex.label.dist = 2,
main = "Random overall",
layout = coords
)
}
inf_contact_h <-
setdiff(unlist(adjacent_vertices(h, v = V(h)[color == "red"])), V(h)[color ==
"red"])
#Compute P(HIV|PrEP)
treat_h <- V(h)[V(h)$color == "blue"]
treat_inf_contact_h <-
V(h)[inf_contact_h][which(V(h)[inf_contact_h]$color == "blue")]
h <-
set_vertex_attr(h,
name = "color",
index = inf_contact_h,
value = "orange")
h <-
set_vertex_attr(h,
name = "color",
index = treat_inf_contact_h,
value = "purple")
if (plots) {
plot(
h,
vertex.size = 10,
vertex.label.cex = 1,
vertex.label.dist = 2,
main = "Random overall",
layout = coords
)
}
hiv_given_prep_h <-
ifelse(length(treat_h) != 0, (length(treat_inf_contact_h) * p2) / length(treat_h), 0)
#Compute P(HIV|-PrEP)
no_treat_h <- V(h)[V(h)$color %in% c("black", "orange")]
no_treat_inf_contact_h <- V(h)[V(h)$color == "orange"]
hiv_given_no_prep_h <-
ifelse(length(no_treat_h) != 0, (length(no_treat_inf_contact_h) * p1) /
length(no_treat_h), 0)
#duplicate network structure, additional PrEP2-PrEP1% treated
l_hiv_j <- round((gorder(g) * phiv), 0)
l_PrEP2_j <- round(((gorder(g) - l_hiv_j) * PrEP2), 0)
l_sus_j <- round((gorder(g) - (l_hiv_j + l_PrEP2_j)), 0)
j <-
set_vertex_attr(g, "color", value = c(
rep("red", l_hiv_j),
rep("blue", l_PrEP2_j),
rep("black", l_sus_j)
))
if (plots) {
plot(
j,
vertex.size = 10,
vertex.label.cex = 1,
vertex.label.dist = 2,
main = "Random additional",
layout = coords
)
}
inf_contact_j <-
setdiff(unlist(adjacent_vertices(j, v = V(j)[color == "red"])), V(j)[color ==
"red"])
#compute (HIV|PrEP)
treat_j <- V(j)[V(j)$color == "blue"]
treat_inf_contact_j <-
V(j)[inf_contact_j][which(V(j)[inf_contact_j]$color == "blue")]
j <-
set_vertex_attr(j,
name = "color",
index = inf_contact_j,
value = "orange")
j <-
set_vertex_attr(j,
name = "color",
index = treat_inf_contact_j,
value = "purple")
if (plots) {
plot(
j,
vertex.size = 10,
vertex.label.cex = 1,
vertex.label.dist = 2,
main = "Random additional",
layout = coords
)
}
hiv_given_prep_j <-
ifelse(length(treat_j) != 0, (length(treat_inf_contact_j) * p2) / length(treat_j), 0)
#Compute P(HIV|-PrEP)
no_treat_j <- V(j)[V(j)$color %in% c("black", "orange")]
no_treat_inf_contact_j <- V(j)[V(j)$color == "orange"]
hiv_given_no_prep_j <-
ifelse(length(no_treat_j) != 0, (length(no_treat_inf_contact_j) * p1) /
length(no_treat_j), 0)
# Regenerated graph
k <-
if (model == "ER") {
sample_gnp(N, eprob)
} else if (model == "BA") {
sample_pa(N, power = pow, directed = FALSE)
} else if (model == "WS") {
sample_smallworld(
dim = 1,
size = N,
nei = nb,
p = rprob
)
}
l_hiv_k <- round((gorder(k) * phiv), 0)
l_PrEP2_k <- round(((gorder(k) - l_hiv_k) * PrEP2), 0)
l_sus_k <- round((gorder(k) - (l_hiv_k + l_PrEP2_k)), 0)
vertex_attr(k) <-
list(color = c(
rep("red", l_hiv_k),
rep("blue", l_PrEP2_k),
rep("black", l_sus_k)
))
if (plots) {
plot(
k,
vertex.size = 10,
vertex.label.cex = 1,
vertex.label.dist = 2,
main = "Regenerated",
layout = coords
)
}
inf_contact_k <-
setdiff(unlist(adjacent_vertices(k, v = V(k)[color == "red"])), V(k)[color ==
"red"])
#Compute P(HIV|PrEP)
treat_k <- V(k)[V(k)$color == "blue"]
treat_inf_contact_k <-
V(k)[inf_contact_k][which(V(k)[inf_contact_k]$color == "blue")]
k <-
set_vertex_attr(k,
name = "color",
index = inf_contact_k,
value = "orange")
k <-
set_vertex_attr(k,
name = "color",
index = treat_inf_contact_k,
value = "purple")
if (plots) {
plot(
k,
vertex.size = 10,
vertex.label.cex = 1,
vertex.label.dist = 2,
main = "Regenerated",
layout = coords
)
}
hiv_given_prep_k <-
ifelse(length(treat_k) != 0, (length(treat_inf_contact_k) * p2) / length(treat_k), 0)
#Compute P(HIV|-PrEP)
no_treat_k <- V(k)[V(k)$color %in% c("black", "orange")]
no_treat_inf_contact_k <- V(k)[V(k)$color == "orange"]
hiv_given_no_prep_k <-
ifelse(length(no_treat_k) != 0, (length(no_treat_inf_contact_k) * p1) /
length(no_treat_k), 0)
#Network Summary Statistics
stats_g <- c(
Co_g <- count_components(g),
#Number of Components
Cs_g <- max(components(g)$csize),
#Largest Component Size
B_g <- mean(betweenness(g)),
# Average Betweenness Centrality
De_g <- edge_density(g),
#Density
Ce_g <- centr_degree(g)$centralization,
#Degree Centralization
G_g <- mean_distance(g),
#Average geodesic distance
Di_g <- diameter(g),
#Network Diameter
T_g <- transitivity(g),
#Transitivity/Clustering
K_g <-
sum(coreness(g) == 2) / length(coreness(g)),
#Proportion of nodes in 2-cores
As_g <- assortativity_degree(g, directed = F) #Degree Assortativity
)
names(stats_g) <-
c(
"Number of Components g",
"Largest Component Size g",
"Avg. Betweenness g",
"Density g",
"Degree Centralization g",
"Avg. Geodesic Distance g",
"Diameter g",
"Transitivity g",
"Proportion of nodes in 2-cores g",
"Degree Assortativity g"
)
stats_k <- c(
Co_k <- count_components(k),
#Number of Components
Cs_k <- max(components(k)$csize),
#Largest Component Size
B_k <- mean(betweenness(k)),
# Average Betweenness Centrality
De_k <- edge_density(k),
#Density
Ce_k <- centr_degree(k)$centralization,
#Degree Centralization
G_k <- mean_distance(k),
#Average geodesic distance
Di_k <- diameter(k),
#Network Diameter
T_k <- transitivity(k),
#Transitivity/Clustering
K_k <-
sum(coreness(k) == 2) / length(coreness(k)),
#Proportion of nodes in 2-cores,
As_k <- assortativity_degree(k, directed = F) #Degree Assortativity
)
names(stats_k) <-
c(
"Number of Components k",
"Largest Component Size k",
"Avg. Betweenness k",
"Density k",
"Degree Centralization k",
"Avg. Geodesic Distance k",
"Diameter k",
"Transitivity k",
"Proportion of nodes in 2-cores k",
"Degree Assortativity k"
)
# Combine effect estimates
prep <-
c(hiv_given_prep_g,
hiv_given_prep_h,
hiv_given_prep_j,
hiv_given_prep_k)
names(prep) <-
c("hiv_given_prep_g",
"hiv_given_prep_h",
"hiv_given_prep_j",
"hiv_given_prep_k")
no_prep <-
c(
hiv_given_no_prep_g,
hiv_given_no_prep_h,
hiv_given_no_prep_j,
hiv_given_no_prep_k
)
names(no_prep) <-
c(
"hiv_given_no_prep_g",
"hiv_given_no_prep_h",
"hiv_given_no_prep_j",
"hiv_given_no_prep_k"
)
ef <- prep + no_prep
names(ef) <- c("control,", "random", "additive", "regenerated")
#compute causal contrasts
cc <- as.data.frame(t(ef[-1] - ef[1]))
names(cc) <- c("random", "additive", "regenerated")
res <-
cbind(
as.data.frame(t(args)),
as.data.frame(t(prep)),
as.data.frame(t(no_prep)),
cc,
as.data.frame(t(stats_g)),
as.data.frame(t(stats_k))
)
names(res) <-
c(
names(args),
names(prep),
names(no_prep),
names(cc),
names(stats_g),
names(stats_k)
)
return(res)
}
```
## Parallel resampling
```{r simulation parallel}
sim_par <-
function(N = 20,
phiv = 0.1,
PrEP1 = 0.1,
PrEP2 = 0.2,
p1 = 0.2,
p2 = 0.1,
model = "ER",
eprob = 0.1,
pow = 1,
nb = 5,
rprob = 0.05,
nsim = 200) {
res <-
future_map_dfr(
1:nsim,
~ sim(
N = N,
phiv = phiv,
PrEP1 = PrEP1,
PrEP2 = PrEP2,
p1 = p1,
p2 = p2,
model = model,
eprob = eprob,
pow = pow,
nb = nb,
rprob = rprob
),
.options = furrr_options(seed = TRUE)
)
res <- cbind(res, nsim)
return(res)
}
```
Trying to parallellize sim rather than use simpar, use rerun from purrr?
Don't use rerun, use a map with sim_par?
```{r}
col_list = c("random", "additive", "regenerated")
```
## Results
### Check sensitivity to network size, plot heatmap by N
```{r}
params_N <-
tidyr::expand_grid(
N = c(20, 200,2000),
model = "ER",
phiv = 0.1,
PrEP1 = 0.2,
PrEP2 = 0.4,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
nsim = 200
)
# future::plan(
# list(
# future::tweak(
# future::multisession,
# workers = 2),
# future::tweak(
# future::multisession,
# workers = 12)
# )
# )
set.seed(1000)
res_N <-
future_pmap_dfr(params_N,
sim_par,
.options = furrr_options(seed = TRUE),
.progress = TRUE)
means_N <-
res_N %>% group_by(N, p1, p2) %>% summarise(across(all_of(col_list), mean))
var_N <-
res_N %>% group_by(N, p1, p2) %>% summarise(across(all_of(col_list), var))
save(res_N,means_N,var_N,file="Network Size Results.RData")
```
### check sensitivity to sample size-cluster?
```{r}
params_samp <-
tidyr::expand_grid(
N = 20,
phiv = .1,
PrEP1 = 0.2,
PrEP2 = 0.4,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
nsim = 10 ^ seq(2, 4, 1)
)
# future::plan(
# list(
# future::tweak(
# future::multisession,
# workers = 4),
# future::tweak(
# future::multisession,
# workers = 6)
# )
# )
system.time(
res_samp <-
future_pmap_dfr(
params_samp,
sim_par,
.options = furrr_options(seed = TRUE),
.progress = TRUE
)
)
means_samp <-
res_samp %>% group_by(nsim, p1, p2) %>% summarise(across(col_list, mean))
var_samp <-
res_samp %>% group_by(nsim, p1, p2) %>% summarise(across(col_list, var))
beepr::beep(8)
# save(res_samp,means_samp,var_samp,file=paste("N=",N,"Sample size results.RData", sep=" "))
```
### Increase underlying HIV prevalence phiv
```{r}
params_phiv <-
expand.grid(
N = 20,
phiv = seq(0.1, 1, 0.1),
PrEP1 = PrEP1,
PrEP2 = PrEP2,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
nsim = nsim
)
future::plan(list(
future::tweak(future::multisession,
workers = 3),
future::tweak(future::multisession,
workers = 8)
))
res_phiv <- future_pmap_dfr(params_phiv, sim_par)
means_phiv <-
res_phiv %>% group_by(across(all_of(c("phiv", "p1", "p2")))) %>% summarise(across(col_list, mean))
var_phiv <-
res_phiv %>% group_by(across(all_of(c("phiv", "p1", "p2")))) %>% summarise(across(col_list, var))
beepr::beep(8)
save(res_phiv, means_phiv, var_phiv, file = "HIV Prevalence Results.RData")
```
### Increasing p1 only
```{r}
params_p1 <-
expand_grid(
N = 20,
phiv = phiv,
PrEP1 = PrEP1,
PrEP2 = PrEP2,
p1 = seq(0.1, 1, 0.01),
p2 = 0.1,
nsim = nsim
)
# future::plan("multisession",workers=future::availableCores())
res_p1 <- future_pmap_dfr(params_p1, sim_par)
means_p1 <-
res_p1 %>% group_by(p1, p2) %>% summarise(across(col_list, mean))
var_p1 <- res_p1 %>% group_by(p1, p2) %>% summarise(across(col_list, var))
save(res_p1, means_p1, var_p1, file = "p1 Results.RData")
```
### Increasing p2 only
```{r}
params_p2 <-
expand_grid(
N = 20,
phiv = 0.1,
PrEP1 = PrEP1,
PrEP2 = PrEP2,
p2 = seq(0.1, 1, 0.01),
p1 = 0.1,
nsim = nsim
)
res_p2 <- future_pmap_dfr(params_p2, sim_par)
means_p2 <-
res_p2 %>% group_by(p2, p1) %>% summarise(across(col_list, mean))
var_p2 <- res_p2 %>% group_by(p2, p1) %>% summarise(across(col_list, var))
save(res_p2, means_p2, var_p2, file = "p2 Results.RData")
```
### Increasing PrEP1 and PrEP2
```{r}
params_PrEP <-
expand_grid(
N = 20,
phiv = 0.1,
PrEP1 = seq(0.1, 1, 0.1),
PrEP2 = seq(0.1, 1, 0.1),
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
nsim = nsim
) %>% filter(PrEP2 > PrEP1)
# plan(multisession(workers = availableCores()))
# future::plan(
# list(
# future::tweak(
# future::multisession,
# workers = 4),
# future::tweak(
# future::multisession,
# workers = 6)
# )
# )
system.time(res_PrEP <- future_pmap_dfr(params_PrEP, sim_par))
beepr::beep(8)
means_PrEP <-
res_PrEP %>% group_by(across(all_of(c(
"PrEP1", "PrEP2", "p1", "p2"
)))) %>% summarise(across(col_list, mean), .groups = "keep")
var_PrEP <-
res_PrEP %>% group_by(across(all_of(c(
"PrEP1", "PrEP2", "p1", "p2"
)))) %>% summarise(across(col_list, var), .groups = "keep")
save(res_PrEP, means_PrEP, var_PrEP, file = "PrEP Results.RData")
```
### Change network generative model with defaults
```{r}
params_model <-
expand.grid(
N = 50,
phiv = 0.1,
model = c("ER", "BA", "WS"),
PrEP1 = PrEP1,
PrEP2 = PrEP2,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
nsim = nsim,
stringsAsFactors = F
)
plan("multisession", workers = availableCores() / 4)
res_model <- future_pmap_dfr(params_model, sim_par)
means_model <-
res_model %>% group_by(across(all_of(c("model", "p1", "p2")))) %>% summarise(across(col_list, mean))
var_model <-
res_model %>% group_by(across(all_of(c("model", "p1", "p2")))) %>% summarise(across(col_list, var))
save(res_model, means_model, var_model, file = "Generative Model Results.RData")
```
### Change network generative model with larger network size
```{r}
params_model_large <-
expand.grid(
N = N,
phiv = 0.1,
model = c("ER", "BA", "WS"),
PrEP1 = PrEP1,
PrEP2 = PrEP2,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
nsim = nsim,
stringsAsFactors = F
)
plan("multisession", workers = availableCores() / 4)
res_model_large <- future_pmap_dfr(params_model_large, sim_par)
means_model_large <-
res_model_large %>% group_by(across(all_of(c("model", "p1", "p2")))) %>% summarise(across(col_list, mean))
var_model_large <-
res_model_large %>% group_by(across(all_of(c("model", "p1", "p2")))) %>% summarise(across(col_list, var))
save(
res_model_large,
means_model_large,
var_model_large,
file = paste("N=", N, "Generative Model Results.RData")
)
```
### Change ER edge probability
```{r}
params_eprob <-
expand.grid(
N = N,
eprob = seq(0.1, 1, 0.1),
phiv = 0.1,
PrEP1 = PrEP1,
PrEP2 = PrEP2,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
nsim = nsim
)
res_eprob <- future_pmap_dfr(params_eprob, sim_par)
means_eprob <-
res_eprob %>% group_by(across(all_of(c("eprob", "p1", "p2")))) %>% summarise(across(col_list, mean))
var_eprob <-
res_eprob %>% group_by(across(all_of(c("eprob", "p1", "p2")))) %>% summarise(across(col_list, var))
save(res_eprob, means_eprob, var_eprob, file = "ER edge probability Results.RData")
```
### Change BA preferential attachment power
```{r}
params_pow <-
expand.grid(
N = N,
phiv = 0.1,
PrEP1 = 0.2,
PrEP2 = 0.4,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
model = "BA",
pow = seq(1, 2, 0.1),
nsim = nsim,
stringsAsFactors = F
)
res_pow <- future_pmap_dfr(params_pow, sim_par)
means_pow <-
res_pow %>% group_by(across(all_of(c("pow", "p1", "p2")))) %>% summarise(across(col_list, mean))
var_pow <-
res_pow %>% group_by(across(all_of(c("pow", "p1", "p2")))) %>% summarise(across(col_list, var))
save(res_pow, means_pow, var_pow, file = "Preferential attachment power results.RData")
```
### Change Small-World neighborhood size
```{r}
params_nb <-
expand.grid(
N = N,
phiv = phiv,
PrEP1 = PrEP1,
PrEP2 = PrEP2,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
scale = "additive",
model = "WS",
eprob = eprob,
pow = pow,
nb = seq(5, N / 2, 5),
rprob = rprob,
nsim = nsim,
stringsAsFactors = F
)
plan("multisession", workers = availableCores() / 4)
res_nb <- future_pmap_dfr(params_nb, sim_par)
means_nb <-
res_nb %>% group_by(across(all_of(c("nb", "p1", "p2")))) %>% summarise(across(col_list, mean))
var_nb <-
res_nb %>% group_by(across(all_of(c("nb", "p1", "p2")))) %>% summarise(across(col_list, var))
save(res_nb, means_nb, var_nb, file = "Small World Neighborhood Size Results.RData")
```
### Change Small-World rewiring probability
```{r}
params_rprob <-
expand.grid(
N = N,
phiv = phiv,
PrEP1 = PrEP1,
PrEP2 = PrEP2,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
scale = "additive",
model = "WS",
eprob = eprob,
pow = pow,
nb = nb,
rprob = seq(0.05, 1, 0.1),
nsim = nsim,
stringsAsFactors = F
)
res_rprob <- future_pmap_dfr(params_rprob, sim_par)
means_rprob <-
res_rprob %>% group_by(across(all_of(c("rprob", "p1", "p2")))) %>% summarise(across(col_list, mean))
var_rprob <-
res_rprob %>% group_by(across(all_of(c("rprob", "p1", "p2")))) %>% summarise(across(col_list, var))
save(res_rprob, means_rprob, var_rprob, file = "Small World Rewiring Probability Results.RData")
```
### Check BA Model
```{r}
params_ba <-
expand.grid(
N = 50,
phiv = 0.1,
PrEP1 = 0.2,
PrEP2 = 0.4,
p1 = seq(0.1, 1, 0.1),
p2 = seq(0.1, 1, 0.1),
model = "BA",
pow = pow,
nsim = nsim,
stringsAsFactors = F
)
res_ba <- future_pmap_dfr(params_ba, sim_par)
means_ba <-
res_ba %>% group_by(across(all_of(c("p1", "p2")))) %>% summarise(across(col_list, mean))
var_ba <-
res_ba %>% group_by(across(all_of(c("p1", "p2")))) %>% summarise(across(col_list, var))
save(res_ba, means_ba, var_ba, file = "BA check Results.RData")
lims <- c(min(means_ba[, col_list[2]]), max(means_ba[, col_list[2]]))
ggplot(means_ba, aes_string(x = "p1", y = "p2", fill = col_list[2])) + geom_tile() +
scale_fill_gradientn(limits = lims, colors = brewer.pal(6, name = "RdYlBu")) +
xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
```
http://127.0.0.1:18177/graphics/plot_zoom_png?width=1091&height=900
## Figures
### Mean plots
#### Network Size
```{r}
lims_means_N <- c(min(means_N[, col_list]), max(means_N[, col_list]))
N_plot_1 <-
ggplot(means_N, aes_string(x = "p1", y = "p2", fill = col_list[1])) + geom_tile() +
facet_grid( ~ N) + scale_fill_gradientn(limits = lims_means_N, colors =
brewer.pal(6, name = "RdYlBu")) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
N_plot_2 <-
ggplot(means_N, aes_string(x = "p1", y = "p2", fill = col_list[2])) + geom_tile() +
facet_grid( ~ N) + scale_fill_gradientn(limits = lims_means_N, colors =
brewer.pal(6, name = "RdYlBu")) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
N_plot_3 <-
ggplot(means_N, aes_string(x = "p1", y = "p2", fill = col_list[3])) + geom_tile() +
facet_grid( ~ N) + scale_fill_gradientn(limits = lims_means_N, colors =
brewer.pal(6, name = "RdYlBu")) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
plot = N_plot_2 / N_plot_1 / N_plot_3 + # Create grid of plots with title
plot_annotation(title = "Contrast Estimate Mean by Network Size and Allocation") &
theme(plot.title = element_text(hjust = 0.5))
# ggsave(plot=N_plot_2/N_plot_1/N_plot_3 + # Create grid of plots with title
# plot_annotation(title = "Contrast Estimate Mean by Network Size and Allocation" ) &
# theme(plot.title = element_text(hjust = 0.5)),
# file="Network Size Mean Plot.png",path="Figures/")
# ggsave(N_plot_2/N_plot_1/N_plot_3,file="Network Size Mean Plot.png",path="Figures/")
```
```{r}
lims_means_N_2 <-
c(min(means_N_2[, col_list]), max(means_N_2[, col_list]))
N_2_plot_1 <-
ggplot(means_N_2, aes_string(x = "p1", y = "p2", fill = col_list[1])) +
geom_tile() + facet_grid( ~ N) + scale_fill_gradientn(limits = lims_means_N_2, colors =
brewer.pal(6, name = "RdYlBu")) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
N_2_plot_2 <-
ggplot(means_N_2, aes_string(x = "p1", y = "p2", fill = col_list[2])) +
geom_tile() + facet_grid( ~ N) + scale_fill_gradientn(limits = lims_means_N_2, colors =
brewer.pal(6, name = "RdYlBu")) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
N_2_plot_3 <-
ggplot(means_N_2, aes_string(x = "p1", y = "p2", fill = col_list[3])) +
geom_tile() + facet_grid( ~ N) + scale_fill_gradientn(limits = lims_means_N_2, colors =
brewer.pal(6, name = "RdYlBu")) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
N_2_plot_2 / N_2_plot_1 / N_2_plot_3 + # Create grid of plots with title
plot_annotation(title = "Contrast Estimate Mean by Network Size and Allocation") &
theme(plot.title = element_text(hjust = 0.5))
```
#### Sample size
```{r}
lims_means_samp <-
c(min(means_samp[, col_list]), max(means_samp[, col_list]))
samp_plot_1 <-
ggplot(means_samp, aes_string(x = "p1", y = "p2", fill = col_list[1])) +
geom_tile() + facet_grid( ~ nsim) + scale_fill_gradientn(limits = lims_means_samp, colors =
brewer.pal(5, name = "RdYlBu")) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
samp_plot_2 <-
ggplot(means_samp, aes_string(x = "p1", y = "p2", fill = col_list[2])) +
geom_tile() + facet_grid( ~ nsim) + scale_fill_gradientn(limits = lims_means_samp, colors =
brewer.pal(5, name = "RdYlBu")) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
samp_plot_3 <-
ggplot(means_samp, aes_string(x = "p1", y = "p2", fill = col_list[3])) +
geom_tile() + facet_grid( ~ nsim) + scale_fill_gradientn(limits = lims_means_samp, colors =
brewer.pal(5, name = "RdYlBu")) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
samp_plot_2 / samp_plot_1 / samp_plot_3
# ggsave(plot=
samp_plot_2 / samp_plot_1 / samp_plot_3 + # Create grid of plots with title
plot_annotation(title = "Contrast Estimate Mean by Sample Size and Allocation") &
theme(plot.title = element_text(hjust = 0.5))
# , file="Sample Size Mean plots.png",path="Figures/")
```
#### HIV prevalence
```{r}
lims_means_phiv <-
c(min(means_phiv[means_phiv$phiv < 0.9, col_list]), max(means_phiv[means_phiv$phiv <
0.9, col_list]))
phiv_plot_1 <-
means_phiv %>% filter(phiv < 0.9) %>% ggplot(aes_string(x = "p1", y = "p2", fill =
col_list[1])) + scale_x_discrete(guide = guide_axis(n.dodge = 2)) + geom_tile() +
facet_grid( ~ phiv) + scale_fill_gradientn(
limits = lims_means_phiv,
n.breaks = 10,
colors = brewer.pal(10, name = "RdYlBu")
) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)")
phiv_plot_2 <-
means_phiv %>% filter(phiv < 0.9) %>% ggplot(aes_string(x = "p1", y = "p2", fill =
col_list[2])) + geom_tile() + facet_grid( ~ phiv) + scale_fill_gradientn(
limits = lims_means_phiv,
n.breaks = 10,
colors = brewer.pal(10, name = "RdYlBu")
) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)") +
scale_x_discrete(guide = guide_axis(n.dodge = 2))
phiv_plot_3 <-
means_phiv %>% filter(phiv < 0.9) %>% ggplot(aes_string(x = "p1", y = "p2", fill =
col_list[3])) + geom_tile() + facet_grid( ~ phiv) + scale_fill_gradientn(
limits = lims_means_phiv,
n.breaks = 10,
colors = brewer.pal(10, name = "RdYlBu")
) + xlab("P(HIV|Contact and -PrEP)") + ylab("P(HIV|Contact and PrEP)") +
scale_x_discrete(guide = guide_axis(n.dodge = 2))
ggarrange(phiv_plot_2 / phiv_plot_1 / phiv_plot_3)
ggsave(
plot = phiv_plot_2 / phiv_plot_1 / phiv_plot_3 + # Create grid of plots with title
plot_annotation(title = "Contrast Estimate Mean by HIV Prevalence and Allocation") &
theme(plot.title = element_text(hjust = 0.5)),
file = "HIV Prevalence Mean plots.png",
path = "Figures/"
)
```
#### P(HIV|Contact and -PrEP)
```{r}
lims_means_p1 <- c(min(means_p1[, col_list]), max(means_p1[, col_list]))
p1_plot_1 <-