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sensitivity.R
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sensitivity.R
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# MIT License
#
# Copyright (c) 2023 Ivan Specht
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
### Sensitivity analysis
# Parameters to assess sensitivity: (as changes from default values)
source("main.R")
## Unhash these for regular run
init <- initialize(
init_mst = T,
pooled_coalescent = T,
disjoint_coalescent = T
)
mcmc <- init[[1]]
data <- init[[2]]
mcmc$psi <- 0.1 / (1 + 0.1)
data$n_local = 10
data$sample_every = 10
data$n_global = 100000
## Unhash these for run on pre-computed data and initial MCMC
# load("data.RData")
# load("mcmc.RData")
a_gs <- c(4, 6) # Default = 5
lambda_gs <- a_gs/5 # Default = 1. Also update a_g to maintain mean of 5. Corresponds to variance of 5 (default), 25, 1
a_ss <- c(4, 6) # Default = 5
lambda_ss <- a_ss/5 # Default = 1. Also update a_s to maintain mean of 5. Corresponds to variance of 5 (default), 25, 1
rhos <- c(0.05, 0.2) # Default = 0.1. Also update psi = rho / (1 + rho)
psis <- 0.1 / (c(0.8, 1.2) + 0.1)
vs <- c(500, 2000)
# jth column is corresponds to each of the above params, in order
combos <- matrix(c(5, 1, 5, 1, 0.1, 0.1/(1 + 0.1), 1000), nrow = 15, ncol = 7, byrow = T)
combos[1:2, 1] <- a_gs
combos[3:4, 1] <- lambda_gs * 5
combos[3:4, 2] <- lambda_gs
combos[5:6, 3] <- a_ss
combos[7:8, 3] <- lambda_ss * 5
combos[7:8, 4] <- lambda_ss
combos[9:10, 5] <- rhos
combos[9:10, 6] <- rhos / (1 + rhos)
combos[11:12, 6] <- psis
combos[13:14, 7] <- vs
run_combo <- function(i, mcmc){
mcmc$a_g <- combos[i, 1]
mcmc$lambda_g <- combos[1, 2]
mcmc$a_s <- combos[i, 3]
mcmc$lambda_s <- combos[i, 4]
mcmc$rho <- combos[i, 5]
mcmc$psi <- combos[i, 6]
mcmc$v <- combos[i, 7]
out <- run_mcmc(mcmc, data)
return(out)
}
outs <- mclapply(1:nrow(combos), run_combo, mcmc = mcmc, mc.cores = nrow(combos))
save(outs, file = "outs.RData")
#load("outs.RData")
sensitivity <- list()
all_liks <- list()
for (i in 1:nrow(combos)) {
all_liks[[i]] <- outs[[i]][[1]]
sensitivity[[i]] <- outs[[i]][[2]]
}
## Get adjacency matrix. Ancestor > data$n_obs doesn't count for anything. Indirect transmission OK
get_adj <- function(run, n_obs){
out <- matrix(0, nrow = n_obs, ncol = n_obs)
len <- length(run)
for (i in (len*0.1 + 1):len) {
h <- run[[i]]$h
present <- which(!is.na(h) & h <= n_obs)
present <- present[present <= n_obs]
out[cbind(h[present], present)] <- out[cbind(h[present], present)] + 1
}
return(out / (0.9*len))
}
adjs <- lapply(sensitivity, get_adj, n_obs = data$n_obs)
# Run on default settings
default <- outs[[nrow(combos)]]
default_adj <- get_adj(default[[2]], data$n_obs)
# Histogram of change in adjacency matrix
network_change <- function(i){
change <- as.vector(adjs[[i]] - default_adj)
change <- change[abs(change) > 0]
p <- ggplot(data.frame(x = change), aes(x = x)) +
geom_histogram(aes(y=after_stat(density)), binwidth = 0.1, boundary = 0.1, color = "white", fill = "grey") +
xlab("Change in Posterior Probability") +
ylab("Probability Density") +
#scale_y_continuous(trans='log2') +
xlim(-1, 1) +
theme_minimal()
p
}
hists <- lapply(1:nrow(combos), network_change)
all_hists <- plot_grid(
hists[[1]],
hists[[2]],
hists[[3]],
hists[[4]],
hists[[5]],
hists[[6]],
hists[[7]],
hists[[8]],
hists[[9]],
hists[[10]],
hists[[11]],
hists[[12]],
hists[[13]],
hists[[14]],
hists[[15]],
ncol = 3,
labels = "AUTO"
)
ggsave("./figs/hist.pdf", width = 9.1, height = 12)
ggsave("./figs/hist.png", width = 9.1, height = 12)
## Next up, density plots of mu
param_dens <- function(i, param){
run <- sensitivity[[i]]
out <- c()
len <- length(run)
for (j in (len*0.1 + 1):len) {
out <- c(out, (run[[j]][[param]]))
}
p <- ggplot(data.frame(x = out), aes(x = x)) +
geom_histogram(aes(y=after_stat(density)), color = "white", fill = "grey") +
xlab(paste("Value of", param)) +
ylab("Probability Density") +
scale_x_continuous(breaks = signif(seq(min(out), max(out), by = (max(out) - min(out)) / 2) , 2)) +
scale_y_continuous(labels = function(x) format(x, scientific = TRUE)) +
theme_minimal() +
theme(plot.margin = margin(t = 0, r = 0.2, b = 0, l = 0, unit = "in"))
p
}
output_plots <- function(param){
dens <- lapply(1:nrow(combos), param_dens, param = param)
all_dens <- plot_grid(
dens[[1]],
dens[[2]],
dens[[3]],
dens[[4]],
dens[[5]],
dens[[6]],
dens[[7]],
dens[[8]],
dens[[9]],
dens[[10]],
dens[[11]],
dens[[12]],
dens[[13]],
dens[[14]],
dens[[15]],
ncol = 3,
labels = "AUTO"
)
ggsave(paste0("./figs/", param, ".pdf"), width = 9.1, height = 12)
ggsave(paste0("./figs/", param, ".png"), width = 9.1, height = 12)
}
output_plots("mu")
output_plots("p")
#output_plots("lambda")
output_plots("b")
# system("gcloud storage cp ./figs/b.pdf gs://broad-viral/ispecht")
# system("gcloud storage cp ./figs/mu.pdf gs://broad-viral/ispecht")
# system("gcloud storage cp ./figs/p.pdf gs://broad-viral/ispecht")
# system("gcloud storage cp ./figs/hist.pdf gs://broad-viral/ispecht")
# system("gcloud storage cp outs.RData gs://broad-viral/ispecht")