no_control_runs_df = process_runs(no_control_runs)
control_runs_df = process_runs(control_runs)
expensive_control_runs_df = process_runs(expensive_control_runs)
library(ggplot2)
ggplot(subset(no_control_runs_df, variable %in% c("N", "P")), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5)
ggplot(subset(control_runs_df, variable %in% c("N", "P")), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5)
ggplot(subset(expensive_control_runs_df, variable %in% c("N", "P")), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5)
ggplot(subset(no_control_runs_df, variable %in% c("N", "P") & run == sample.int(50,1)), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5)
ggplot(subset(control_runs_df, variable %in% c("N", "P") & run == sample.int(50,1)), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5)
ggplot(subset(expensive_control_runs_df, variable %in% c("N", "P") & run == sample.int(50,1)), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5)
ggplot(subset(no_control_runs_df, variable %in% c("S1", "S2")), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5) + scale_y_log10()
ggplot(subset(control_runs_df, variable %in% c("S1", "S2")), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5) + scale_y_log10()
ggplot(subset(expensive_control_runs_df, variable %in% c("S1", "S2")), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5) + scale_y_log10()
ggplot(subset(no_control_runs_df, variable %in% c("S1", "S2") & run == sample.int(50,1)), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5) + xlim(0,30) + scale_y_log10()
ggplot(subset(control_runs_df, variable %in% c("S1", "S2") & run == sample.int(50,1)), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5) + scale_y_log10()
ggplot(subset(expensive_control_runs_df, variable %in% c("S1", "S2") & run == sample.int(50,1)), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5) + scale_y_log10()
ggplot(subset(no_control_runs_df, variable %in% c("N", "P", "h")), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5)
ggplot(subset(control_runs_df, variable %in% c("N", "P", "h")), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5)
ggplot(subset(expensive_control_runs_df, variable %in% c("N", "P", "h")), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(alpha = 0.5)
expensive_control_runs_df %>% filter(run==randrun) %>% spread(variable, value) %>% print(n=200)
ave_no_control = no_control_runs_df %>%
group_by(time, variable) %>%
summarize(ave = mean(value))
ave_control = control_runs_df %>%
group_by(time, variable) %>%
summarize(ave = mean(value))
ave_expensive = expensive_control_runs_df %>%
group_by(time, variable) %>%
summarize(ave = mean(value))
library(noamtools)
ggplot(subset(ave_no_control, variable %in% c("N", "P", "h")), aes(x=time, y=ave, col=variable)) + geom_line(size = 1) +
theme_nr +
ylab("Number of hosts\nor units effort") +
xlab("Time (years)") +
theme(legend.position="none", axis.title=element_text(size=32), axis.text=element_text(size=26))
ggplot(subset(ave_control, variable %in% c("N", "P", "h")), aes(x=time, y=ave, col=variable)) + geom_line(size = 1) + theme_nr + ylab("Population / Control Effort") +
theme_nr +
ylab("Number of hosts\nor units effort") +
xlab("Time (years)") +
theme(legend.position="none", axis.title=element_text(size=32), axis.text=element_text(size=26))
ggplot(subset(ave_expensive, variable %in% c("N", "P", "h")), aes(x=time, y=ave, col=variable)) + geom_line(size = 1) + theme_nr + ylab("Population / Control Effort") +
theme_nr +
ylab("Number of hosts\nor units effort") +
xlab("Time (years)") +
theme(legend.position="none", axis.title=element_text(size=32), axis.text=element_text(size=26))
randrun = sample.int(50,1); print(randrun)
ggplot(subset(no_control_runs_df, variable %in% c("N", "P", "h") & run %in% randrun), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(size = 1) + theme_nr +
ylab("Population / Control Effort") +
xlab("Time") +
theme(legend.position="none")
ggplot(subset(control_runs_df, variable %in% c("N", "P", "h") & run %in% randrun), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(size = 1) +
theme_nr +
ylab("Number of Hosts\nor units effort") +
xlab("Time (years)") +
theme(legend.position="none", axis.title=element_text(size=32), axis.text=element_text(size=26))
ggplot(subset(expensive_control_runs_df, variable %in% c("N", "P", "h") & run %in% randrun), aes(x=time, y=value, col=variable, group=paste0(run,variable))) + geom_line(size = 1)+
theme_nr +
ylab("Number of Hosts\nor units effort") +
xlab("Time (years)") +
theme(legend.position="none")
#------
closeAllConnections()
parms = list(
max_i = 20,
lambda = 0.001,
lambda_ex = 0.2,
alpha = 0.1,
mu = 0.01,
r = 0.5,
d = 0.01,
K = 100,
init_pop = 100,
time_max = 40,
prevent_inf = 0,
prevent_ex = 0,
macro_timestep = 1,
micro_timestep = 0.25,
micro_relax_steps = 2,
project = FALSE,
n_sims = 500,
control_min = 0,
control_max = 1000,
v = 50,
c = 100,
progress = TRUE,
micro_record = file("micro.txt", open="w+")
# macro_record = file("macro.txt", open="w")
)
out = try(macro_state_c_runopt(macro_state_init = macro_state, parms=parms, shadow_state_init=shadow_state, time=0, control_guess_init=0))
out_df = process_run(out)
ggplot(subset(out_df, variable %in% c("N", "P", "h")), aes(x=time, y=value, col=variable)) + geom_line(size=1)
out_df %>% spread(variable, value) %>% print
control_runs <- mclapply(1:50, function(x) {
myseed = sample.int(1e6, 1)
set.seed(myseed)
out = try(macro_state_c_runopt(macro_state_init = macro_state, parms=parms, shadow_state_init=shadow_state, time=0, control_guess_init=0))
if ("try-error" %in% class(out)) out = list(out, myseed)
return(out)
})
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