Internal Noise
data = read.csv('Recovered_Parameters/Internal/InternalNoise_Summary.csv')
converge = read.csv('Recovered_Parameters/Internal/InternalNoise_Convergence.csv')
converge$converged = "No"
converge$converged[converge$Rhat_k < 1.2 & converge$Rhat_l < 1.2 & converge$Rhat_s < 1.2] = 'Yes'
message(paste('Percent Chains Converged:', 100*dim(converge[converge$converged=='Yes',])[1] / dim(converge)[1]))
## Percent Chains Converged: 100
data$PercentVar = mapvalues(data$PercentVar,
from = c('Var_001', 'Var_01', 'Var_05',
'Var_10', 'Var_25'),
to = c('0.1%', '1%', '5%',
'10%', '25%'))
data = subset(data, PercentVar %in% c('0.1%', '1%', '5%'))
Recovered Number of ELIs: k
ggplot(data, aes(PercentVar, k_Mean, fill=PercentVar)) +
stat_summary(fun.y=mean, geom='bar') +
stat_summary(fun.data=mean_cl_boot, geom='linerange') +
geom_hline(aes(yintercept=10), linetype=2) +
xlab('Percent Variance Added') +
guides(fill=F) +
ylab('Recovered k') +
scale_y_continuous(breaks=seq(0, 12, 2)) +
theme_bw()

Recovered Rate of ELIs: \(\lambda\)
ggplot(data, aes(PercentVar, lambda_Mean, fill=PercentVar)) +
stat_summary(fun.y=mean, geom='bar') +
stat_summary(fun.data=mean_cl_boot, geom='linerange') +
geom_hline(aes(yintercept=0.5), linetype=2) +
xlab('Percent Variance Added') +
guides(fill=F) +
ylab(expression(paste('Recovered ',lambda))) +
scale_y_continuous(breaks=seq(0, 0.6, 0.1)) +
theme_bw()

Recovered Start Time: s
ggplot(data, aes(PercentVar, start_Mean, fill=PercentVar)) +
stat_summary(fun.y=mean, geom='bar') +
stat_summary(fun.data=mean_cl_boot, geom='linerange') +
geom_hline(aes(yintercept=4), linetype=2) +
xlab('Percent Variance Added') +
guides(fill=F) +
ylab('Recovered start time') +
scale_y_continuous(breaks=seq(0, 14, 2)) +
theme_bw()

External Noise
data = read.csv('Recovered_Parameters/External/ExternalNoise_Summary.csv')
converge = read.csv('Recovered_Parameters/External/ExternalNoise_Convergence.csv')
converge$converged = "No"
converge$converged[converge$Rhat_k < 1.2 & converge$Rhat_l < 1.2 & converge$Rhat_s < 1.2] = 'Yes'
message(paste('Percent Chains Converged:', 100*dim(converge[converge$converged=='Yes',])[1] / dim(converge)[1]))
## Percent Chains Converged: 100
data$PercentVar = mapvalues(data$PercentVar,
from = c('Var_001', 'Var_01', 'Var_05',
'Var_10', 'Var_25'),
to = c('0.1%', '1%', '5%',
'10%', '25%'))
Recovered Number of ELIs: k
ggplot(data, aes(PercentVar, k_Mean, fill=PercentVar)) +
stat_summary(fun.y=mean, geom='bar') +
stat_summary(fun.data=mean_cl_boot, geom='linerange') +
geom_hline(aes(yintercept=10), linetype=2) +
xlab('Percent Variance Added') +
guides(fill=F) +
ylab('Recovered k') +
scale_y_continuous(breaks=seq(0, 12, 2)) +
theme_bw()

Recovered Rate of ELIs: \(\lambda\)
ggplot(data, aes(PercentVar, lambda_Mean, fill=PercentVar)) +
stat_summary(fun.y=mean, geom='bar') +
stat_summary(fun.data=mean_cl_boot, geom='linerange') +
geom_hline(aes(yintercept=0.5), linetype=2) +
xlab('Percent Variance Added') +
guides(fill=F) +
ylab(expression(paste('Recovered ',lambda))) +
scale_y_continuous(breaks=seq(0, 0.6, 0.1)) +
theme_bw()

Recovered Start Time: s
ggplot(data, aes(PercentVar, start_Mean, fill=PercentVar)) +
stat_summary(fun.y=mean, geom='bar') +
stat_summary(fun.data=mean_cl_boot, geom='linerange') +
geom_hline(aes(yintercept=4), linetype=2) +
xlab('Percent Variance Added') +
guides(fill=F) +
ylab('Recovered start time') +
scale_y_continuous(breaks=seq(0, 14, 2)) +
theme_bw()
