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comparison
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# plot marginal posterior distributions of the two models side by side.
# Model 1 is the one with the extra parameter 'phi'
# Model 2 is the one with 'phi' = 1.
# ==================== epsilon1 ========================= #
epsilon1_M1 <- posterior_sample_table$epsilon1
epsilon1_M2 <- posterior_sample_table_model2$epsilon1
M.epsilon1.df <- data.frame(epsilon1 = c(epsilon1_M1, epsilon1_M2), type = as.factor(rep(c('M1', 'M2'), each = length(epsilon1_M1))))
p <- ggplot(M.epsilon1.df, aes(x=epsilon1, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = 1/15)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic()
p + stat_function(fun = function(x) {dnorm(x, 1, 100)}, aes(color = 'prior')) +
geom_vline(aes(xintercept=1),
linetype="solid",color = 'black') +
lims(x = c(0,5)) + xlab('epsilon1') +
theme(text = element_text(size = 20))
# ==================== epsilon2 ========================= #
epsilon2_M1 <- posterior_sample_table$epsilon2
epsilon2_M2 <- posterior_sample_table_model2$epsilon2
M.epsilon2.df <- data.frame(epsilon2 = c(epsilon2_M1, epsilon2_M2), type = as.factor(rep(c('M1', 'M2'), each = length(epsilon2_M1))))
p <- ggplot(M.epsilon2.df, aes(x=epsilon2, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = 1/15)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic()
p + stat_function(fun = function(x) {dnorm(x, 1, 100)}, aes(color = 'prior')) +
geom_vline(aes(xintercept=1),
linetype="solid",color = 'black') +
lims(x = c(0,10)) + xlab('epsilon2') +
theme(text = element_text(size = 20))
# ==================== phi ========================= #
ggplot(posterior_sample_table, aes(x = phi)) +
geom_histogram(breaks=seq(0,0.5,0.5/100),aes(y = ..density.., color = 'posterior')) +
stat_function(fun = function(x) {dnorm(x, 1,1e+5)}, aes(color = 'prior')) +
lims(x = c(0,0.5)) +
theme_classic() +
scale_colour_manual(name="Distribution",
values=c(posterior="black", prior="purple")) +
theme(text = element_text(size = 20))
# ==================== beta_WT ========================= #
beta_WT_M1 <- posterior_sample_table$beta
beta_WT_M2 <- posterior_sample_table_model2$beta
M.beta_WT.df <- data.frame(beta_WT = log10(c(beta_WT_M1, beta_WT_M2)), type = as.factor(rep(c('M1', 'M2'), each = length(beta_WT_M1))))
p <- ggplot(M.beta_WT.df, aes(x=beta_WT, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = 1/15)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic()
p + stat_function(fun = function(x) {dnorm(x, -6, 4)}, aes(color = 'prior')) +
lims(x = c(-10,0)) + xlab('log10(beta_WT)') +
theme(text = element_text(size = 20))
# ==================== p ========================= #
p_M1 <- posterior_sample_table$p
p_M2 <- posterior_sample_table_model2$p
M.p.df <- data.frame(p = log10(c(p_M1, p_M2)), type = as.factor(rep(c('M1', 'M2'), each = length(p_M1))))
p <- ggplot(M.p.df, aes(x=p, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = 1/15)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic()
p + stat_function(fun = function(x) {dnorm(x, -2, 4)}, aes(color = 'prior')) +
lims(x = c(-6,6)) + xlab('log10(p)') +
theme(text = element_text(size = 20))
# ==================== delta_I ========================= #
delta_I_M1 <- posterior_sample_table$delta_I
delta_I_M2 <- posterior_sample_table_model2$delta_I
M.delta_I.df <- data.frame(delta_I = c(delta_I_M1, delta_I_M2), type = as.factor(rep(c('M1', 'M2'), each = length(delta_I_M1))))
p <- ggplot(M.delta_I.df, aes(x=delta_I, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = 1/15)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic()
p + stat_function(fun = function(x) {dlnorm(x, log(0.89), 1)}, aes(color = 'prior')) +
lims(x = c(0,10)) + xlab('delta_I') +
theme(text = element_text(size = 20))
# ==================== delta_V ========================= #
delta_V_M1 <- posterior_sample_table$delta_V
delta_V_M2 <- posterior_sample_table_model2$delta_V
M.delta_V.df <- data.frame(delta_V = c(delta_V_M1, delta_V_M2), type = as.factor(rep(c('M1', 'M2'), each = length(delta_V_M1))))
p <- ggplot(M.delta_V.df, aes(x=delta_V, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = 0.5)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic()
p + stat_function(fun = function(x) {dlnorm(x, log(28.4), 1)}, aes(color = 'prior')) +
lims(x = c(0,40)) + xlab('delta_V') +
theme(text = element_text(size = 20))
# ==================== delta_M ========================= #
delta_M_M1 <- posterior_sample_table$delta_M
delta_M_M2 <- posterior_sample_table_model2$delta_M
M.delta_M.df <- data.frame(delta_M = c(delta_M_M1, delta_M_M2), type = as.factor(rep(c('M1', 'M2'), each = length(delta_M_M1))))
p <- ggplot(M.delta_M.df, aes(x=delta_M, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = 1e-4)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic()
p + stat_function(fun = function(x) {dlnorm(x, log(4.4e-3), 1)}, aes(color = 'prior')) +
lims(x = c(1e-5,3e-2)) + xlab('delta_M') +
theme(text = element_text(size = 20))
# ==================== kappa_M ========================= #
kappa_M_M1 <- posterior_sample_table$kappa_M
kappa_M_M2 <- posterior_sample_table_model2$kappa_M
M.kappa_M.df <- data.frame(kappa_M = log10(c(kappa_M_M1, kappa_M_M2)), type = as.factor(rep(c('M1', 'M2'), each = length(kappa_M_M1))))
p <- ggplot(M.kappa_M.df, aes(x=kappa_M, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = 1/10)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic()
p + stat_function(fun = function(x) {dnorm(x, -6, 4)}, aes(color = 'prior')) +
lims(x = c(-20,0)) + xlab('log10(kappa_M)') +
theme(text = element_text(size = 20))
# ================== R0 ==============#
R0_WT.M1 <- posterior_sample_table$beta * posterior_sample_table$p * 1e+7 / (posterior_sample_table$delta_I * (posterior_sample_table$delta_V + posterior_sample_table$kappa_M * 330 / posterior_sample_table$delta_M))
R0_WT.M2 <- posterior_sample_table_model2$beta * posterior_sample_table_model2$p * 1e+7 / (posterior_sample_table_model2$delta_I * (posterior_sample_table_model2$delta_V + posterior_sample_table_model2$kappa_M * 330 / posterior_sample_table_model2$delta_M))
R0.WT.df <- data.frame(R0 = c(R0_WT.M1, R0_WT.M2), type = as.factor(rep(c('M1', 'M2'), each = length(R0_WT.M1))))
ggplot(R0.WT.df, aes(x=R0, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = .1)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic() +
lims(x = c(0,50)) + xlab('R0_WT') +
theme(text = element_text(size = 20))
R0_KO.M1 <- posterior_sample_table$epsilon1 * posterior_sample_table$beta * posterior_sample_table$p * 1e+7 / (posterior_sample_table$delta_I * (posterior_sample_table$delta_V + posterior_sample_table$kappa_M * 330 / posterior_sample_table$delta_M))
R0_KO.M2 <- posterior_sample_table_model2$epsilon1 * posterior_sample_table_model2$beta * posterior_sample_table_model2$p * 1e+7 / (posterior_sample_table_model2$delta_I * (posterior_sample_table_model2$delta_V + posterior_sample_table_model2$kappa_M * 330 / posterior_sample_table_model2$delta_M))
R0.KO.df <- data.frame(R0 = c(R0_KO.M1, R0_KO.M2), type = as.factor(rep(c('M1', 'M2'), each = length(R0_KO.M1))))
ggplot(R0.KO.df, aes(x=R0, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = .1)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic() +
lims(x = c(0,50)) + xlab('R0_KO') +
theme(text = element_text(size = 20))
library(plyr)
median.WT <- ddply(R0.WT.df,"type", summarise, grp.median = quantile(R0, 0.5))
head(median.WT)
median.KO <- ddply(R0.KO.df,"type", summarise, grp.median = quantile(R0, 0.5))
head(median.KO)
# ================== initial growth rate r ================== #
T0 <- 1e+7
s <- 330
epsilon1.M1 <- posterior_sample_table$epsilon1
p.M1 <- posterior_sample_table$p
beta.M1 <- posterior_sample_table$beta
delta_I.M1 <- posterior_sample_table$delta_I
delta_V.M1 <- posterior_sample_table$delta_V
kappa_M.M1 <- posterior_sample_table$kappa_M
delta_M.M1 <- posterior_sample_table$delta_M # M1 parameters
epsilon1.M2 <- posterior_sample_table_model2$epsilon1
p.M2 <- posterior_sample_table_model2$p
beta.M2 <- posterior_sample_table_model2$beta
delta_I.M2 <- posterior_sample_table_model2$delta_I
delta_V.M2 <- posterior_sample_table_model2$delta_V
kappa_M.M2 <- posterior_sample_table_model2$kappa_M
delta_M.M2 <- posterior_sample_table_model2$delta_M # M2 parameters
r.WT.M1 <- 1/2 * (- (delta_I.M1 + delta_V.M1 + kappa_M.M1 * s/delta_M.M1) + sqrt((delta_I.M1 + delta_V.M1 + kappa_M.M1 * s/delta_M.M1)^2 - 4*delta_I.M1*(kappa_M.M1*s/delta_M.M1 + delta_V.M1)*(1 - R0_WT.M1)))
r.KO.M1 <- 1/2 * (- (delta_I.M1 + delta_V.M1 + kappa_M.M1 * s/delta_M.M1) + sqrt((delta_I.M1 + delta_V.M1 + kappa_M.M1 * s/delta_M.M1)^2 - 4*delta_I.M1*(kappa_M.M1*s/delta_M.M1 + delta_V.M1)*(1 - R0_KO.M1)))
r.WT.M2 <- 1/2 * (- (delta_I.M2 + delta_V.M2 + kappa_M.M2 * s/delta_M.M2) + sqrt((delta_I.M2 + delta_V.M2 + kappa_M.M2 * s/delta_M.M2)^2 - 4*delta_I.M2*(kappa_M.M2*s/delta_M.M2 + delta_V.M2)*(1 - R0_WT.M2)))
r.KO.M2 <- 1/2 * (- (delta_I.M2 + delta_V.M2 + kappa_M.M2 * s/delta_M.M2) + sqrt((delta_I.M2 + delta_V.M2 + kappa_M.M2 * s/delta_M.M2)^2 - 4*delta_I.M2*(kappa_M.M2*s/delta_M.M2 + delta_V.M2)*(1 - R0_KO.M2)))
r.WT.df <- data.frame(r = c(r.WT.M1, r.WT.M2), type = as.factor(rep(c('M1', 'M2'), each = length(r.WT.M1))))
ggplot(r.WT.df, aes(x=r, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = .1)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic() +
lims(x = c(0,10)) + xlab('r_WT') +
theme(text = element_text(size = 20))
r.KO.df <- data.frame(r = c(r.KO.M1, r.KO.M2), type = as.factor(rep(c('M1', 'M2'), each = length(r.WT.M1))))
ggplot(r.KO.df, aes(x=r, color=type)) +
geom_histogram(aes(y = ..density..),fill = "white", position="dodge", alpha = 1, binwidth = .1)+
theme(legend.position="top")+
#scale_color_grey()+scale_fill_grey() +
theme_classic() +
lims(x = c(0,10)) + xlab('r_KO') +
theme(text = element_text(size = 20))
# ================= comparison model simulations =========== #
Within_host_model = function(t, y, theta){
dydt1 = -theta[1] * theta[2] * y[1] * y[3];
dydt2 = theta[1] * theta[2] * y[1] * y[3] - theta[3] * y[2];
dydt3 = theta[4] * y[2] - theta[5] * y[3] - theta[6] * y[4] * y[3];
dydt4 = 330 + theta[7] * theta[9] * y[2] - theta[8] * y[4];
list(c(dydt1, dydt2, dydt3, dydt4))
}
n_iteration <- 1250
t_ppc <- seq(0, 10, 0.1)
# ============ vectors for viral load and macrophage simulation of M1 and M2
V_ppc_WT.M1 <- matrix(, nrow = n_iteration, ncol = length(t_ppc))
V_ppc_KO.M1 <- matrix(, nrow = n_iteration, ncol = length(t_ppc)) # viral load
M_ppc_WT.M1 <- matrix(, nrow = n_iteration, ncol = length(t_ppc))
M_ppc_KO.M1 <- matrix(, nrow = n_iteration, ncol = length(t_ppc)) # macrophage
I_ppc_WT.M1 <- matrix(, nrow = n_iteration, ncol = length(t_ppc))
I_ppc_KO.M1 <- matrix(, nrow = n_iteration, ncol = length(t_ppc)) # infected cells
V_ppc_WT.M2 <- matrix(, nrow = n_iteration, ncol = length(t_ppc))
V_ppc_KO.M2 <- matrix(, nrow = n_iteration, ncol = length(t_ppc)) # viral load
M_ppc_WT.M2 <- matrix(, nrow = n_iteration, ncol = length(t_ppc))
M_ppc_KO.M2 <- matrix(, nrow = n_iteration, ncol = length(t_ppc)) # macrophage
I_ppc_WT.M2 <- matrix(, nrow = n_iteration, ncol = length(t_ppc))
I_ppc_KO.M2 <- matrix(, nrow = n_iteration, ncol = length(t_ppc)) # infected cells
# ============ vector for 95 PI of viral load and macrophage simulation of M1 #
lower_95PI_WT.M1 <- t_ppc
median_95PI_WT.M1 <- t_ppc
upper_95PI_WT.M1 <- t_ppc
lower_95PI_KO.M1 <- t_ppc
median_95PI_KO.M1 <- t_ppc
upper_95PI_KO.M1 <- t_ppc # V, M1
lower_95PI_WT_M.M1 <- t_ppc
median_95PI_WT_M.M1 <- t_ppc
upper_95PI_WT_M.M1 <- t_ppc
lower_95PI_KO_M.M1 <- t_ppc
median_95PI_KO_M.M1 <- t_ppc
upper_95PI_KO_M.M1 <- t_ppc # M, M1
lower_95PI_WT_I.M1 <- t_ppc
median_95PI_WT_I.M1 <- t_ppc
upper_95PI_WT_I.M1 <- t_ppc
lower_95PI_KO_I.M1 <- t_ppc
median_95PI_KO_I.M1 <- t_ppc
upper_95PI_KO_I.M1 <- t_ppc # I, M1
# ============ vector for 95 PI of viral load and macrophage simulation of M2 #
lower_95PI_WT.M2 <- t_ppc
median_95PI_WT.M2 <- t_ppc
upper_95PI_WT.M2 <- t_ppc
lower_95PI_KO.M2 <- t_ppc
median_95PI_KO.M2 <- t_ppc
upper_95PI_KO.M2 <- t_ppc # V, M2
lower_95PI_WT_M.M2 <- t_ppc
median_95PI_WT_M.M2 <- t_ppc
upper_95PI_WT_M.M2 <- t_ppc
lower_95PI_KO_M.M2 <- t_ppc
median_95PI_KO_M.M2 <- t_ppc
upper_95PI_KO_M.M2 <- t_ppc # M, M2
lower_95PI_WT_I.M2 <- t_ppc
median_95PI_WT_I.M2 <- t_ppc
upper_95PI_WT_I.M2 <- t_ppc
lower_95PI_KO_I.M2 <- t_ppc
median_95PI_KO_I.M2 <- t_ppc
upper_95PI_KO_I.M2 <- t_ppc # I, M2
#======== ODE solver for viral load and macrophage in M1
for (i in 1:n_iteration){
y_init.M1 = c(data_combined_muc1_V_W$T0, data_combined_muc1_V_W$I0, data_combined_muc1_V_W$V0, 330/posterior_sample_table$delta_M[i])
y_init.M2 = c(data_combined_muc1_V_W$T0, data_combined_muc1_V_W$I0, data_combined_muc1_V_W$V0, 330/posterior_sample_table_model2$delta_M[i])
param_fit.M1 = c(1,
posterior_sample_table$beta[i],
posterior_sample_table$delta_I[i],
posterior_sample_table$p[i],
posterior_sample_table$delta_V[i],
posterior_sample_table$kappa_M[i],
1,
posterior_sample_table$delta_M[i],
posterior_sample_table$phi[i])
param_fit_hat.M1 = c(posterior_sample_table$epsilon1[i],
posterior_sample_table$beta[i],
posterior_sample_table$delta_I[i],
posterior_sample_table$p[i],
posterior_sample_table$delta_V[i],
posterior_sample_table$kappa_M[i],
posterior_sample_table$epsilon2[i],
posterior_sample_table$delta_M[i],
posterior_sample_table$phi[i])
param_fit.M2 = c(1,
posterior_sample_table_model2$beta[i],
posterior_sample_table_model2$delta_I[i],
posterior_sample_table_model2$p[i],
posterior_sample_table_model2$delta_V[i],
posterior_sample_table_model2$kappa_M[i],
1,
posterior_sample_table_model2$delta_M[i],
1)
param_fit_hat.M2 = c(posterior_sample_table_model2$epsilon1[i],
posterior_sample_table_model2$beta[i],
posterior_sample_table_model2$delta_I[i],
posterior_sample_table_model2$p[i],
posterior_sample_table_model2$delta_V[i],
posterior_sample_table_model2$kappa_M[i],
posterior_sample_table_model2$epsilon2[i],
posterior_sample_table_model2$delta_M[i],
1)
model_output_WT.M1 = ode(times = t_ppc, y = y_init.M1, func = Within_host_model, parms = param_fit.M1, method = "bdf")
model_output_KO.M1 = ode(times = t_ppc, y = y_init.M1, func = Within_host_model, parms = param_fit_hat.M1, method = "bdf")
V_ppc_WT.M1[i,] = model_output_WT.M1[,4]
M_ppc_WT.M1[i,] = model_output_WT.M1[,5]
I_ppc_WT.M1[i,] = model_output_WT.M1[,2]
V_ppc_KO.M1[i,] = model_output_KO.M1[,4]
M_ppc_KO.M1[i,] = model_output_KO.M1[,5]
I_ppc_KO.M1[i,] = model_output_KO.M1[,2]
model_output_WT.M2 = ode(times = t_ppc, y = y_init.M2, func = Within_host_model, parms = param_fit.M2, method = "bdf")
model_output_KO.M2 = ode(times = t_ppc, y = y_init.M2, func = Within_host_model, parms = param_fit_hat.M2, method = "bdf")
V_ppc_WT.M2[i,] = model_output_WT.M2[,4]
M_ppc_WT.M2[i,] = model_output_WT.M2[,5]
I_ppc_WT.M2[i,] = model_output_WT.M2[,2]
V_ppc_KO.M2[i,] = model_output_KO.M2[,4]
M_ppc_KO.M2[i,] = model_output_KO.M2[,5]
I_ppc_KO.M2[i,] = model_output_KO.M2[,2]
}
# ======= selecting 95% PI ============= #
for (i in 1:length(t_ppc)){
temp_WT.M1 = unname(quantile(V_ppc_WT.M1[,i], probs = c(0.025, 0.5, 0.975)))
temp_KO.M1 = unname(quantile(V_ppc_KO.M1[,i], probs = c(0.025, 0.5, 0.975)))
temp_WT_M.M1 = unname(quantile(M_ppc_WT.M1[,i], probs = c(0.025, 0.5, 0.975)))
temp_KO_M.M1 = unname(quantile(M_ppc_KO.M1[,i], probs = c(0.025, 0.5, 0.975)))
temp_WT_I.M1 = unname(quantile(I_ppc_WT.M1[,i], probs = c(0.025, 0.5, 0.975)))
temp_KO_I.M1 = unname(quantile(I_ppc_KO.M1[,i], probs = c(0.025, 0.5, 0.975)))
lower_95PI_WT.M1[i] = temp_WT.M1[1]
median_95PI_WT.M1[i] = temp_WT.M1[2]
upper_95PI_WT.M1[i] = temp_WT.M1[3]
lower_95PI_KO.M1[i] = temp_KO.M1[1]
median_95PI_KO.M1[i] = temp_KO.M1[2]
upper_95PI_KO.M1[i] = temp_KO.M1[3]
lower_95PI_WT_M.M1[i] = temp_WT_M.M1[1]
median_95PI_WT_M.M1[i] = temp_WT_M.M1[2]
upper_95PI_WT_M.M1[i] = temp_WT_M.M1[3]
lower_95PI_KO_M.M1[i] = temp_KO_M.M1[1]
median_95PI_KO_M.M1[i] = temp_KO_M.M1[2]
upper_95PI_KO_M.M1[i] = temp_KO_M.M1[3]
lower_95PI_WT_I.M1[i] = temp_WT_I.M1[1]
median_95PI_WT_I.M1[i] = temp_WT_I.M1[2]
upper_95PI_WT_I.M1[i] = temp_WT_I.M1[3]
lower_95PI_KO_I.M1[i] = temp_KO_I.M1[1]
median_95PI_KO_I.M1[i] = temp_KO_I.M1[2]
upper_95PI_KO_I.M1[i] = temp_KO_I.M1[3]
#============== M2 ==================#
temp_WT.M2 = unname(quantile(V_ppc_WT.M2[,i], probs = c(0.025, 0.5, 0.975)))
temp_KO.M2 = unname(quantile(V_ppc_KO.M2[,i], probs = c(0.025, 0.5, 0.975)))
temp_WT_M.M2 = unname(quantile(M_ppc_WT.M2[,i], probs = c(0.025, 0.5, 0.975)))
temp_KO_M.M2 = unname(quantile(M_ppc_KO.M2[,i], probs = c(0.025, 0.5, 0.975)))
temp_WT_I.M2 = unname(quantile(I_ppc_WT.M2[,i], probs = c(0.025, 0.5, 0.975)))
temp_KO_I.M2 = unname(quantile(I_ppc_KO.M2[,i], probs = c(0.025, 0.5, 0.975)))
lower_95PI_WT.M2[i] = temp_WT.M2[1]
median_95PI_WT.M2[i] = temp_WT.M2[2]
upper_95PI_WT.M2[i] = temp_WT.M2[3]
lower_95PI_KO.M2[i] = temp_KO.M2[1]
median_95PI_KO.M2[i] = temp_KO.M2[2]
upper_95PI_KO.M2[i] = temp_KO.M2[3]
lower_95PI_WT_M.M2[i] = temp_WT_M.M2[1]
median_95PI_WT_M.M2[i] = temp_WT_M.M2[2]
upper_95PI_WT_M.M2[i] = temp_WT_M.M2[3]
lower_95PI_KO_M.M2[i] = temp_KO_M.M2[1]
median_95PI_KO_M.M2[i] = temp_KO_M.M2[2]
upper_95PI_KO_M.M2[i] = temp_KO_M.M2[3]
lower_95PI_WT_I.M2[i] = temp_WT_I.M2[1]
median_95PI_WT_I.M2[i] = temp_WT_I.M2[2]
upper_95PI_WT_I.M2[i] = temp_WT_I.M2[3]
lower_95PI_KO_I.M2[i] = temp_KO_I.M2[1]
median_95PI_KO_I.M2[i] = temp_KO_I.M2[2]
upper_95PI_KO_I.M2[i] = temp_KO_I.M2[3]
}
# ======== plot ============#
viral.load.data <- data.frame(time = rep(c(1,2,3,5,7), each = 5),
V_WT = log10(exp(data_combined_muc1_V_W$log_viral_load_data_WT)),
V_KO = log10(exp(data_combined_muc1_V_W$log_viral_load_data_KO))) # viral load data
fit_plot_viral_load_WT <- data.frame(time = c(t_ppc, t_ppc),
lower.M = c(lower_95PI_WT.M1, lower_95PI_WT.M2),
median.M = c(median_95PI_WT.M1, median_95PI_WT.M2),
upper.M = c(upper_95PI_WT.M1, upper_95PI_WT.M2),
Model = as.factor(rep(c('M1','M2'), each = length(t_ppc))))
fit_plot_viral_load_KO <- data.frame(time = c(t_ppc, t_ppc),
lower.M = c(lower_95PI_KO.M1, lower_95PI_KO.M2),
median.M = c(median_95PI_KO.M1, median_95PI_KO.M2),
upper.M = c(upper_95PI_KO.M1, upper_95PI_KO.M2),
Model = as.factor(rep(c('M1','M2'), each = length(t_ppc))))
ggplot(fit_plot_viral_load_WT, aes(time))+
geom_point(data = viral.load.data, aes(time, V_WT), size = 3) +
geom_line(data = fit_plot_viral_load_WT, aes(time, log10(median.M), color = Model), size = 2, alpha = 0.8) +
geom_ribbon(aes(ymin = log10(lower.M), ymax = log10(upper.M), fill= Model), alpha = 0.1, na.rm = TRUE) +
theme_bw() +
theme(text = element_text(size = 20)) # compared WT viral load data between the two models
ggplot(fit_plot_viral_load_KO, aes(time))+
geom_point(data = viral.load.data, aes(time, V_KO), size = 3) +
geom_line(data = fit_plot_viral_load_KO, aes(time, log10(median.M), color = Model), size = 2, alpha = 0.8) +
geom_ribbon(aes(ymin = log10(lower.M), ymax = log10(upper.M), fill= Model), alpha = 0.1, na.rm = TRUE) +
theme_bw() +
theme(text = element_text(size = 20)) # compared KO viral load data between the two models
# ===================== macrophages ===============#
macrophage.data.WT <- data.frame(time = rep(c(1,3,5,7), c(5,5,5,4)),
M_WT = log10(exp(data_combined_muc1_V_W$Macrophage_data_WT)))
fit_plot_macrophage_WT <- data.frame(time = c(t_ppc, t_ppc),
lower.M = c(lower_95PI_WT_M.M1, lower_95PI_WT_M.M2),
median.M = c(median_95PI_WT_M.M1, median_95PI_WT_M.M2),
upper.M = c(upper_95PI_WT_M.M1, upper_95PI_WT_M.M2),
Model = as.factor(rep(c('M1','M2'), each = length(t_ppc))))
macrophage.data.KO = data.frame(time = rep(c(1,3,5,7), c(5,4,5,6)),
M_KO = log10(exp(data_combined_muc1_V_W$Macrophage_data_KO)))
fit_plot_macrophage_KO <- data.frame(time = c(t_ppc, t_ppc),
lower.M = c(lower_95PI_KO_M.M1, lower_95PI_KO_M.M2),
median.M = c(median_95PI_KO_M.M1, median_95PI_KO_M.M2),
upper.M = c(upper_95PI_KO_M.M1, upper_95PI_KO_M.M2),
Model = as.factor(rep(c('M1','M2'), each = length(t_ppc))))
ggplot(fit_plot_macrophage_WT, aes(time))+
geom_point(data = macrophage.data.WT, aes(time, M_WT), size = 3) +
geom_line(data = fit_plot_macrophage_WT, aes(time, log10(median.M), color = Model), size = 2, alpha = 0.8) +
geom_ribbon(aes(ymin = log10(lower.M), ymax = log10(upper.M), fill= Model), alpha = 0.1, na.rm = TRUE) +
theme_bw() +
theme(text = element_text(size = 20)) # compared WT macrophage data between the two models
ggplot(fit_plot_macrophage_KO, aes(time))+
geom_point(data = macrophage.data.KO, aes(time, M_KO), size = 3) +
geom_line(data = fit_plot_macrophage_KO, aes(time, log10(median.M), color = Model), size = 2, alpha = .8) +
geom_ribbon(aes(ymin = log10(lower.M), ymax = log10(upper.M), fill= Model), alpha = .1, na.rm = TRUE) +
theme_bw() +
theme(text = element_text(size = 20)) # compared KO macrophage data between the two models
# ================= infected cells or epithelial cells ==================#
fit_plot_infected_cells_WT <- data.frame(time = c(t_ppc, t_ppc),
lower.M = c(lower_95PI_WT_I.M1, lower_95PI_WT_I.M2),
T_WT = c(median_95PI_WT_I.M1, median_95PI_WT_I.M2),
upper.M = c(upper_95PI_WT_I.M1, upper_95PI_WT_I.M2),
Model = as.factor(rep(c('M1','M2'), each = length(t_ppc))))
fit_plot_infected_cells_KO <- data.frame(time = c(t_ppc, t_ppc),
lower.M = c(lower_95PI_KO_I.M1, lower_95PI_KO_I.M2),
T_KO = c(median_95PI_KO_I.M1, median_95PI_KO_I.M2),
upper.M = c(upper_95PI_KO_I.M1, upper_95PI_KO_I.M2),
Model = as.factor(rep(c('M1','M2'), each = length(t_ppc))))
ggplot(fit_plot_infected_cells_WT, aes(time))+
geom_line(data = fit_plot_infected_cells_WT, aes(time, T_WT, color = Model), size = 2, alpha = 0.8) +
geom_ribbon(aes(ymin = lower.M, ymax = upper.M, fill= Model), alpha = 0.1, na.rm = TRUE) +
theme_bw() +
theme(text = element_text(size = 20)) # compared WT viral load data between the two models
ggplot(fit_plot_infected_cells_KO, aes(time))+
geom_line(data = fit_plot_infected_cells_KO, aes(time, T_KO, color = Model), size = 2, alpha = 0.8) +
geom_ribbon(aes(ymin = lower.M, ymax = upper.M, fill= Model), alpha = 0.1, na.rm = TRUE) +
theme_bw() +
theme(text = element_text(size = 20)) # compared KO viral load data between the two models
# Bayes factor
#install.packages("bridgesampling")
library(bridgesampling)
bridge_H1 <- bridge_sampler(fit_M1)
bridge_H2 <- bridge_sampler(fit_M2)
Bayes.factor <- bf(bridge_H1, bridge_H2)
Err1 <- error_measures(bridge_H1)$percentage
Err2 <- error_measures(bridge_H2)$percentage
bayesfactor_parameters(fit_M1)