#
# LTBI screening
# N Green
#
# observed against predicted
# validation
# model sim vs regn predictions
# regression predictions
head(pred_data_30000)
head(pred_NMB_30000)
head(pred_sim_30000_wide)
pred_sim_30000_long <- melt(data = pred_sim_30000_wide,
id.vars = c("Start", "Complete", "Agree", "Effective", "policy"),
variable_name = "pred_num")
sim_NMB_30000 <-
sim_matrix %>%
subset(wtp == 30000)
sim_NMB_30000$pred_num <- unique(pred_sim_30000_long$pred_num)
NMB_pred_and_sim <-
merge(x = sim_NMB_30000, y = pred_NMB_30000,
all.x = TRUE, all.y = FALSE,
by = c("Start", "Complete", "Agree", "Effective", "policy"))
sim_meanNMB_30000 <-
sim_NMB_30000 %>%
group_by(scenario, Start, Complete, Agree, Effective, policy) %>%
summarise(mean_NMB = mean(NMB)) %>%
merge(y = pred_NMB_30000,
all.x = TRUE, all.y = FALSE,
by = c("Start", "Complete", "Agree", "Effective", "policy"))
# select required plot_data
NMB_predsim_and_sim <-
merge(x = sim_NMB_30000, y = pred_sim_30000_long,
all.x = FALSE, all.y = FALSE,
by = c("Start", "Complete", "Agree", "Effective", "policy", "pred_num"))
# plots -------------------------------------------------------------------
sp <-
subset(NMB_pred_and_sim, policy == "screened") %>%
ggplot(aes(x = pred, y = NMB, color = scenario)) +
geom_point() +
scale_color_gradientn(colours = rainbow(5)) +
ylim(12200, 12900) + xlim(12200, 12900) +
geom_abline(slope = 1) +
xlab("Metamodel expected NMB") +
ylab("Original model NMB")
sp
sp <-
subset(sim_meanNMB_3000, policy == "screened") %>%
ggplot(aes(x = pred, y = mean_NMB, color = scenario)) +
geom_point() +
scale_color_gradientn(colours = rainbow(5)) +
ylim(12200, 12900) + xlim(12200, 12900) +
geom_abline(slope = 1) +
xlab("Metamodel expected NMB") +
ylab("Original model expected NMB")
sp
sp <-
subset(NMB_predsim_and_sim, policy == "screened") %>%
ggplot(aes(x = value, y = NMB, color = scenario)) +
geom_point() +
scale_color_gradientn(colours = rainbow(5)) +
ylim(12200, 12900) + xlim(12200, 12900) +
geom_abline(slope = 1) +
xlab("Metamodel NMB") +
ylab("Original model NMB")
sp
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