New idea to visualize the distribution of PSA sampled parameters

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Issue 类型
功能
描述清晰度
基本清楚
活跃度
停滞
技术栈
r
领域
data-visualization

调研方向

Start by checking how dampack currently exposes plotting and visualization features, then use the supplied R example with df_psa_random to understand the proposed grouping and density-ridge plots. Done would mean a documented way to visualize PSA sampled parameter distributions grouped as probabilities, utilities, costs, and other; the issue does not specify an entry point, tests, or expected interface.

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描述

feature

When the DARTH framework is uses, it would be nice to show the sampled distributions in an illustrative way.

Example to get started with. But we can improve this

Melt the dataset

df_melt <- melt(df_psa_random, variable.name = "Parameter")

Create a new column to classify parameters by prefix

df_melt <- df_melt %>%
mutate(Group = case_when(
str_starts(Parameter, "p_") ~ "Probabilities",
str_starts(Parameter, "u_") ~ "Utilities",
str_starts(Parameter, "c_") ~ "Costs",
TRUE ~ "Other"
))

Optionally check the groups

table(df_melt$Group)

df_probs <- df_melt %>% filter(Group == "Probabilities")

Probabilities:
ggplot(df_probs, aes(x = value, y = Parameter)) +
geom_density_ridges(scale = 1.5, rel_min_height = 0.01) +
theme_bw(base_size = 14) +
ggtitle("Probabilities")

df_utils <- df_melt %>% filter(Group == "Utilities")

ggplot(df_utils, aes(x = value, y = Parameter)) +
geom_density_ridges(scale = 1.5, rel_min_height = 0.01) +
theme_bw(base_size = 14) +
ggtitle("Utilities")

df_costs <- df_melt %>% filter(Group == "Costs")

ggplot(df_costs, aes(x = value, y = Parameter)) +
geom_density_ridges(scale = 1.5, rel_min_height = 0.01) +
theme_bw(base_size = 14) +
ggtitle("Costs")

df_other <- df_melt %>% filter(Group == "Other")

ggplot(df_other, aes(x = value, y = Parameter)) +
geom_density_ridges(scale = 1.5, rel_min_height = 0.01) +
theme_bw(base_size = 14) +
ggtitle("Other")

主要语言
R
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环境准备

从这里开始

  1. 先读完整个 Issue，再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库，在一个分支上完成修改。
  4. 提交 Pull Request，并在描述里引用这个 Issue 编号。

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