如何以tidyverse风格为deSolve包组织一系列模拟(场景)?

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英文:

How to organize a series of simulations (scenarios) for the deSolve package in tidyverse style?

问题

以下是翻译好的部分:

先前的帖子中,原帖作者提出了一个问题,询问如何使用deSolve包来组织一系列具有不同状态和参数的模拟场景。已经提供了一个解决方案,但我想知道是否可以以更适合管道友好风格的方式来实现,但不使用嵌套数据框。目标是将其用作使用清晰的表格数据结构制作最终ggplot的教学示例。

方法1:经典的“基本-R”风格

到目前为止,我通常使用apply函数的列表方法。然后,可以将生成的数据框列表适应于deSolve::plot方法:

library("deSolve")

model <- function(t, y, p) {
  with(as.list(c(y, p)), {
    dN <- r * N * (1 - N/K)
    list(c(dN))
  })
}

times <- 0:10
y0    <- c(N = 0.5)         # 状态变量
p     <- c(r = 0.2, K = 1)  # 模型参数

## 运行单个模拟
out <- ode(y0, times, model, p)
plot(out)

## 创建包含一些状态和参数组合的数据框
scenarios <- expand.grid(N = seq(0.5, 1.5, 0.2), K = 1, r = seq(0.2, 1, 0.2))

## 用于运行数据框的单行模拟的函数
## 注意 scenarios 和 scenario 之间的差异(复数/单数)
simulate <- function(scenario) {
  ## 拆分场景设置为初始状态(y0)和参数(p)
  y0 <- scenario["N"]
  p  <- scenario[c("r", "K")]
  ode(y0, times, model, p)
}

## MARGIN = 1:每行是一个场景
## simplify = FALSE:函数应返回一个列表
outputs <- apply(scenarios, MARGIN = 1, FUN = simulate, simplify = FALSE)

## plot.deSolve方法可与列表作为第二个参数一起使用
plot(out, outputs)

方法2:迈向管道

基于这个示例,我创建了一个名为simulate_inout的函数,以一种ggplot兼容的方式返回单个场景的输入和输出。然后,应该在管道中为所有场景(所有行)调用它。

以下是示例:

## 保留输入和输出的simulate版本
simulate_inout <- function(scenario) {
  scenario <- unlist(scenario)
  ## 拆分场景设置为初始状态(y0)和参数(p)
  y0 <- scenario["N"]
  p  <- scenario[c("r", "K")]
  
  ## 集成模型
  output <- ode(y0, times, model, p)
  
  ## 复制输入的行
  input <- do.call("rbind", replicate(length(times), 
    scenario, simplify = FALSE))
  
  ## 返回包含输入和输出的数据框
  cbind(input, output)
}

## 单个场景
simulate_inout(scenarios[1,])

simulate_all <- function(scenarios) {
  ## 遍历所有行
  ret <- NULL
  for (i in 1:nrow(scenarios)) {
    ret <- rbind(ret, simulate_inout(scenarios[i,]))
  }
  data.frame(ret)
}

## 使用ggplot绘图
library("ggplot2")
scenarios |> simulate_all() |> ggplot(aes(time, N.1)) + 
  geom_path() + facet_grid(r ~ N)

问题

我希望以一致的tidyverse风格简化此代码,并摆脱simulate_all中的for循环以及其他特定技巧,如do.call

英文:

In a previous post the original poster asked a question on how to organize a series of scenarios with varying states and parameters for simulations with the deSolve package. A solution was given, but I wonder if it can be made easier in a pipeline-friendly style, but without the use of nested data frames. The goal is to use it as a teaching example using clear tabular data structures that can be fit to a final ggplot.

Approach 1: Classical "base-R" style

Until now, I usually use a list approach with the apply-function. The resulting list of data frames can then be fit to the deSolve::plot-method:

library(&quot;deSolve&quot;)

model &lt;- function(t, y, p) {
  with(as.list(c(y, p)), {
    dN &lt;- r * N * (1 - N/K)
    list(c(dN))
  })
}

times &lt;- 0:10
y0    &lt;- c(N = 0.5)         # state variables
p     &lt;- c(r = 0.2, K = 1)  # model parameters

## run a single simulation
out &lt;- ode(y0, times, model, p)
plot(out)

## create a data frame with some combinations of states and parameters
scenarios &lt;- expand.grid(N = seq(0.5, 1.5, 0.2), K = 1, r = seq(0.2, 1, 0.2))

## a function to run a simulation for a single line of the data frame
## note difference between scenarios and scenario (plural/singular)
simulate &lt;- function(scenario) {
  ## split scenario settings to initial states (y0) and parameters (p)
  y0 &lt;- scenario[&quot;N&quot;]
  p  &lt;- scenario[c(&quot;r&quot;, &quot;K&quot;)]
  ode(y0, times, model, p)
}

## MARGIN = 1: each row is a scenario
## simplify = FALSE: function should return a list
outputs &lt;- apply(scenarios, MARGIN = 1, FUN = simulate, simplify = FALSE)

## the plot.deSolve method works with lists as second argument
plot(out, outputs)

Approach 2: A step towards a pipeline

Based on this example, I created a function simulate_inout that returns both, inputs and outputs in a ggplot-compatible way for a single scenario. This should then be called for all scenarios (all rows) in a pipeline.

The following works:

## version of simulate that preserves inputs and outputs
simulate_inout &lt;- function(scenario) {
  scenario &lt;- unlist(scenario)
  ## split scenario settings to initial states (y0) and parameters (p)
  y0 &lt;- scenario[&quot;N&quot;]
  p  &lt;- scenario[c(&quot;r&quot;, &quot;K&quot;)]
  
  ## integrate the model
  output &lt;- ode(y0, times, model, p)
  
  ## replicate rows of inputs
  input &lt;- do.call(&quot;rbind&quot;, replicate(length(times), 
    scenario, simplify = FALSE))
  
  ## return a data frame with inputs and outputs
  cbind(input, output)
}

## a single scenario
simulate_inout(scenarios[1,])

simulate_all &lt;- function(scenarios) {
  ## iterate over all rows
  ret &lt;- NULL
  for (i in 1:nrow(scenarios)) {
    ret &lt;- rbind(ret, simulate_inout(scenarios[i,]))
  }
  data.frame(ret)
}

## plot with ggplot
library(&quot;ggplot2&quot;)
scenarios |&gt; simulate_all() |&gt; ggplot(aes(time, N.1)) + 
  geom_path() + facet_grid(r ~ N)

Question

I would like to streamline this code in consistent tidyverse style and to get of the for-loop in simulate_all and other specific tricks like do.call.

答案1

得分: 1

You could use purrr::map_dfr to loop over the rows of your scenarios df. Requires some rewriting of your functions such that it takes the parameters itself as arguments. Additionally, I simplified your code a bit.

EDIT Replaced the superseded pmap_dfr by pmap(...) |&gt; list_rbind(). Use ... to pass the arguments to simulate_inout.

library(deSolve)
library(ggplot2)
library(purrr)

model &lt;- function(t, y, p) {
  N &lt;- y
  r &lt;- p[[1]]
  K &lt;- p[[2]]
  
  dN &lt;- r * N * (1 - N / K)
  
  list(dN)
}
times &lt;- 0:10

simulate_inout &lt;- function(...) {
  args &lt;- list(...)
  
  y0 &lt;- args[[&quot;N&quot;]]
  p &lt;- args[c(&quot;r&quot;, &quot;K&quot;)]
  
  output &lt;- ode(y0, times, model, p)
  
  data.frame(args, output)
}

scenarios &lt;- expand.grid(
  N = seq(0.5, 1.5, 0.2),
  K = 1,
  r = seq(0.2, 1, 0.2)
)

scenarios |&gt;
  purrr::pmap(simulate_inout) |&gt;
  list_rbind() |&gt; # or dplyr::bind_rows()
  ggplot(aes(time, X1)) +
  geom_path() +
  facet_grid(r ~ N)

如何以tidyverse风格为deSolve包组织一系列模拟(场景)?<!-- -->

英文:

You could use purrr::map_dfr to loop over the rows of your scenarios df. Requires some rewriting of your functions such that it takes the parameters itself as arguments. Additionally I simplified your code a bit.

EDIT Replaced the superseded pmap_dfr by pmap(...) |&gt; list_rbind(). Use ... to pass the arguments to simulate_inout.

library(deSolve)
library(ggplot2)
library(purrr)

model &lt;- function(t, y, p) {
  N &lt;- y
  r &lt;- p[[1]]
  K &lt;- p[[2]]
  
  dN &lt;- r * N * (1 - N / K)
  
  list(dN)
}
times &lt;- 0:10

simulate_inout &lt;- function(...) {
  args &lt;- list(...)
  
  y0 &lt;- args[[&quot;N&quot;]]
  p &lt;- args[c(&quot;r&quot;, &quot;K&quot;)]
  
  output &lt;- ode(y0, times, model, p)
  
  data.frame(args, output)
}

scenarios &lt;- expand.grid(
  N = seq(0.5, 1.5, 0.2),
  K = 1,
  r = seq(0.2, 1, 0.2)
)

scenarios |&gt;
  purrr::pmap(simulate_inout) |&gt;
  list_rbind() |&gt; # or dplyr::bind_rows()
  ggplot(aes(time, X1)) +
  geom_path() +
  facet_grid(r ~ N)

如何以tidyverse风格为deSolve包组织一系列模拟(场景)?<!-- -->

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  • 本文由 发表于 2023年8月5日 00:35:48
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