R 如何循环参数值、运行函数和保存结果

我将模拟写入函数，以便手动设置参数值，并使用这些参数值多次运行模拟。为了了解不同设置如何影响模拟结果，我一直在手动更改参数值、运行模拟并保存输出。我已经多次这样做，并将输出数据绑定在一起进行分析/可视化，但如果我可以自动化此过程，则会更加方便

如何循环参数值、运行模拟并将所有结果保存在单个数据帧中

---

以下是我的代码的一种感觉：

#### load libraries ####

library(plyr)
library(igraph)

#### set parameters N and StDv ####

N <- 10 

StDv <- 0.1

#### my model to be simulated, written as a function ####
myModel <- function(){

  #generate small world network, netSim, for the agents
  netSim <- sample_smallworld(dim = 1, nei = 1, size = N, p = 0.1) 

  #retrieve an adjacency matrix from net
  adjMatrix <- as.matrix(as_adjacency_matrix(netSim, names = TRUE, edges = FALSE)) 

  #create dataframe with numbered agents and assigned prior  
  data <- data.frame("agent" = c(1:N),
                     "t0" = rnorm(N, mean = 0.5, sd = StDv))

  #simulate communication and in the network for 5 rounds

  #round 1
  data$t1 <- with(data, ifelse(rowSums(adjMatrix) > 0,  
                               0.75 * t0 + (1-0.75) * (adjMatrix %*% t0 / rowSums(adjMatrix)), 
                               t0)) 

  #round 2
  data$t2 <- with(data, ifelse(rowSums(adjMatrix) > 0,  
                               0.75 * t1 + (1-0.75) * (adjMatrix %*% t1 / rowSums(adjMatrix)), 
                               t1)) 

  #round 3
  data$t3 <- with(data, ifelse(rowSums(adjMatrix) > 0,  
                               0.75 * t2 + (1-0.75) * (adjMatrix %*% t2 / rowSums(adjMatrix)), 
                               t2)) 

  #round 4
  data$t4 <- with(data, ifelse(rowSums(adjMatrix) > 0, 
                               0.75 * t3 + (1-0.75) * (adjMatrix %*% t3 / rowSums(adjMatrix)), 
                               t3)) 

  #round 5
  data$t5 <- with(data, ifelse(rowSums(adjMatrix) > 0,  
                               0.75 * t4 + (1-0.75) * (adjMatrix %*% t4 / rowSums(adjMatrix)), 
                               t4)) 


  #calculate measures of interest
  colResponses <- colMeans(data[2:7])
  colErrorSq <- (colResponses-1)^2
  variance <- as.vector(sapply(data[2:7], function(i)
    var(i)))
  data2 <- data[2:7] 
  data2 <- (data2-1)^2
  avgIndErrSq <- colMeans(data2)
  rm(data2)

  #bind together output 
  Output <- data.frame("N" = N,
                       "StDv" = StDv,
                       "Time" = c("t0", "t1", "t2", "t3", "t4", "t5"),
                       "Collective.Response" = colResponses,
                       "Collective.Error.Squared" = colErrorSq,
                       "Variance" = variance,
                       "Avg.Ind.Error.Squared" = avgIndErrSq)

}

#### Simulate my model by running the function 100 times and saving the results as "myResults" ####
myResults <- ldply(1:100, function(i) data.frame(Iteration = i, myModel())) 

#####加载库####
图书馆（plyr）
图书馆（igraph）
####设置参数N和StDv####
N我建议使用for循环遍历您的选项。嵌套for循环应循环遍历这些向量的所有值和组合

#Loop through all N values in vector
for (i in 1:length(N_values)) {

    N = N_values[i]

    #Loop through all StDev values in vector for each 
    #iteration of all N values
    for (j in 1:length(StDv_values) {

        StDv = StDv_values[j]

        MyModel <- insert your model here... etc...
    }
}

您正在创建一个数据帧，但我看不到它与任何东西绑定

对于所有数据的汇编，我建议如下：

1） 在for循环外部初始化空变量
2） 为每次迭代将所有新的Output data.frame值插入CompiledDF变量

CompiledDF = NULL

#Loop through all N values in vector
for (i in 1:length(N_values)) {

    N = N_values[i]

    #Loop through all StDev values in vector for each 
    #iteration of all N values
    for (j in 1:length(StDv_values) {

        StDv = StDv_values[j]

        MyModel <- insert your model here... etc...


        Output <- data.frame(etc...
                                   )

        CompiledDF <- rbind(CompiledDF, Output)


    }
}

CompiledDF=NULL
#循环遍历向量中的所有N个值
对于（1中的i：长度（N_值））{
N=N_值[i]
#循环遍历每个节点的向量中的所有StDev值
#所有N值的迭代
对于（j in 1：长度（StDv_值）{
StDv=StDv_值[j]
MyModel我建议使用for循环遍历您的选项。嵌套的for循环应该遍历这些向量的所有值和组合

#Loop through all N values in vector
for (i in 1:length(N_values)) {

    N = N_values[i]

    #Loop through all StDev values in vector for each 
    #iteration of all N values
    for (j in 1:length(StDv_values) {

        StDv = StDv_values[j]

        MyModel <- insert your model here... etc...
    }
}

您正在创建一个数据帧，但我看不到它与任何东西绑定

对于所有数据的汇编，我建议如下：

1） 在for循环外部初始化空变量
2） 为每次迭代将所有新的Output data.frame值插入CompiledDF变量

CompiledDF = NULL

#Loop through all N values in vector
for (i in 1:length(N_values)) {

    N = N_values[i]

    #Loop through all StDev values in vector for each 
    #iteration of all N values
    for (j in 1:length(StDv_values) {

        StDv = StDv_values[j]

        MyModel <- insert your model here... etc...


        Output <- data.frame(etc...
                                   )

        CompiledDF <- rbind(CompiledDF, Output)


    }
}

CompiledDF=NULL
#循环遍历向量中的所有N个值
对于（1中的i：长度（N_值））{
N=N_值[i]
#循环遍历每个节点的向量中的所有StDev值
#所有N值的迭代
对于（j in 1：长度（StDv_值）{
StDv=StDv_值[j]
MyModel我将编写一个辅助函数，用适当的值组合处理调用
MyModel
的重复细节

runAll <- function(N_vec, StDv_vec){
  f <- function(N, StDv){
    ldply(1:100, function(i) data.frame(Iteration = i, myModel(N, StDv)))
  }
  vals <- expand.grid(N = N_vec, StDv = StDv_vec)
  res <- Map(function(.N, .StDv){f(.N, .StDv)}, vals$N, vals$StDv)
  res <- do.call(rbind, res)
  row.names(res) <- NULL
  res
}

N_values <- c(10, 20, 40, 80)
StDv_values <- c(0.05, 0.1, 0.25, 0.5)

res <- runAll(N_values, StDv_values)

dim(res)
#[1] 9600    8

我将编写一个辅助函数来处理使用适当的值组合调用
myModel
的重复细节

runAll <- function(N_vec, StDv_vec){
  f <- function(N, StDv){
    ldply(1:100, function(i) data.frame(Iteration = i, myModel(N, StDv)))
  }
  vals <- expand.grid(N = N_vec, StDv = StDv_vec)
  res <- Map(function(.N, .StDv){f(.N, .StDv)}, vals$N, vals$StDv)
  res <- do.call(rbind, res)
  row.names(res) <- NULL
  res
}

N_values <- c(10, 20, 40, 80)
StDv_values <- c(0.05, 0.1, 0.25, 0.5)

res <- runAll(N_values, StDv_values)

dim(res)
#[1] 9600    8