如何遍历for循环中的参数
我有一个作为for循环编写的模型，其中包含我指定的许多参数：如何遍历for循环中的参数,r,for-loop,iteration,parameter-passing,R,For Loop,Iteration,Parameter Passing,我有一个作为for循环编写的模型，其中包含我指定的许多参数： ## functions needed to run the model learn <- function(prior, sensi, speci, e){ out <- ifelse(e == 1, (sensi*prior) / ((sensi*prior) + (1-speci)*(1-prior)), ((1-sensi)*prior) / (((1-sensi)*prior)
## functions needed to run the model
learn <- function(prior, sensi, speci, e){
out <- ifelse(e == 1, (sensi*prior) / ((sensi*prior) + (1-speci)*(1-prior)),
((1-sensi)*prior) / (((1-sensi)*prior) + (speci*(1-prior))))
out
}
feed <- function(vec){
prior <- 0.5
for (i in vec){
res <- learn(prior, sensi, speci, i)
prior <- res
}
return(prior)
}
## specify parameters
iterations <- 100
N <- 10
BR <- 0.66
sensi <- 0.75
speci <- 0.45
## initialize results object
res <- NULL
## loop for number of iterations
for (j in 1:iterations){
X <- as.numeric(rbinom(1, 1, BR))
if (X == 1){ # if X is 1...
agents <- c(1:N)
evidence <- vector("list", length(agents))
for (i in agents) {
n <- sample(10, 1, replace = TRUE)
evidence[[i]] <- rbinom(n, 1, sensi)
}
} else { # if X is 0...
agents <- c(1:N)
evidence <- vector("list", length(agents))
for (i in agents) {
n <- sample(10, 1, replace = TRUE)
evidence[[i]] <- rbinom(n, 1, sensi)
evidence[[i]] <- ifelse(evidence[[i]]==1, 0, 1) # flip evidence
}
}
# feed vectors of evidence through learn function
t0 <- sapply(evidence, feed)
# save dataframe
df <- data.frame("i" = j,
"ID" = c(1:N),
"E" = t0,
"X" = X,
"N" = N,
"BR" = BR,
"sensi" = sensi,
"speci" = speci)
res <- rbind(res, df)
}
如何将每行参数值传递给我的模型，并自动运行
paramspaceapply
## functions needed to run the model
learn <- function(prior, sensi, speci, e){
out <- ifelse(e == 1, (sensi*prior) / ((sensi*prior) + (1-speci)*(1-prior)),
((1-sensi)*prior) / (((1-sensi)*prior) + (speci*(1-prior))))
out
}
feed <- function(vec,sensi,speci){
prior <- 0.5
for (i in vec){
res <- learn(prior, sensi, speci, i)
prior <- res
}
return(prior)
}
runModel <- function(iterations = 100,
N = 10,
BR = 0.66,
sensi = 0.75,
speci = 0.45 ) {
## initialize results object
res <- NULL
## loop for number of iterations
for (j in 1:iterations){
X <- as.numeric(rbinom(1, 1, BR))
if (X == 1){ # if X is 1...
agents <- c(1:N)
evidence <- vector("list", length(agents))
for (i in agents) {
n <- sample(10, 1, replace = TRUE)
evidence[[i]] <- rbinom(n, 1, sensi)
}
} else { # if X is 0...
agents <- c(1:N)
evidence <- vector("list", length(agents))
for (i in agents) {
n <- sample(10, 1, replace = TRUE)
evidence[[i]] <- rbinom(n, 1, sensi)
evidence[[i]] <- ifelse(evidence[[i]]==1, 0, 1) # flip evidence
}
}
# feed vectors of evidence through learn function
#t0 <- sapply(evidence, feed)
t0 <- sapply(evidence,function(e){feed(e,sensi,speci)})
# save dataframe
df <- list("i" = iterations,
"ID" = c(1:N),
"E" = t0,
"X" = X,
"N" = N,
"BR" = BR,
"sensi" = sensi,
"speci" = speci)
res <- rbind(res, df)
}
res
}
# Define parameter space
iterations <- 100
N_vec <- c(10, 50)
BR_vec <- c(0.25, 0.50, 0.75)
sensi_vec <- c(0.45, 0.75)
speci_vec <- c(0.45, 0.75)
paramspace <- expand.grid(iterations = iterations, N = N_vec, BR = BR_vec, sensi = sensi_vec, speci = speci_vec)
# Loop over parameter space :
res <- apply(paramspace,1,function(paramset) {
iterations = paramset[1]
N = paramset[2]
BR = paramset[3]
sensi = paramset[4]
speci = paramset[5]
runModel(iterations = iterations, N = N, BR = BR , sensi = sensi, speci = speci )
})
##运行模型所需的函数
了解您还可以使用foreach
包，该包与适当的后端一起使用，提供并行化功能，以防您的任务变得更加密集。这里有一个简单的例子来了解它是如何工作的
foreach(a=1:3, b=4:6) %do% (a + b)
然后我尝试将您的代码嵌入到foreach
require(foreach)
## functions needed to run the model
learn <- function(prior, sensi, speci, e){
out <- ifelse(e == 1, (sensi*prior) / ((sensi*prior) + (1-speci)*(1-prior)),
((1-sensi)*prior) / (((1-sensi)*prior) + (speci*(1-prior))))
out
}
feed <- function(vec){
prior <- 0.5
for (i in vec){
res <- learn(prior, sensi, speci, i)
prior <- res
}
return(prior)
}
## set up for multiple parameterizations
iterations <- 100
N_vec <- c(10, 50)
BR_vec <- c(0.25, 0.50, 0.75)
sensi_vec <- c(0.45, 0.75)
speci_vec <- c(0.45, 0.75)
paramspace <- expand.grid(iterations = iterations, N = N_vec, BR = BR_vec, sensi = sensi_vec, speci = speci_vec)
res <- foreach(iterations = paramspace$iterations,
N = paramspace$N,
BR = paramspace$BR,
sensi = paramspace$sensi,
speci = paramspace$speci) %do% {
## initialize results object
res <- NULL
## loop for number of iterations
for (j in 1:iterations){
X <- as.numeric(rbinom(1, 1, BR))
if (X == 1){ # if X is 1...
agents <- c(1:N)
evidence <- vector("list", length(agents))
for (i in agents) {
n <- sample(10, 1, replace = TRUE)
evidence[[i]] <- rbinom(n, 1, sensi)
}
} else { # if X is 0...
agents <- c(1:N)
evidence <- vector("list", length(agents))
for (i in agents) {
n <- sample(10, 1, replace = TRUE)
evidence[[i]] <- rbinom(n, 1, sensi)
evidence[[i]] <- ifelse(evidence[[i]]==1, 0, 1) # flip evidence
}
}
# feed vectors of evidence through learn function
t0 <- sapply(evidence, feed)
# save dataframe
df <- data.frame("i" = j,
"ID" = c(1:N),
"E" = t0,
"X" = X,
"N" = N,
"BR" = BR,
"sensi" = sensi,
"speci" = speci)
res <- rbind(res, df)
}
res
}
需要（foreach）
##运行模型所需的函数
了解另一种方法是创建函数并使用Map（…）
。Map
的优点是，您的paramspace
不会被强制转换为矩阵，从而使所有内容都具有相同的类型（即数字、字符等）
我还做了一些其他的修改，以允许R为我们做会计。主要是：
X
现在是一个逻辑表达式，因此我们可以简化if
语句。此外，分配是一次完成的，而不是循环我们更改feed（）
函数以同时生成证据。这使我们能够
使用replicate
重复循环
learn22 10 0.66 0.75 0.45假0.43103448 0.006827641
#>3 3 10 0.66 0.75 0.45真实0.43103448 0.775671866
#>4 4 10 0.66 0.75 0.45真实0.71716957 0.431034483
#>5 5 10 0.66 0.75 0.45假0.2417679 0.016593958
#>61010.66 0.75 0.45假0.30303324 0.008992838
#>7100.66 0.75 0.45真值0.82967106 0.865405260
#>8100.66 0.75 0.45假0.43103448 0.439027817
#>9 9 10 0.66 0.75 0.45假0.57692308 0.050262167
#>101010.66 0.75 0.45假0.02178833 0.296208531
这个输出比您最初的方法要宽一点。我们总是可以重塑E#
列，但这可能最终更适合您的实际用例
最后，这里是正在运行的Map（）
：
iterations您可以将代码转换为函数，然后使用apply（paramspace，1，my_函数）
将paramspace
逐行传递到函数中。您提供的代码遇到了一些问题。请看我在原始帖子中添加的回复。我猜这是由于t0这就是它：我更新了feed函数以传递给它speci/sensi参数。仍然有一些警告，但当您单独运行RunModel（）
时（即设置初始参数时）不会出现。我猜某些参数组合会产生这些警告。循环模型运行正常，警告来自保存结果的dataframe调用：df我的意图是在结果中标记迭代。也许我的变量在某个地方混在一起了。我可以调查一下。
require(foreach)
## functions needed to run the model
learn <- function(prior, sensi, speci, e){
out <- ifelse(e == 1, (sensi*prior) / ((sensi*prior) + (1-speci)*(1-prior)),
((1-sensi)*prior) / (((1-sensi)*prior) + (speci*(1-prior))))
out
}
feed <- function(vec){
prior <- 0.5
for (i in vec){
res <- learn(prior, sensi, speci, i)
prior <- res
}
return(prior)
}
## set up for multiple parameterizations
iterations <- 100
N_vec <- c(10, 50)
BR_vec <- c(0.25, 0.50, 0.75)
sensi_vec <- c(0.45, 0.75)
speci_vec <- c(0.45, 0.75)
paramspace <- expand.grid(iterations = iterations, N = N_vec, BR = BR_vec, sensi = sensi_vec, speci = speci_vec)
res <- foreach(iterations = paramspace$iterations,
N = paramspace$N,
BR = paramspace$BR,
sensi = paramspace$sensi,
speci = paramspace$speci) %do% {
## initialize results object
res <- NULL
## loop for number of iterations
for (j in 1:iterations){
X <- as.numeric(rbinom(1, 1, BR))
if (X == 1){ # if X is 1...
agents <- c(1:N)
evidence <- vector("list", length(agents))
for (i in agents) {
n <- sample(10, 1, replace = TRUE)
evidence[[i]] <- rbinom(n, 1, sensi)
}
} else { # if X is 0...
agents <- c(1:N)
evidence <- vector("list", length(agents))
for (i in agents) {
n <- sample(10, 1, replace = TRUE)
evidence[[i]] <- rbinom(n, 1, sensi)
evidence[[i]] <- ifelse(evidence[[i]]==1, 0, 1) # flip evidence
}
}
# feed vectors of evidence through learn function
t0 <- sapply(evidence, feed)
# save dataframe
df <- data.frame("i" = j,
"ID" = c(1:N),
"E" = t0,
"X" = X,
"N" = N,
"BR" = BR,
"sensi" = sensi,
"speci" = speci)
res <- rbind(res, df)
}
res
}