使用 "Expand.Grid" 和 "mapply" 计算函数

Question

我正在使用 R 编程语言。我正在尝试在我定义的两个网格中包含的点上评估我定义的函数。

首先，我为这个问题创建了一些示例数据：

#load library
library(dplyr)
library(data.table)


# create some data for this example
set.seed(123)
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)

然后，我定义了两个网格：

#grid_1 - does not work for some reason
random_1 <- seq(80,100,5)
random_2 <- seq(random_1,120,5)
random_3 <- seq(85,120,5)
random_4 <- seq(random_3,120,5)
split_1 =  seq(0,1,0.5)
split_2 =  seq(0,1,0.5)
split_3 =  seq(0,1,0.5)
DF_2 <- expand.grid(random_1 , random_2, random_3, random_4, split_1, split_2, split_3)


#grid_2
random_1 <- seq(80,100,5)
random_2 <- seq(85,120,5)
random_3 <- seq(85,120,5)
random_4 <- seq(90,120,5)
split_1 =  seq(0,1,0.5)
split_2 =  seq(0,1,0.5)
split_3 =  seq(0,1,0.5)
DF_1 <- expand.grid(random_1 , random_2, random_3, random_4, split_1, split_2, split_3)

接下来，我定义了我要在这两个网格中包含的点上计算的函数（“grid_function”）：

#### define function
results_table <- data.frame()

grid_function <- function(random_1, random_2, random_3, random_4, split_1, split_2, split_3) {
    

    
    #bin data according to random criteria
    train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c")))
    
    train_data$cat = as.factor(train_data$cat)
    
    #new splits
    a_table = train_data %>%
        filter(cat == "a") %>%
        select(a1, b1, c1, cat)
    
    b_table = train_data %>%
        filter(cat == "b") %>%
        select(a1, b1, c1, cat)
    
    c_table = train_data %>%
        filter(cat == "c") %>%
        select(a1, b1, c1, cat)
    

    #calculate random quantile ("quant") for each bin
    
    table_a = data.frame(a_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_1)))
    
    table_b = data.frame(b_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_2)))
    
    table_c = data.frame(c_table%>% group_by(cat) %>%
                             mutate(quant = quantile(c1, prob = split_3)))
    
    
    
    
    #create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
    table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
    table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
    table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
    
    #group all tables
    
    final_table = rbind(table_a, table_b, table_c)
    
    #create a table: for each bin, calculate the average of "diff"
    final_table_2 = data.frame(final_table %>%
                                   group_by(cat) %>%
                                   summarize(
                                       mean = mean(diff)
                                   ))
    
    #add "total mean" to this table
    final_table_2 = data.frame(final_table_2 %>% add_row(cat = "total", mean = mean(final_table$diff)))
    
    #format this table: add the random criteria to this table for reference
    final_table_2$random_1 = random_1
    
    final_table_2$random_2 = random_2
    
    final_table_2$random_3 = random_3
    
    final_table_2$random_4 = random_4
    
    final_table_2$split_1 = split_1
    
    final_table_2$split_2 = split_2
    
    final_table_2$split_3 = split_3
    
    final_table_2$iteration_number = i
    
    
    results_table <- rbind(results_table, final_table_2)
    
    final_results = dcast(setDT(results_table), iteration_number + random_1 + random_2 + random_3 + random_4 + split_1 + split_2 + split_3 ~ cat, value.var = 'mean')
    
}

问题： 现在，我正在尝试计算“DF_1”和“DF_2”中包含的每个点的“grid_function” ”。我发现了两个相关的 Whosebug 帖子，其中提出了类似的问题，但提供的答案似乎对我不起作用。

方法一：

group_by(DF_2, random_1, random_2, random_3, random_4, split_1, split_2, split_3) %>% dplyr::mutate(z = grid_function(random_1, random_2, random_3, random_4, split_1, split_2, split_3))

Error: Must group by variables found in `.data`.
* Column `random_1` is not found.
* Column `random_2` is not found.
* Column `random_3` is not found.
* Column `random_4` is not found.
* Column `split_1` is not found.
* Column `split_2` is not found.
* Column `split_3` is not found.
Run `rlang::last_error()` to see where the error occurred.

方法二：apply function using expand.grid in R

 do.call(mapply, c(function(random_1, random_2, random_3, random_4, split_1, split_2, split_3)) , unname(DF_2)))

Error: unexpected ')' in "do.call(mapply, c(function(random_1, random_2, random_3, random_4, split_1, split_2, split_3))"

有人可以告诉我如何解决这个问题吗？

Answer 1

我认为 grid_function 中存在错误。当我尝试手动触发它时它确实会产生错误：

> unlist(DF_1[1,])
random_1 random_2 random_3 random_4  split_1  split_2  split_3 
      80       85       85       90        0        0        0 
> grid_function(80,85,85,90,0,0,0)
`summarise()` ungrouping output (override with `.groups` argument)
Error in grid_function(80, 85, 85, 90, 0, 0, 0) : 
  object 'i' not found

或者我用错了。为了测试目的，我制作了自己的简单网格函数。它生成中位数。

grid_function2 <- function(random_1 , random_2, random_3, random_4, split_1, split_2, split_3){
 return(median(c(random_1 , random_2, random_3, random_4, split_1, split_2, split_3))) 
}

为了在网格上应用该函数，您应该将参数名称定义为 colnames:

# DF_1 is the expanded grid from your question
colnames(DF_1) <- c("random_1" , "random_2", "random_3",
                    "random_4", "split_1", "split_2", "split_3")

然后你可以在其上应用一个带有命名参数的函数:

resultdf1 <- apply(DF_1,1, # 1 means rows
                   FUN=function(x){
                     do.call(
                       # Call Function grid_function2 with the arguments in
                       # a list
                       grid_function2,
                       # force list type for the arguments
                       as.list(
                         # make the row to a named vector
                         unlist(x)
                         )
                       )
                     }
                   )

使用 "Expand.Grid" 和 "mapply" 计算函数

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