R 中多个 glmer 的 imap：命名列表

Question

我有这个功能：

mweFitModelsGLMER <- function(longData,
                            strModelName = "ID Rand Model",
                            strFormula = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)",
                            ...)
{
  output = list() # Initialise output
  Ptn <- unique(longData$Ptn)
  
  Model <- longData %>%
    lme4::glmer(formula = stats::as.formula(strFormula),
                ...)
  #Do a few other things like prepare summary table etc.
  
  output$Models <-
    # list(
    tibble(
      Protein = Ptn,
      Formula = strFormula,
      `Model Name` = strModelName,
       Model = list(Model),
       )
  #)
  return(output)

}

其主要目的是拟合混合模型，做一些其他事情，然后输出一个大的小块。这本身与帖子末尾的数据配合得很好：

mweFitModelsGLMER(allData, family = "binomial", na.action = na.pass)

现在我尝试创建一个辅助函数，它可以接受公式规范的命名列表，将其传递给上面的函数并返回输出：

mweMultipleModelsGLMER <- function(longData,
                                   namLstFormula = list(Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)",
                                                        Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"),
                                   ...)
{
  purrr::imap(
    namLstFormula,
    ~ mweFitModelsGLMER(
      longData = longData,
      strModelName = paste(.y),
      strFormula = .x,
      ...
    )
  )
}

然后像这样运行它：

mweMultipleModelsGLMER(allData, 
                       namLstFormula = 
                                      list(Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)",
                                           Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"), 
                       family = "binomial")

R 对此做出回应

map2()
中的错误：ℹ在索引：1中。ℹ名称：Model1。错误造成的在
lme4::glmer()
：！ “控制”不是一个列表；使用 glmerControl()

好吧，那我就在通话中明确说明..

mweMultipleModelsGLMER(
  allData,
  namLstFormula = list(Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)",
                       Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"),
  family = "binomial",
  control = lme4::glmerControl(optimizer = "bobyqa")
)

R 回复我：

map2()
中的错误：ℹ在索引：1中。ℹ名称：Model1。错误造成的在
lme4::glmer()
：！ “控制”不是一个列表；使用 glmerControl()

Backtrace:
     ▆
  1. ├─global mweMultipleModelsGLMER(...)
  2. │ └─purrr::imap(...)
  3. │   └─purrr::map2(.x, vec_index(.x), .f, ...)
  4. │     └─purrr:::map2_("list", .x, .y, .f, ..., .progress = .progress)
  5. │       ├─purrr:::with_indexed_errors(...)
  6. │       │ └─base::withCallingHandlers(...)
  7. │       ├─purrr:::call_with_cleanup(...)
  8. │       └─.f(.x[[i]], .y[[i]], ...)
  9. │         └─global mweFitModelsGLMER(...)
 10. │           └─longData %>% ...
 11. └─lme4::glmer(., formula = stats::as.formula(strFormula), ...)
 12.   └─base::stop("'control' is not a list; use glmerControl()")

作为参考，我确实提供了帮助（“glmer”）

glmer(公式，数据 = NULL，族 = 高斯 , 控制 = glmerControl() , 开始 = NULL ，详细 = 0L , nAGQ = 1L 、子集、权重、na.action、偏移量、对比 = NULL , 必须启动, etastart ，devFunOnly = FALSE）

我错过了什么？

数据如下：

library(tidyverse)

allData <- structure(list(phMeta_UID = c("Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", 
"Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", 
"Pat 1 BRS", "Pat 1 SHF", "Pat 1 SHF", "Pat 1 SHF", "Pat 1 SHF", 
"Pat 1 SHF", "Pat 1 SHF", "Pat 1 SHF", "Pat 2 BRS", "Pat 2 BRS", 
"Pat 2 BRS", "Pat 2 SHF", "Pat 2 SHF", "Pat 2 SHF", "Pat 2 SHF", 
"Pat 2 SHF", "Pat 2 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", 
"Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", 
"Pat 1 SHF", "Pat 2 BRS", "Pat 2 BRS", "Pat 2 BRS", "Pat 2 BRS", 
"Pat 2 BRS", "Pat 2 BRS", "Pat 2 SHF"), phMeta_Time = c(0, 0.5, 
1, 2, 3, 4, 6, 8, 12, 0, 0.5, 2, 3, 4, 8, 12, 0, 0.5, 2, 0, 0.5, 
2, 3, 4, 10, 3, 4, 8, 9, 12, 0, 0.5, 2, 1, 1, 3, 4, 6, 8, 12, 
1), phMeta_Batch = c(1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 
1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 
1, 2, 1, 1, 2, 2), phMeta_Site = c("BRS", "BRS", "BRS", "BRS", 
"BRS", "BRS", "BRS", "BRS", "BRS", "SHF", "SHF", "SHF", "SHF", 
"SHF", "SHF", "SHF", "BRS", "BRS", "BRS", "SHF", "SHF", "SHF", 
"SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", 
"SHF", "SHF", "SHF", "BRS", "BRS", "BRS", "BRS", "BRS", "BRS", 
"SHF"), phMeta_Toxicity = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L), levels = c("N", "Y"), class = "factor"), phMeta_Patient = c(4, 
4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 2, 2, 2, 
2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, 5, 5, 5, 5, 5, 5, 2), phMeta_SiteXPatient = c(1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, 2, 2, 2, 2, 2, 2, 2), phMeta_Phlebotomy = c(NA, 
NA, NA, NA, NA, NA, NA, NA, NA, "Venous", "Capillary", "Venous", 
NA, "Venous", "Capillary", "Venous", NA, NA, NA, "Venous", "Capillary", 
"Capillary", "Capillary", "Capillary", "Venous", "Venous", "Venous", 
"Venous", NA, "Venous", "Venous", "Capillary", "Venous", "Venous", 
NA, NA, NA, NA, NA, NA, "Capillary"), value = structure(c(0.821944197243299, 
-0.0198715543825022, 0.631293040769747, 0.0849008002934989, 0.0887812578740912, 
1.05453893286552, -2.12824809442977, 0.276304235154362, 0.859985670512456, 
-1.04724028808727, -0.00411159277206202, 1.67249503748148, -0.54509175851945, 
-1.46818604842327, -0.498407201908304, 0.51962029081445, -1.08569307903582, 
-0.270959349353233, -0.211864905388811, 0.158115347225517, 0.0227322978830837, 
0.852349233070034, -0.401612245382643, -2.98257656282869, -0.191542564781942, 
-1.45732401444245, 0.494605681417659, 0.464925863604591, 0.856056504259303, 
1.63695935481179, -1.33574175565861, 0.42694933523218, -0.0213328145592944, 
-0.966697791972374, -0.634006734239892, 0.128202810199108, 0.960861331383678, 
-0.258051551124902, 1.25488311517846, 1.08015428255721, 1.18190128745978
), dim = c(41L, 1L)), Ptn = c("phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", 
"phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand"
)), row.names = c(NA, -41L), class = c("tbl_df", "tbl", "data.frame"
))

编辑1：

如果我忽略列表名称，该功能将起作用：

fFitMultipleModelsGLMER2 <- function(nestlistData,
                                    strPredictorROC = "phMeta_Toxicity",
                                    LstFormula = c("phMeta_Toxicity ~ 1 + (1 | phMeta_UID)", 
                                                      "phMeta_Toxicity ~ value + (1 | phMeta_UID)"),
                                    ...)
{
  purrr::map(
    namLstFormula,
    ~fFitModelsGLMER(
      longData = nestlistData,
      strPredictor = strPredictorROC,
      strFormula = .x,
      ...
    )
  )
}

Answer 1

问题在于使用

purrr

风格的匿名函数，即使用

formula

和

...

。作为一个意想不到的副作用，您不是通过

...

传递给“外部”函数的可选参数，而是通过

 第二次将

.x

和

.y

 参数传递给“内部”

mweFitModelsGLMER

...

。

这可以从下面的示例中看出，我只是在

print

调用中

...

imap

的内容：

library(purrr)

foo <- function(longData,
                namLstFormula = list(
                  Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)",
                  Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"
                ),
                ...) {
  purrr::imap(
    namLstFormula,
    ~ print(list(...))
  )
}

baz <- foo(allData,
  namLstFormula =
    list(
      Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)",
      Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"
    ),
  family = "binomial"
)
#> [[1]]
#> [1] "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)"
#> 
#> [[2]]
#> [1] "Model1"
#> 
#> [[1]]
#> [1] "phMeta_Toxicity ~ value + (1 | phMeta_UID)"
#> 
#> [[2]]
#> [1] "Model2"

要解决您的问题，请使用

function(.x, .y)

或

\(.x, .y)

而不是公式：

mweMultipleModelsGLMER <- function(longData,
                                   namLstFormula = list(
                                     Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)",
                                     Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"
                                   ),
                                   ...) {
  purrr::imap(
    namLstFormula,
    \(.x, .y)
    mweFitModelsGLMER(
      longData = longData,
      strModelName = .y,
      strFormula = .x,
      ...
    )
  )
}

mweMultipleModelsGLMER(allData,
  namLstFormula =
    list(
      Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)",
      Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"
    ),
  family = "binomial"
)
#> $Model1
#> $Model1$Models
#> # A tibble: 1 × 4
#>   Protein           Formula                              `Model Name` Model     
#>   <chr>             <chr>                                <chr>        <list>    
#> 1 phOSI_ICOS ligand phMeta_Toxicity ~ 1 + (1 | phMeta_U… Model1       <glmerMod>
#> 
#> 
#> $Model2
#> $Model2$Models
#> # A tibble: 1 × 4
#>   Protein           Formula                              `Model Name` Model     
#>   <chr>             <chr>                                <chr>        <list>    
#> 1 phOSI_ICOS ligand phMeta_Toxicity ~ value + (1 | phMe… Model2       <glmerMod>

R 中多个 glmer 的 imap：命名列表

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编辑1：

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R 中多个 glmer 的 imap：命名列表

问题描述 投票：0回答：1

编辑1：

1个回答

最新问题

问题描述投票：0回答：1