我正在尝试使用嵌套数据框（https://r4ds.had.co.nz/many-models.html）方法来拟合lcmm::lcmm()和purrr::pmap()的多个潜在类增长曲线。
此过程需要使用lcmm（）将模型拟合为一个类（ k = 1 ），然后将该模型用作lcmm::gridsearch()的输入，该模型从中获取初始值 k = 1 模型可输入 k = 2 + 类模型。 gridsearch()还需要对 k = 2 + 模型的模型调用（以及其他两个参数），该模型调用是在对{{1}的调用中对lcmm()的调用}。我通常的方法是使用gridsearch()将参数列表传递给pmap()，但是gridsearch()立即评估对list()的模型调用，并尝试拟合模型而不是将模型调用传递给lcmm()（请参见confusing behavior of purrr::pmap with rlang; "to quote" or not to quote argument that is the Q）。
NB使用RStudio的函数查看器（F2），似乎gridsearch()使用lcmm::gridsearch()用用户定义的随机数调整 k = 2 + 模型调用初始值，然后遍历这些值以找到首选的 k = 2 + 解决方案。
我在下面包括了reprex。在pmap中包装对gridsearch的调用时，命令失败，并显示“ mutate_impl（.data，点）中的错误：评估错误：参数的长度为零。” -我认为这是因为R试图为 k = 2 + 模型评估对match.call()的调用，但是我可能是错的。
当作为参数传递给lcmm()时如何延迟对lcmm()的求值？
以下代表：
pmap()由reprex package（v0.2.1）于2019-01-24创建
答案 0 :(得分：0)
这不完全是我原来问题的答案，因为它不使用 purrr，但是使用 for 循环进行迭代不会有这种延迟评估问题：
library(lcmm)
#> Loading required package: survival
#> Loading required package: parallel
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(tidyr)
library(purrr)
data("data_lcmm")
# take sample
set.seed(123)
data_lcmm <-
data_lcmm %>%
sample_frac(0.1)
# NB grouping variable is needed to reproduce desired data structure
data_lcmm <-
data_lcmm %>%
mutate(group_var = sample(c(0, 1),
size = nrow(data_lcmm),
replace = TRUE
))
data_lcmm_nest <-
data_lcmm %>%
group_by(group_var) %>%
nest() %>%
mutate(data= map(data, as.data.frame))
# lcmm call from ?lcmm
lcmm_k1 <- function(df) {
lcmm(Ydep2 ~ Time + I(Time^2),
random = ~Time, subject = "ID", ng = 1,
data = data_lcmm_nest$data[[1]], link = "linear"
)
}
# fit k = 1 models
data_lcmm_nest <-
data_lcmm_nest %>%
mutate(lcgm = map(data, lcmm_k1))
#> Be patient, lcmm is running ...
#> The program took 0.19 seconds
#> Be patient, lcmm is running ...
#> The program took 0.22 seconds
# set-up output vector
results <- vector(mode = "list", length = nrow(data_lcmm_nest))
# fit models
for(i in 1:nrow(data_lcmm_nest)){
results[[i]] <- gridsearch(
m = lcmm(Ydep2 ~ Time + I(Time^2),
mixture = ~Time,
random = ~Time, subject = "ID", ng = 2,
data = data_lcmm_nest$data[[i]], link = "linear"
),
rep = 5,
maxiter = 2,
minit = data_lcmm_nest$lcgm[[i]]
)
}
#> Be patient, lcmm is running ...
#> The program took 0.56 seconds
#> Be patient, lcmm is running ...
#> The program took 0.42 seconds
#> Be patient, lcmm is running ...
#> The program took 0.47 seconds
#> Be patient, lcmm is running ...
#> The program took 0.48 seconds
#> Be patient, lcmm is running ...
#> The program took 0.52 seconds
#> Be patient, lcmm is running ...
#> The program took 0.5 seconds
#> Be patient, lcmm is running ...
#> The program took 0.33 seconds
#> Be patient, lcmm is running ...
#> The program took 0.32 seconds
#> Be patient, lcmm is running ...
#> The program took 0.39 seconds
#> Be patient, lcmm is running ...
#> The program took 0.38 seconds
#> Be patient, lcmm is running ...
#> The program took 0.37 seconds
#> Be patient, lcmm is running ...
#> The program took 0.47 seconds
data_lcmm_nest <-
data_lcmm_nest %>%
ungroup() %>%
mutate(res = results)
由 reprex package (v0.3.0) 于 2021 年 4 月 20 日创建
devtools::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
#> version R version 4.0.3 (2020-10-10)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United Kingdom.1252
#> ctype English_United Kingdom.1252
#> tz Europe/London
#> date 2021-04-20
#>
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#> package * version date lib source
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#>
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