我正在尝试使用嵌套数据框(https://r4ds.had.co.nz/many-models.html)方法，使用lcmm::lcmm()和purrr::pmap()来拟合多个潜在类成长曲线。
lcmm::gridsearch()，该函数使用来自k = 1模型的起始值来喂入一个k = 2+类别的模型。 gridsearch()还需要k = 2+模型的模型调用（加上另外两个参数），这些参数作为对gridsearch()中的lcmm()的调用传递。我的常规方法是使用pmap()将参数列表传递给gridsearch()，但list()会立即评估对lcmm()的模型调用，并尝试拟合模型，而不是将模型调用传递给gridsearch()（参见purrr::pmap与rlang的混乱行为; "引用"或不引用参数是Q）。NB：使用RStudio的函数查看器（F2），似乎
lcmm :: gridsearch()使用match.call()来调整带有用户定义数量的随机起始值的k = 2+模型调用，然后迭代这些值以找到首选的k = 2+解决方案。我在下面包含了一个reprex。当将对gridsearch的调用包装在pmap中时，该命令会失败，并显示“Error in mutate_impl（.data，dots）：Evaluation error：argument is of length zero。”-我认为这是因为R正在尝试评估对
lcmm（）的调用，用于k = 2+模型，但我可能是错的。如何延迟将
lcmm（）作为参数传递给pmap（）的评估？下面是Reprex：
library(lcmm)
#> Warning: package 'lcmm' was built under R version 3.5.2
#> Loading required package: survival
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)
# load lcmm example data
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$data[[1]], link = "linear"
)
}
# fit k = 1 models
data_lcmm_nest <-
data_lcmm_nest %>%
mutate(lcgm = map(data, lcmm_k1))
#> Be patient, lcmm is running ...
#> The program took 0.18 seconds
#> Be patient, lcmm is running ...
#> The program took 0.19 seconds
# this works for a single row
desired_result <-
gridsearch(
m = lcmm(Ydep2 ~ Time + I(Time^2),
mixture = ~Time,
random = ~Time, subject = "ID", ng = 2,
data = data_lcmm_nest$data[[1]], link = "linear"
),
rep = 5,
maxiter = 2,
minit = data_lcmm_nest$lcgm[[1]]
)
#> Be patient, lcmm is running ...
#> The program took 0.45 seconds
#> Be patient, lcmm is running ...
#> The program took 0.45 seconds
#> Be patient, lcmm is running ...
#> The program took 0.45 seconds
#> Be patient, lcmm is running ...
#> The program took 0.45 seconds
#> Be patient, lcmm is running ...
#> The program took 0.47 seconds
#> Be patient, lcmm is running ...
#> The program took 0.61 seconds
# this fails with Error in mutate_impl(.data, dots) :
# Evaluation error: argument is of length zero.
data_lcmm_nest %>%
mutate(lcgm_2 = pmap(
list(
m = lcmm(Ydep2 ~ Time + I(Time^2),
mixture = ~Time,
random = ~Time, subject = "ID", ng = 2,
data = data, link = "linear"
),
rep = 5,
maxiter = 2,
minit = lcgm
), gridsearch
))
#> Error in mutate_impl(.data, dots): Evaluation error: argument is of length zero.
# wrapping gridsearch in helper also fails
grid_search_helper <- function(g_rep, g_maxiter, g_minit, g_m) {
gridsearch(
m = lcmm(Ydep2 ~ Time + I(Time^2),
mixture = ~Time,
random = ~Time, subject = "ID", ng = 2,
data = g_m, link = "linear"
),
rep = g_rep,
maxiter = g_maxiter,
minit = g_minit
)
}
data_lcmm_nest %>%
mutate(lcgm_2 = pmap(
list(
5,
2,
lcgm,
data
), grid_search_helper
))
#> Error in mutate_impl(.data, dots): Evaluation error: object 'g_m' not found.
由reprex package（v0.2.1）于2019年01月24日创建