按照我之前关于循环(click here to see it)的问题，我现在有另一个疑问：我试图在循环中运行几个响应的lmer模型。数据集与我之前的示例中使用的数据集相同（并且实际的数据集包含更多变量）。所以这就是我制作的：
library(dplyr)
library(purrr)
library(lme4)
lmer_test_list <- test2[c("IA_DWELL_TIME", "IA_MEAN_FIXATION_DURATION", "IA_FIRST_FIXATION_DURATION")] %>% colnames() %>% set_names(.,.)
Lmer_test_FN <- function(test2, na.rm = TRUE, ...){
for (i in lmer_test_list) {
# create plot for each text in paired dataset
model <- lmer(log(get(i)) ~ Modified +
Position_line +
Page +
Trial +
(1|Text) +
(1|Participant),
data = test2,
REML=FALSE) %>%
summary() %>%
print()
assign(paste(lmer_test_list[i], "_lmer", sep = ""), model) %>%
summary() %>%
print()
}
}
Lmer_test_FN(test2)
我设法让循环工作完美，唯一的问题是我的变量名称确实出现在摘要中，而打印出来的是“get（i）”，这意味着我不知道我在看哪一个。
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(get(i)) ~ Modified + Position_line + Page + Trial + (1 | Text) + (1 | Participant)
Data: test2
任何想法如何保留变量名称？另外，正如您可能已经注意到的那样，我尝试使用assign函数将我的模型保存在环境中，这也不起作用。
根据要求，我在这里添加了一个可重复的小数据集示例：
test2 <- structure(list(Modified = structure(c(2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 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, 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,
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, 1L, 1L), .Label = c("Original", "Modified"), class = "factor"),
Trial = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4), Word_length = c(4L,
8L, 9L, 3L, 7L, 3L, 4L, 8L, 2L, 4L, 4L, 6L, 2L, 1L, 5L, 3L,
3L, 7L, 2L, 1L, 6L, 2L, 1L, 4L, 3L, 3L, 4L, 3L, 9L, 4L, 6L,
4L, 5L, 2L, 6L, 7L, 2L, 1L, 6L, 3L, 4L, 1L, 4L, 2L, 5L, 3L,
9L, 7L, 3L, 8L, 2L, 6L, 7L, 2L, 6L, 3L, 6L, 2L, 2L, 6L, 4L,
3L, 1L, 5L, 4L, 4L, 2L, 7L, 4L, 3L, 3L, 9L, 2L, 4L, 3L, 4L,
3L, 4L, 3L, 4L, 2L, 4L, 8L, 4L, 3L, 11L, 1L, 3L, 3L, 9L,
7L, 2L, 1L, 4L, 5L, 4L, 4L, 3L, 5L, 7L, 6L, 6L, 2L, 4L, 2L,
1L, 4L, 4L, 8L, 9L, 3L, 7L, 3L, 6L, 8L, 3L, 4L, 2L, 4L, 3L,
6L, 2L, 3L, 5L, 3L, 3L, 7L, 2L, 1L, 6L, 2L, 1L, 4L, 3L, 3L,
9L, 4L, 6L, 4L, 5L, 3L, 6L, 7L, 2L, 1L, 6L, 3L, 4L, 3L, 4L,
2L, 5L, 3L, 9L, 7L, 3L, 8L, 2L, 6L, 7L, 2L, 6L, 3L, 6L, 3L,
3L, 6L, 4L, 3L, 1L, 5L, 4L, 4L, 2L, 7L, 4L, 3L, 3L, 9L, 2L,
4L, 3L, 4L, 3L, 4L, 3L, 4L, 2L, 4L, 8L, 4L, 4L, 11L, 3L,
5L, 3L, 9L, 7L, 3L, 1L, 4L), Page = c(1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 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, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 5,
5, 5, 5, 5, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2), Position_line = c(1, 2, 3, 4, 5, 4, 3, 2, 1,
1, 2, 3, 4, 5, 6, 7, 8, 7, 6, 5, 4, 3, 2, 1, 1, 2, 1, 1,
2, 3, 4, 5, 6, 5, 4, 3, 2, 1, 1, 2, 3, 4, 4, 3, 1, 2, 3,
4, 5, 5, 4, 3, 2, 1, 1, 2, 3, 6, 6, 5, 4, 3, 2, 1, 1, 2,
3, 4, 5, 6, 7, 6, 5, 4, 3, 2, 1, 1, 2, 3, 4, 5, 6, 6, 5,
4, 3, 2, 1, 1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 1, 1, 1, 2, 3,
3, 2, 1, 1, 2, 3, 4, 4, 3, 2, 1, 1, 2, 3, 4, 5, 6, 7, 7,
6, 5, 4, 3, 2, 1, 1, 2, 3, 2, 1, 1, 3, 4, 5, 5, 4, 3, 2,
1, 1, 2, 3, 4, 4, 3, 2, 1, 2, 3, 4, 5, 5, 4, 3, 2, 1, 1,
2, 3, 4, 5, 6, 5, 4, 3, 2, 1, 1, 2, 3, 4, 5, 6, 7, 6, 5,
4, 3, 2, 1, 1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 1, 1, 3, 4, 5,
6, 6), Text = structure(c(6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("1",
"2", "3", "4", "5", "6", "7", "8"), class = "factor"), Participant = 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, 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, 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, 1L, 58L, 58L, 58L, 58L, 58L,
58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L,
58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L,
58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L,
58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L,
58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L,
58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L,
58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L,
58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L, 58L
), .Label = c("101", "102", "103", "104", "105", "106", "107",
"108", "109", "110", "111", "113", "114", "115", "116", "117",
"118", "119", "120", "121", "122", "123", "124", "125", "126",
"127", "128", "129", "130", "201", "202", "203", "204", "205",
"206", "207", "208", "209", "210", "211", "212", "213", "214",
"215", "216", "217", "218", "219", "220", "221", "222", "224",
"225", "226", "227", "228", "229", "230"), class = "factor"),
IA_LABEL = c("Dear", "Clarissa", "exclaimed", "Mrs", "Hilbery",
"you", "look", "to-night", "so", "like", "your", "mother",
"as", "I", "first", "saw", "her", "walking", "in", "a", "garden",
"in", "a", "grey", "hat", "she", "said", "And", "Clarissas",
"eyes", "filled", "with", "tears", "My", "mother", "walking",
"in", "a", "garden", "But", "alas", "I", "must", "go", "There",
"was", "Professor", "Brierly", "who", "lectured", "on", "Milton",
"talking", "to", "little", "Jim", "Hutton", "He", "is", "unable",
"even", "for", "a", "party", "like", "this", "to", "compass",
"both", "tie", "and", "waistcoat", "or", "make", "his", "hair",
"lie", "flat", "and", "even", "at", "this", "distance", "they",
"are", "quarrelling", "I", "can", "see", "Professor", "Brierly",
"is", "a", "very", "queer", "fish", "with", "all", "those",
"degrees", "People", "seemed", "to", "come", "in", "a", "rush",
"Dear", "Clarissa", "exclaimed", "Mrs", "Hilbery", "She",
"looked", "to-night", "she", "said", "so", "like", "her",
"mother", "as", "she", "first", "saw", "her", "walking",
"in", "a", "garden", "in", "a", "grey", "hat", "And", "Clarissas",
"eyes", "filled", "with", "tears", "Her", "mother", "walking",
"in", "a", "garden", "But", "alas", "she", "must", "go",
"there", "was", "Professor", "Brierly", "who", "lectured",
"on", "Milton", "talking", "to", "little", "Jim", "Hutton",
"who", "was", "unable", "even", "for", "a", "party", "like",
"this", "to", "compass", "both", "tie", "and", "waistcoat",
"or", "make", "his", "hair", "lie", "flat", "and", "even",
"at", "this", "distance", "they", "were", "quarrelling",
"she", "could", "see", "Professor", "Brierly", "was", "a",
"very"), Line_on_page = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 7, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5,
5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7,
7, 7, 7, 7, 7, 7, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3),
IA_MEAN_FIXATION_DURATION = c(NaN, 241, 254.5, NaN, 281,
NaN, 463, 487, NaN, 315, 248, NaN, NaN, 293, 270.666666666667,
NaN, 271, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 504, 264, NaN,
NaN, 236, 342, 209, NaN, 196, NaN, 148, 294, NaN, NaN, NaN,
150, 387, NaN, 213, NaN, 479, 204, 280.666666666667, 295.5,
225, 291, NaN, 321, 341, NaN, 269.5, NaN, 348, NaN, NaN,
NaN, 598, 298, NaN, 105, 250, 190, NaN, 459, 333, NaN, NaN,
233, NaN, 285, NaN, 319, NaN, 224, 165, 272.5, NaN, NaN,
NaN, 179, NaN, NaN, NaN, NaN, NaN, 320, 332, NaN, 304, 305,
338, NaN, 359, 335, NaN, 220, 246.5, 205, NaN, 264, 203,
NaN, 337.666666666667, 144, 232.5, 209.5, 179, 191, NaN,
216, 311.666666666667, 162, 299.666666666667, 179, 498.5,
235.666666666667, 246, 199, 228.5, 261, 249, 284.5, 150.333333333333,
188, 143, 181.666666666667, 171.666666666667, NaN, 204, NaN,
NaN, 177.5, NaN, 192, 156, NaN, 229, 223, NaN, NaN, NaN,
348, NaN, 219, NaN, 150, 210, 273, 175, 230.5, 565, NaN,
181, NaN, 201, 199, NaN, 291.666666666667, NaN, 201, 205.5,
156, 253, 178, NaN, 494, 215, 533, 229, 228, 188.333333333333,
238.666666666667, 181.5, NaN, 237.666666666667, NaN, 245,
184, 252, NaN, NaN, 153, 221, NaN, 287.5, 156, NaN, 186,
264, 268, 470.5, NaN, 228.5, 166, 231, NaN, 284), IA_DWELL_TIME = c(NA,
241, 509, NA, 281, NA, 463, 487, NA, 315, 248, NA, NA, 293,
812, NA, 271, NA, NA, NA, NA, NA, NA, NA, 504, 264, NA, NA,
236, 342, 209, NA, 196, NA, 148, 294, NA, NA, NA, 150, 387,
NA, 213, NA, 479, 204, 842, 591, 225, 291, NA, 321, 341,
NA, 539, NA, 696, NA, NA, NA, 598, 298, NA, 105, 250, 190,
NA, 459, 333, NA, NA, 233, NA, 285, NA, 319, NA, 224, 165,
545, NA, NA, NA, 179, NA, NA, NA, NA, NA, 320, 332, NA, 304,
305, 338, NA, 359, 335, NA, 220, 493, 205, NA, 264, 203,
NA, 1013, 144, 465, 419, 179, 715, NA, 1277, 1323, 162, 1539,
179, 997, 707, 492, 199, 457, 951, 249, 569, 843, 188, 143,
952, 515, NA, 408, NA, NA, 355, NA, 384, 156, NA, 229, 223,
NA, NA, NA, 348, NA, 219, NA, 150, 210, 819, 175, 461, 565,
NA, 362, NA, 402, 398, NA, 875, NA, 201, 411, 156, 253, 534,
NA, 988, 215, 533, 458, 456, 565, 716, 363, NA, 713, NA,
245, 184, 252, NA, NA, 153, 221, NA, 575, 156, NA, 186, 264,
268, 941, NA, 457, 166, 231, NA, 852), IA_FIRST_FIXATION_DURATION = c(NA,
241, 258, NA, 281, NA, 463, 487, NA, 315, 248, NA, NA, 293,
175, NA, 271, NA, NA, NA, NA, NA, NA, NA, 504, 264, NA, NA,
236, 342, 209, NA, 196, NA, 148, 294, NA, NA, NA, 150, 387,
NA, 213, NA, 479, 204, 191, 345, 225, 291, NA, 321, 341,
NA, 274, NA, 425, NA, NA, NA, 598, 298, NA, 105, 250, 190,
NA, 459, 333, NA, NA, 233, NA, 285, NA, 319, NA, 224, 165,
241, NA, NA, NA, 179, NA, NA, NA, NA, NA, 320, 332, NA, 304,
305, 338, NA, 359, 335, NA, 220, 293, 205, NA, 264, 203,
NA, 527, 144, 225, 224, 179, 163, NA, 181, 183, 162, 292,
179, 600, 253, 194, 199, 209, 449, 249, 346, 143, 188, 143,
171, 164, NA, 236, NA, NA, 194, NA, 184, 156, NA, 229, 223,
NA, NA, NA, 348, NA, 219, NA, 150, 210, 145, 175, 204, 565,
NA, 203, NA, 225, 196, NA, 435, NA, 201, 226, 156, 253, 166,
NA, 493, 215, 533, 263, 215, 183, 332, 185, NA, 178, NA,
245, 184, 252, NA, NA, 153, 221, NA, 418, 156, NA, 186, 264,
268, 373, NA, 273, 166, 231, NA, 216)), row.names = c(NA,
-201L), groups = structure(list(Text = structure(c(5L, 6L, 6L
), .Label = c("1", "2", "3", "4", "5", "6", "7", "8"), class = "factor"),
Modified = structure(c(1L, 1L, 2L), .Label = c("Original",
"Modified"), class = "factor"), .rows = list(101:107, 108:201,
1:100)), row.names = c(NA, -3L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"))
为了在公式中得到正确的名称，你应该避免使用get()并实际构建一个合适的公式对象。您可以使用bquote()将符号名称（从字符创建）插入表达式中。这里我们更改函数以使用lapply返回模型列表
Lmer_test_FN <- function(test2, na.rm = TRUE, ...){
lapply(lmer_test_list, function(i) {
model_formula <- bquote(log(.(as.name(i))) ~ Modified +
Position_line +
Page +
Trial +
(1|Text) +
(1|Participant))
lmer(model_formula, data = test2, REML=FALSE)
})
}
models <- Lmer_test_FN(test2)
然后，如果要查看模型的摘要，只需将摘要应用于该列表即可
lapply(models, summary)
在全局名称空间中的R中创建一组包含名称中数据的变量通常不是一个好主意。如果您使用命名列表，一切都会更容易。您可以使用索引来提取个人模型
models[[1]]
models$IA_DWELL_TIME