更有效地进行多元回归模型（总共 8 个：2 个模型包含不同的预测变量和 4 个不同的结果）
Doing multiple regression models (8 in total: 2 models containing different predictors and on 4 different outcomes) more efficiently
对于我当前的项目，我反复指定对不同结果具有不同数量 predictors/covariates 的回归模型。现在我只是完整地写出每个模型，但我确信有一种（非常）更快的方法需要更少的代码来完成我正在做的事情。
我的示例数据是 24 名中风患者的重复测量数据集，我在其中评估了三种不同类型的康复 (Group) 对功能恢复评分（Outcome 1 至 Outcome 4).每周测量每位患者的功能能力 (Time_num)，持续 8 周：
library(tidyverse)
library(magrittr)
library(nlme)
mydata <- structure(list(Subject = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L), Age = c(60, 52.5, 57.1, 63, 65.1, 39, 59.3, 65.3,
61.4, 56.3, 46.4, 58.2, 58, 57.7, 56.6, 42.3, 52.5, 51.8, 43.2,
50.9, 56.7, 67.5, 65, 56.5, 65.5, 45.6, 56.7, 47.9, 65.5, 46.6,
68.6, 52.1, 43.1, 62.1, 62.9, 58.3, 49.6, 42.1, 59.7, 62.9, 56.2,
71.7, 60.5, 59.8, 54.3, 76.1, 56.2, 74.3, 48.7, 69.9, 59.6, 58.4,
55.9, 56.5, 33, 57.1, 63, 53.1, 51.3, 46.9, 57.2, 47, 58, 63.7,
69.8, 57.9, 62.7, 44.8, 51.5, 57, 58.1, 53.3, 57.2, 54.2, 50.2,
60.4, 61.1, 81.3, 59.6, 68.8, 49.2, 51, 53.5, 55.9, 66.7, 60.3,
59.8, 61.6, 63.8, 59.8, 55.5, 57.7, 66.3, 54.7, 56.3, 56.7, 57.7,
63.8, 53.5, 56.1, 49, 44.5, 36, 58.2, 50.8, 56.8, 47.9, 51.1,
53.2, 53.4, 59.3, 42.8, 63.6, 51.2, 49, 62.6, 44.8, 59.9, 44.7,
56, 54.3, 58.7, 62.2, 76.7, 31.4, 65.2, 52.8, 56.7, 52.4, 60.6,
54.8, 43.2, 77.6, 58.1, 49.8, 55.2, 53.6, 54.1, 72.9, 58.7, 51.9,
64.9, 56.6, 61, 71.3, 63.1, 57.4, 56.9, 53.8, 73, 58.9, 60.7,
63.8, 54.6, 74.5, 46.7, 44.2, 56.3, 66.8, 56.5, 43.6, 62.8, 55.3,
53.7, 54.9, 46.6, 51.8, 60.7, 62.9, 61.5, 61.6, 43.6, 66.8, 50.1,
51.6, 69.9, 52.2, 58.1, 62.1, 69.2, 59.1, 55.2, 47.2, 64.5, 54.2,
75.9, 52.9, 62.5, 58, 64.5, 70.7, 60.5), Sex = structure(c(1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("Male",
"Female"), class = "factor"), Group = 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, 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, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("A",
"B", "C"), class = "factor"), Time_num = c(1, 2, 3, 4, 5, 6,
7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3,
4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8,
1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5,
6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2,
3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7,
8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4,
5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1,
2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6,
7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8), First_outcome = c(45L,
45L, 45L, 45L, 80L, 80L, 80L, 90L, 20L, 25L, 25L, 25L, 30L, 35L,
30L, 50L, 50L, 50L, 55L, 70L, 70L, 75L, 90L, 90L, 25L, 25L, 35L,
40L, 60L, 60L, 70L, 80L, 100L, 100L, 100L, 100L, 100L, 100L,
100L, 100L, 20L, 20L, 30L, 50L, 50L, 60L, 85L, 95L, 30L, 35L,
35L, 40L, 50L, 60L, 75L, 85L, 30L, 35L, 45L, 50L, 55L, 65L, 65L,
70L, 40L, 55L, 60L, 70L, 80L, 85L, 90L, 90L, 65L, 65L, 70L, 70L,
80L, 80L, 80L, 80L, 30L, 30L, 40L, 45L, 65L, 85L, 85L, 85L, 25L,
35L, 35L, 35L, 40L, 45L, 45L, 45L, 45L, 45L, 80L, 80L, 80L, 80L,
80L, 80L, 15L, 15L, 10L, 10L, 10L, 20L, 20L, 20L, 35L, 35L, 35L,
45L, 45L, 45L, 50L, 50L, 40L, 40L, 40L, 55L, 55L, 55L, 60L, 65L,
20L, 20L, 30L, 30L, 30L, 30L, 30L, 30L, 35L, 35L, 35L, 40L, 40L,
40L, 40L, 40L, 35L, 35L, 35L, 40L, 40L, 40L, 45L, 45L, 45L, 65L,
65L, 65L, 80L, 85L, 95L, 100L, 45L, 65L, 70L, 90L, 90L, 95L,
95L, 100L, 25L, 30L, 30L, 35L, 40L, 40L, 40L, 40L, 25L, 25L,
30L, 30L, 30L, 30L, 35L, 40L, 15L, 35L, 35L, 35L, 40L, 50L, 65L,
65L), Second_outcome = c(3, 50, 7, 43, -23, 32, 48, 46, 32, 46,
23, 34, 46, -2, 46, 49, 45, 44, 53, 1, 61, 23, 41, 52, 25, 54,
26, -1, 22, 50, 21, 20, 70, 62, 67, 18, 55, 25, 5, 16, 43, 35,
59, 5, -5, 50, 35, 32, 25, 25, 13, 57, 42, 21, 35, 34, 38, 52,
63, 52, 44, 36, 32, 30, 26, 42, 44, 53, 39, 29, 13, 37, 41, 31,
18, 41, 40, 29, 28, 22, 6, -15, 16, 26, 0, 41, 35, 28, 35, 32,
41, 49, 16, 43, 56, 63, 14, 46, 43, 46, 36, -3, 49, 33, 49, 20,
20, 31, 27, 23, 34, 36, 39, 20, 29, 58, 45, 60, 40, 17, 77, 45,
13, 62, 43, 74, 47, 56, 13, 12, 36, 2, 40, 57, 35, 31, 28, 82,
49, 6, 10, 46, 49, 17, 55, 16, 12, -17, -7, 22, 20, -14, 21,
17, 41, 47, 25, 34, 72, 59, 26, 24, 46, 16, 35, 34, 51, 40, 25,
53, 24, 14, 66, 18, 18, 34, 29, 81, 12, 50, 55, 33, 62, 38, 24,
25, 29, 60, 71, -6, 60, 49), Third_outcome = c(87, 78, 94, 93,
78, 84, 72, 81, 82, 81, 86, 72, 80, 82, 77, 82, 79, 71, 82, 79,
86, 86, 76, 73, 80, 74, 81, 73, 81, 80, 65, 84, 73, 85, 87, 78,
77, 70, 85, 80, 77, 73, 75, 85, 67, 87, 90, 84, 71, 73, 81, 72,
74, 74, 85, 90, 75, 70, 81, 69, 81, 73, 79, 74, 76, 77, 82, 80,
87, 87, 82, 81, 76, 80, 79, 71, 81, 77, 74, 78, 73, 79, 77, 78,
94, 78, 71, 82, 81, 80, 79, 70, 68, 82, 78, 68, 66, 82, 80, 71,
73, 79, 83, 71, 80, 78, 82, 73, 86, 76, 75, 81, 84, 84, 85, 80,
83, 79, 75, 77, 82, 89, 78, 74, 79, 82, 73, 86, 77, 81, 84, 84,
73, 80, 82, 81, 81, 83, 81, 79, 84, 82, 75, 75, 80, 67, 81, 82,
82, 80, 80, 80, 76, 81, 82, 85, 86, 81, 89, 78, 84, 79, 80, 77,
85, 88, 78, 81, 82, 81, 82, 77, 74, 86, 81, 73, 80, 77, 81, 76,
83, 76, 81, 79, 76, 83, 77, 79, 71, 77, 82, 87), Fourth_outcome = c(59,
36, 53, 51, 59, 50, 56, 57, 52, 42, 60, 44, 46, 52, 54, 68, 63,
37, 51, 46, 67, 42, 63, 47, 41, 48, 51, 48, 51, 34, 35, 46, 52,
52, 44, 67, 47, 58, 57, 55, 50, 56, 36, 42, 51, 51, 42, 49, 59,
55, 44, 53, 42, 64, 75, 64, 41, 44, 39, 64, 40, 48, 51, 54, 42,
52, 35, 55, 53, 66, 34, 50, 56, 35, 32, 63, 52, 35, 63, 38, 57,
67, 35, 41, 47, 31, 55, 60, 52, 60, 44, 52, 63, 53, 48, 69, 43,
44, 40, 45, 63, 39, 48, 56, 44, 57, 56, 62, 54, 49, 47, 62, 41,
41, 59, 32, 62, 39, 64, 46, 44, 78, 68, 38, 51, 27, 57, 55, 67,
51, 44, 61, 24, 49, 62, 61, 43, 41, 54, 47, 41, 28, 40, 31, 57,
58, 36, 48, 58, 61, 67, 50, 47, 56, 56, 69, 43, 43, 58, 55, 48,
52, 46, 51, 38, 58, 44, 43, 49, 59, 31, 37, 46, 55, 45, 50, 45,
67, 48, 37, 51, 47, 66, 42, 52, 46, 61, 47, 34, 49, 58, 38)), row.names = c(NA,
-192L), class = c("tbl_df", "tbl", "data.frame"))
看起来如下：
head(mydata)
# A tibble: 6 x 9
Subject Age Sex Group Time_num First_outcome Second_outcome Third_outcome Fourth_outcome
<int> <dbl> <fct> <fct> <dbl> <int> <dbl> <dbl> <dbl>
1 1 60 Male A 1 45 3 87 59
2 1 52.5 Female A 2 45 50 78 36
3 1 57.1 Female A 3 45 7 94 53
4 1 63 Male A 4 45 43 93 51
5 1 65.1 Male A 5 80 -23 78 59
6 1 39 Female A 6 80 32 84 50
我 运行 现在的模型是每个结果有 2 个线性混合效应模型（使用 nlme::lme）：一个只包含 Group，一个另外包含 Age 和Sex。我现在的做法是：
# Outcome 1
outcome1_modelA <-
lme(fixed=First_outcome ~ 1 + Time_num*Group,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
outcome1_modelB <-
lme(fixed=First_outcome ~ 1 + Time_num*Group + Time_num*Age + Time_num*Sex,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
# Outcome 2, 3, and finally...
# Outcome 4
outcome4_modelA <-
lme(fixed=Fourth_outcome ~ 1 + Time_num*Group,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
outcome4_modelB <-
lme(fixed=Fourth_outcome ~ 1 + Time_num*Group + Time_num*Age + Time_num*Sex,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
但是看到我得到了更多的结果和更多的模型，我想学习一种方法来提高我的代码的效率。我读过有关 for 循环的内容，但似乎找不到适合我的示例。不涉及 for 循环的解决方案也将不胜感激！
创建一个函数来使用您想要更改的部分的参数来完成它 - 您将需要 as.formula() 函数
my_model_function <- function(x, y){
fixed_effects <- as.formula(paste(y, "~ 1 +",
paste("Time_num", x, sep="*", collapse=" + ")))
lme(fixed=fixed_effects,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
}
outcome1_modelA <- my_model_function(x = "Group",
y = "First_outcome")
outcome1_modelB <- my_model_function(x = c("Group", "Age", "Sex"),
y = "First_outcome")
为了使其更加自动化，您可以在 lapply() 中创建嵌套循环，这将 return 模型输出的嵌套列表。
x_values <- list("Group", c("Group", "Age", "Sex"))
y_values <- list("First_outcome", "Second_outcome")
lapply(x_values, function(x_value){
lapply(y_values, function(y_value){
my_model_function(x_value, y_value)
})
})
你也可以在这里替换用户定义的函数——真的不需要创建一个函数，因为它允许从一段代码中重复调用它（我把它留在里面是因为我正在写一个 phone，编辑太多了，但类似于）
lapply(list(1,2,3), function(i){
i^2
})
对于我当前的项目，我反复指定对不同结果具有不同数量 predictors/covariates 的回归模型。现在我只是完整地写出每个模型，但我确信有一种（非常）更快的方法需要更少的代码来完成我正在做的事情。
我的示例数据是 24 名中风患者的重复测量数据集，我在其中评估了三种不同类型的康复 (Group) 对功能恢复评分（Outcome 1 至 Outcome 4).每周测量每位患者的功能能力 (Time_num)，持续 8 周：
library(tidyverse)
library(magrittr)
library(nlme)
mydata <- structure(list(Subject = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L,
21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 23L,
23L, 23L, 23L, 23L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 24L, 24L,
24L, 24L), Age = c(60, 52.5, 57.1, 63, 65.1, 39, 59.3, 65.3,
61.4, 56.3, 46.4, 58.2, 58, 57.7, 56.6, 42.3, 52.5, 51.8, 43.2,
50.9, 56.7, 67.5, 65, 56.5, 65.5, 45.6, 56.7, 47.9, 65.5, 46.6,
68.6, 52.1, 43.1, 62.1, 62.9, 58.3, 49.6, 42.1, 59.7, 62.9, 56.2,
71.7, 60.5, 59.8, 54.3, 76.1, 56.2, 74.3, 48.7, 69.9, 59.6, 58.4,
55.9, 56.5, 33, 57.1, 63, 53.1, 51.3, 46.9, 57.2, 47, 58, 63.7,
69.8, 57.9, 62.7, 44.8, 51.5, 57, 58.1, 53.3, 57.2, 54.2, 50.2,
60.4, 61.1, 81.3, 59.6, 68.8, 49.2, 51, 53.5, 55.9, 66.7, 60.3,
59.8, 61.6, 63.8, 59.8, 55.5, 57.7, 66.3, 54.7, 56.3, 56.7, 57.7,
63.8, 53.5, 56.1, 49, 44.5, 36, 58.2, 50.8, 56.8, 47.9, 51.1,
53.2, 53.4, 59.3, 42.8, 63.6, 51.2, 49, 62.6, 44.8, 59.9, 44.7,
56, 54.3, 58.7, 62.2, 76.7, 31.4, 65.2, 52.8, 56.7, 52.4, 60.6,
54.8, 43.2, 77.6, 58.1, 49.8, 55.2, 53.6, 54.1, 72.9, 58.7, 51.9,
64.9, 56.6, 61, 71.3, 63.1, 57.4, 56.9, 53.8, 73, 58.9, 60.7,
63.8, 54.6, 74.5, 46.7, 44.2, 56.3, 66.8, 56.5, 43.6, 62.8, 55.3,
53.7, 54.9, 46.6, 51.8, 60.7, 62.9, 61.5, 61.6, 43.6, 66.8, 50.1,
51.6, 69.9, 52.2, 58.1, 62.1, 69.2, 59.1, 55.2, 47.2, 64.5, 54.2,
75.9, 52.9, 62.5, 58, 64.5, 70.7, 60.5), Sex = structure(c(1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("Male",
"Female"), class = "factor"), Group = 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, 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, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("A",
"B", "C"), class = "factor"), Time_num = c(1, 2, 3, 4, 5, 6,
7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3,
4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8,
1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5,
6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2,
3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7,
8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4,
5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1,
2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6,
7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8), First_outcome = c(45L,
45L, 45L, 45L, 80L, 80L, 80L, 90L, 20L, 25L, 25L, 25L, 30L, 35L,
30L, 50L, 50L, 50L, 55L, 70L, 70L, 75L, 90L, 90L, 25L, 25L, 35L,
40L, 60L, 60L, 70L, 80L, 100L, 100L, 100L, 100L, 100L, 100L,
100L, 100L, 20L, 20L, 30L, 50L, 50L, 60L, 85L, 95L, 30L, 35L,
35L, 40L, 50L, 60L, 75L, 85L, 30L, 35L, 45L, 50L, 55L, 65L, 65L,
70L, 40L, 55L, 60L, 70L, 80L, 85L, 90L, 90L, 65L, 65L, 70L, 70L,
80L, 80L, 80L, 80L, 30L, 30L, 40L, 45L, 65L, 85L, 85L, 85L, 25L,
35L, 35L, 35L, 40L, 45L, 45L, 45L, 45L, 45L, 80L, 80L, 80L, 80L,
80L, 80L, 15L, 15L, 10L, 10L, 10L, 20L, 20L, 20L, 35L, 35L, 35L,
45L, 45L, 45L, 50L, 50L, 40L, 40L, 40L, 55L, 55L, 55L, 60L, 65L,
20L, 20L, 30L, 30L, 30L, 30L, 30L, 30L, 35L, 35L, 35L, 40L, 40L,
40L, 40L, 40L, 35L, 35L, 35L, 40L, 40L, 40L, 45L, 45L, 45L, 65L,
65L, 65L, 80L, 85L, 95L, 100L, 45L, 65L, 70L, 90L, 90L, 95L,
95L, 100L, 25L, 30L, 30L, 35L, 40L, 40L, 40L, 40L, 25L, 25L,
30L, 30L, 30L, 30L, 35L, 40L, 15L, 35L, 35L, 35L, 40L, 50L, 65L,
65L), Second_outcome = c(3, 50, 7, 43, -23, 32, 48, 46, 32, 46,
23, 34, 46, -2, 46, 49, 45, 44, 53, 1, 61, 23, 41, 52, 25, 54,
26, -1, 22, 50, 21, 20, 70, 62, 67, 18, 55, 25, 5, 16, 43, 35,
59, 5, -5, 50, 35, 32, 25, 25, 13, 57, 42, 21, 35, 34, 38, 52,
63, 52, 44, 36, 32, 30, 26, 42, 44, 53, 39, 29, 13, 37, 41, 31,
18, 41, 40, 29, 28, 22, 6, -15, 16, 26, 0, 41, 35, 28, 35, 32,
41, 49, 16, 43, 56, 63, 14, 46, 43, 46, 36, -3, 49, 33, 49, 20,
20, 31, 27, 23, 34, 36, 39, 20, 29, 58, 45, 60, 40, 17, 77, 45,
13, 62, 43, 74, 47, 56, 13, 12, 36, 2, 40, 57, 35, 31, 28, 82,
49, 6, 10, 46, 49, 17, 55, 16, 12, -17, -7, 22, 20, -14, 21,
17, 41, 47, 25, 34, 72, 59, 26, 24, 46, 16, 35, 34, 51, 40, 25,
53, 24, 14, 66, 18, 18, 34, 29, 81, 12, 50, 55, 33, 62, 38, 24,
25, 29, 60, 71, -6, 60, 49), Third_outcome = c(87, 78, 94, 93,
78, 84, 72, 81, 82, 81, 86, 72, 80, 82, 77, 82, 79, 71, 82, 79,
86, 86, 76, 73, 80, 74, 81, 73, 81, 80, 65, 84, 73, 85, 87, 78,
77, 70, 85, 80, 77, 73, 75, 85, 67, 87, 90, 84, 71, 73, 81, 72,
74, 74, 85, 90, 75, 70, 81, 69, 81, 73, 79, 74, 76, 77, 82, 80,
87, 87, 82, 81, 76, 80, 79, 71, 81, 77, 74, 78, 73, 79, 77, 78,
94, 78, 71, 82, 81, 80, 79, 70, 68, 82, 78, 68, 66, 82, 80, 71,
73, 79, 83, 71, 80, 78, 82, 73, 86, 76, 75, 81, 84, 84, 85, 80,
83, 79, 75, 77, 82, 89, 78, 74, 79, 82, 73, 86, 77, 81, 84, 84,
73, 80, 82, 81, 81, 83, 81, 79, 84, 82, 75, 75, 80, 67, 81, 82,
82, 80, 80, 80, 76, 81, 82, 85, 86, 81, 89, 78, 84, 79, 80, 77,
85, 88, 78, 81, 82, 81, 82, 77, 74, 86, 81, 73, 80, 77, 81, 76,
83, 76, 81, 79, 76, 83, 77, 79, 71, 77, 82, 87), Fourth_outcome = c(59,
36, 53, 51, 59, 50, 56, 57, 52, 42, 60, 44, 46, 52, 54, 68, 63,
37, 51, 46, 67, 42, 63, 47, 41, 48, 51, 48, 51, 34, 35, 46, 52,
52, 44, 67, 47, 58, 57, 55, 50, 56, 36, 42, 51, 51, 42, 49, 59,
55, 44, 53, 42, 64, 75, 64, 41, 44, 39, 64, 40, 48, 51, 54, 42,
52, 35, 55, 53, 66, 34, 50, 56, 35, 32, 63, 52, 35, 63, 38, 57,
67, 35, 41, 47, 31, 55, 60, 52, 60, 44, 52, 63, 53, 48, 69, 43,
44, 40, 45, 63, 39, 48, 56, 44, 57, 56, 62, 54, 49, 47, 62, 41,
41, 59, 32, 62, 39, 64, 46, 44, 78, 68, 38, 51, 27, 57, 55, 67,
51, 44, 61, 24, 49, 62, 61, 43, 41, 54, 47, 41, 28, 40, 31, 57,
58, 36, 48, 58, 61, 67, 50, 47, 56, 56, 69, 43, 43, 58, 55, 48,
52, 46, 51, 38, 58, 44, 43, 49, 59, 31, 37, 46, 55, 45, 50, 45,
67, 48, 37, 51, 47, 66, 42, 52, 46, 61, 47, 34, 49, 58, 38)), row.names = c(NA,
-192L), class = c("tbl_df", "tbl", "data.frame"))
看起来如下：
head(mydata)
# A tibble: 6 x 9
Subject Age Sex Group Time_num First_outcome Second_outcome Third_outcome Fourth_outcome
<int> <dbl> <fct> <fct> <dbl> <int> <dbl> <dbl> <dbl>
1 1 60 Male A 1 45 3 87 59
2 1 52.5 Female A 2 45 50 78 36
3 1 57.1 Female A 3 45 7 94 53
4 1 63 Male A 4 45 43 93 51
5 1 65.1 Male A 5 80 -23 78 59
6 1 39 Female A 6 80 32 84 50
我 运行 现在的模型是每个结果有 2 个线性混合效应模型（使用 nlme::lme）：一个只包含 Group，一个另外包含 Age 和Sex。我现在的做法是：
# Outcome 1
outcome1_modelA <-
lme(fixed=First_outcome ~ 1 + Time_num*Group,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
outcome1_modelB <-
lme(fixed=First_outcome ~ 1 + Time_num*Group + Time_num*Age + Time_num*Sex,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
# Outcome 2, 3, and finally...
# Outcome 4
outcome4_modelA <-
lme(fixed=Fourth_outcome ~ 1 + Time_num*Group,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
outcome4_modelB <-
lme(fixed=Fourth_outcome ~ 1 + Time_num*Group + Time_num*Age + Time_num*Sex,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
但是看到我得到了更多的结果和更多的模型，我想学习一种方法来提高我的代码的效率。我读过有关 for 循环的内容，但似乎找不到适合我的示例。不涉及 for 循环的解决方案也将不胜感激！
创建一个函数来使用您想要更改的部分的参数来完成它 - 您将需要 as.formula() 函数
my_model_function <- function(x, y){
fixed_effects <- as.formula(paste(y, "~ 1 +",
paste("Time_num", x, sep="*", collapse=" + ")))
lme(fixed=fixed_effects,
random= ~1 + Time_num|Subject,
data=mydata,
na.action="na.omit",
method="ML")
}
outcome1_modelA <- my_model_function(x = "Group",
y = "First_outcome")
outcome1_modelB <- my_model_function(x = c("Group", "Age", "Sex"),
y = "First_outcome")
为了使其更加自动化，您可以在 lapply() 中创建嵌套循环，这将 return 模型输出的嵌套列表。
x_values <- list("Group", c("Group", "Age", "Sex"))
y_values <- list("First_outcome", "Second_outcome")
lapply(x_values, function(x_value){
lapply(y_values, function(y_value){
my_model_function(x_value, y_value)
})
})
你也可以在这里替换用户定义的函数——真的不需要创建一个函数，因为它允许从一段代码中重复调用它（我把它留在里面是因为我正在写一个 phone，编辑太多了，但类似于）
lapply(list(1,2,3), function(i){
i^2
})