运行多行的R代码，每次都有一个变量以提高可读性

Question

我希望通过查看是否有一种“循环”或“重新运行”的代码行的方法来提高代码的可读性，而这些代码线非常相似，但每次都有一个变量不同。

我的实际数据分析涉及从blme软件包运行许多blmer调用。我的每个分析都有一个因变量，一个自变量（其中有很多），“波浪”变量（由于在3个时间点上收集了数据）和独特的参与者ID作为随机效果。

我正在尝试构建许多模型，所有模型都非常相似，但是每个模型都在输入的变量上不同。

在以下代码中，我概述了更多详细信息，构建了一个新的，虚拟的数据文件，并试图重新创建与实际文件中类似的模型。

该代码在我的真实数据和虚拟数据中没有问题。我想提请注意这里的关注是，即使只有3种型号（如下文我的示例中），该代码开始变得漫长而重复。

##test script##
library(dplyr)
library(tidyverse)
library(blme)
#packages loaded - I'm not sure these three are exactly needed, I just loaded
#dplyr and tidyverse incase...but blme is for the Bayesian models coming later
#everything below worked on RStudio on my end but, I like I say, I don't 
#know if that is because of the above packages or not...

##build a file
DV0 <- c(100, 50, 75, 80, 20, 30) #let's say performance on a soccer task at time 1 - max 100
DV1 <- c(100, 60, 80, 80, 25, 40) #performance on soccer task at time 2
DV2 <- c(95, 55, 70, 70, 20, 35) #performance on soccer task at time 3
IV1.0 <- c(90, 60, 65, 75, 40, 50) #score on cognitive task A at time 1 - max 100
IV1.1 <- c(95, 70, 75, 80, 50, 70) #score on cog task A at time 2 
IV1.2 <- c(90, 55, 60, 70, 45, 60) #score on cog task A at time 3
IV2.0 <- c(10, 40, 50, 60, 20, 25) #score on cognitive task B at time 1 - max 100
IV2.1 <- c(20, 50, 60, 75, 35, 35) #score on cog task B at time 2
IV2.2 <- c(15, 40, 40, 55, 25, 25) #score on cos task B at time 3
id <- c("Jon", "Sara", "Lisa", "Tim", "Joe", "Paul")

##create a data frame before pivot to a better format for longitudinal data
df <- data.frame(DV0, DV1, DV2, IV1.0, IV1.1, IV1.2, IV2.0, IV2.1, IV2.2,
                 id)
df.long <- long_panel(df, begin = 0, end = 2, label_location = "end")

#now onto the main analyses 
#here I want to use "blmer" from "blme" package to understand how performance
#on the soccer task first is affected by time alone (model1 below). 
#Next,I want to check whether adding performance on cognitive task A
#influences performance (model2 below), before running the same analyses but with
#cognitive task B (model3 below) - in this example I have just two cognitive 
#tasks, but in my real work I have many more IVs to test (let's in this case 
#just say it would be more cognitive tasks). Final thing I plan to add an 
#individual slope and intercept based on the id variable

#time alone and soccer task performance
model1 <- blmer(DV ~ wave + (1 | id), data = df.long, REML = FALSE,
                fixef.prior = normal)
summary(model1)

#new experimental model with cognitive tasks A performance added
model2 <- blmer(DV ~ IV1. + wave + (1 | id), data = df.long, REML = FALSE,
                fixef.prior = normal)
summary(model2)
anova(model1, model2)

#a similar experimental model with cogntive tasks B performance instead of A
model3 <- blmer(DV ~ IV2. + wave + (1 | id), data = df.long, REML = FALSE,
                fixef.prior = normal)
summary(model3)
anova(model1, model3)

#in the real data I then have many more models with IV1. or IV2. changed for 
#another independent variable (e.g., IV3. or IV4.) and as a result the code
#is very long. I'm wanting to know, can the above be put together in fewer 
#lines of code. What I've been reading is maybe that I could loop somewhere
#so that "IV.*" is replaced each time?

#thanks in advance for any help!

因此，如果您有任何方法可以在此示例中基本运行Model1，Model2和Model3的代码3，如果代码较少行，那就太好了。

Answer 1

您可以创建一个接收自变量作为字符串的函数，加上DF和其他选项，以及Leverages as.formula（）。然后使用lapply（）将功能应用于每个自变量。在运行仅波浪模型时，您可以将“”用作“自变量”（即模型1）。

get_model <- function(ind_var, df, REML = FALSE,fixef.prior = "normal",...) {
  f <- as.formula(paste0("DV ~ ",ind_var, " + wave + (1 | id)"))
  blmer(f, data = df, REML = REML,fixef.prior = fixef.prior,...)
}

现在，获取一个名为models的列表，

models = lapply(c("", "IV1.", "IV2."), get_model, df=df.long)

您可以运行您喜欢的任何ANOVA，如：

anova(models[[1]], models[[3]])

输出：

Data: df
Models:
models[[1]]: DV ~ +wave + (1 | id)
models[[3]]: DV ~ IV2. + wave + (1 | id)
            npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)   
models[[1]]    4 141.41 144.98 -66.707   133.41                        
models[[3]]    5 133.12 137.57 -61.560   123.12 10.296  1   0.001333 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

还有另一个选项，它是制作df.long，甚至“更长”，然后通过分组变量估算模型。 此操作的示例

library(data.table)
setDT(df.long)

df.longer=melt(df.long, measure=c("IV1.", "IV2."),variable.name = "ind_var")

rbind(
  df.long[, .(model=list(blmer(DV~wave+(1|id), REML=F, fixef.prior="normal")))][, ind_var:="None"],
  df.longer[, .(model=list(blmer(DV~value+wave+(1|id), REML=F, fixef.prior="normal"))), ind_var]
)

这是使用data.table输出进行

            model ind_var
           <list>  <fctr>
1: <blmerMod[14]>    None
2: <blmerMod[14]>    IV1.
3: <blmerMod[14]>    IV2.