如何一次更改整个脚本中的响应变量？答案

【问题标题】：How to change responding variable in whole script at once?如何一次更改整个脚本中的响应变量？
【发布时间】：2021-04-01 22:11:03
【问题描述】：

我有一个包含 10 多种不同农业性状（例如 GPC、WGC、TW、TKW...）的大型实验的数据。对于每个特征，我都使用相同的分析原理（相同的 R 脚本）。我的问题是，有没有一种简单的方法可以一次替换整个脚本中的响应变量？除了使用查找/替换选项。

这是我正在使用的代码的一部分。 GPC是一个响应变量，在我对GPC做完所有的分析之后，我必须对WGC做同样的分析，然后是TW等等。

mod <- lmer(GPC ~ env/rep + gen*env + (1|env:rep:row) + (1|env:rep:col), data = df)

GEmean <- cast(gen ~ env, data = df, value = "GPC", fun = mean)

有没有办法，例如，将整个脚本中的响应变量标记为 V1，然后在脚本的开头更改 V1 的真正含义，它是哪个特征？

非常欢迎任何建议（使用查找/替换选项 :D 除外）！

谢谢！

【问题讨论】：

标签： r variables traits

【解决方案1】：

您可以将变量定义为字符，然后定义一个 RHS 并使用 paste 和 as.formula，

V1 <- "Y1"

rhs <- "~ X1/X2 + X2*X1 + (1|X1:X2:X3) + (1|X1:X2:X4)"

mod <- lme4::lmer(as.formula(paste(V1, rhs)), data=df)

和往常一样cast。

reshape::cast(X3 ~ X2, data=df, value=V1, fun=mean)
# X3          2           3           4           5           6           7          8
# 1  1        NaN         NaN  1.25829503         NaN         NaN  0.24229907        NaN
# 2  2        NaN         NaN         NaN         NaN -1.66993472         NaN        NaN
# 3  3 0.69914031  0.52025487 -0.13089285 -0.45742957  0.68799299 -0.65833219 -0.8931447
# 4  4 0.07498591 -0.06759845 -0.15950271  0.47577484 -0.31345072  0.98613984        NaN
# 5  5 0.34686080  0.84569994  0.22288982 -0.09291759  0.07317659  0.48756834  1.0484134
# 6  6        NaN -0.47471895 -0.01001069  0.03145422 -0.35163411 -0.43344249        NaN
# 7  7        NaN  0.17256757  0.68457304  0.11125033 -0.30236288  0.06483394        NaN
# 8  8        NaN -0.12143693  0.11786080 -0.10686611  0.78027538  0.70950092        NaN

数据：

df <- structure(list(X1 = c(3L, 7L, 0L, 3L, 5L, 6L, 2L, 5L, 4L, 5L, 
5L, 4L, 6L, 5L, 6L, 3L, 4L, 6L, 4L, 7L, 6L, 3L, 6L, 4L, 4L, 5L, 
4L, 7L, 5L, 6L, 7L, 4L, 5L, 5L, 4L, 8L, 3L, 2L, 5L, 7L, 5L, 5L, 
2L, 5L, 6L, 4L, 5L, 3L, 6L, 4L, 5L, 5L, 4L, 6L, 5L, 3L, 3L, 5L, 
7L, 5L, 3L, 6L, 5L, 4L, 6L, 2L, 4L, 6L, 5L, 6L, 4L, 6L, 8L, 5L, 
7L, 4L, 4L, 8L, 7L, 5L, 5L, 4L, 1L, 4L, 6L, 3L, 6L, 3L, 3L, 5L, 
5L, 6L, 7L, 6L, 5L, 5L, 6L, 8L, 6L, 3L), X2 = c(3L, 4L, 6L, 6L, 
6L, 5L, 6L, 3L, 3L, 6L, 4L, 2L, 1L, 7L, 6L, 4L, 6L, 5L, 7L, 6L, 
5L, 2L, 5L, 8L, 5L, 5L, 3L, 5L, 5L, 5L, 3L, 4L, 9L, 2L, 5L, 5L, 
4L, 5L, 5L, 6L, 8L, 9L, 6L, 4L, 10L, 2L, 3L, 5L, 6L, 6L, 4L, 
6L, 5L, 4L, 6L, 4L, 6L, 6L, 4L, 3L, 4L, 5L, 4L, 6L, 5L, 6L, 5L, 
7L, 9L, 6L, 6L, 5L, 3L, 3L, 2L, 5L, 8L, 5L, 4L, 5L, 8L, 8L, 4L, 
3L, 4L, 4L, 5L, 4L, 5L, 3L, 3L, 7L, 5L, 2L, 6L, 5L, 6L, 6L, 5L, 
5L), X3 = c(3L, 5L, 7L, 4L, 4L, 5L, 3L, 4L, 4L, 7L, 4L, 3L, 6L, 
5L, 8L, 4L, 6L, 5L, 5L, 4L, 6L, 3L, 4L, 6L, 4L, 6L, 6L, 5L, 4L, 
3L, 4L, 5L, 3L, 5L, 6L, 6L, 6L, 3L, 7L, 3L, 6L, 5L, 7L, 4L, 8L, 
4L, 5L, 3L, 5L, 2L, 4L, 4L, 8L, 4L, 6L, 4L, 3L, 5L, 5L, 5L, 4L, 
6L, 5L, 7L, 4L, 6L, 5L, 5L, 7L, 6L, 3L, 4L, 3L, 4L, 5L, 6L, 3L, 
3L, 5L, 5L, 8L, 6L, 4L, 4L, 4L, 6L, 1L, 6L, 6L, 5L, 3L, 8L, 5L, 
5L, 5L, 8L, 2L, 5L, 8L, 5L), X4 = c(3L, 7L, 6L, 6L, 4L, 6L, 3L, 
5L, 4L, 6L, 5L, 6L, 5L, 4L, 5L, 8L, 6L, 7L, 3L, 2L, 2L, 8L, 4L, 
5L, 3L, 5L, 5L, 5L, 6L, 5L, 4L, 6L, 4L, 3L, 2L, 5L, 5L, 7L, 4L, 
5L, 6L, 7L, 5L, 6L, 7L, 2L, 2L, 6L, 6L, 7L, 4L, 5L, 3L, 4L, 4L, 
4L, 5L, 3L, 4L, 8L, 6L, 5L, 2L, 5L, 5L, 3L, 5L, 4L, 8L, 7L, 6L, 
3L, 3L, 3L, 3L, 6L, 5L, 7L, 6L, 4L, 6L, 6L, 6L, 5L, 5L, 3L, 8L, 
5L, 4L, 3L, 5L, 4L, 5L, 6L, 3L, 4L, 5L, 4L, 7L, 6L), X5 = c(6L, 
7L, 3L, 3L, 5L, 4L, 6L, 6L, 7L, 3L, 4L, 4L, 6L, 5L, 5L, 3L, 3L, 
5L, 5L, 3L, 5L, 4L, 6L, 6L, 8L, 3L, 7L, 5L, 6L, 5L, 4L, 3L, 5L, 
6L, 5L, 5L, 6L, 4L, 3L, 4L, 6L, 6L, 3L, 6L, 8L, 5L, 5L, 6L, 4L, 
5L, 5L, 8L, 3L, 6L, 5L, 4L, 4L, 6L, 4L, 6L, 5L, 5L, 5L, 5L, 5L, 
7L, 3L, 3L, 4L, 5L, 4L, 9L, 6L, 9L, 4L, 6L, 5L, 7L, 4L, 3L, 7L, 
5L, 3L, 4L, 7L, 3L, 5L, 7L, 5L, 5L, 5L, 3L, 6L, 5L, 5L, 7L, 3L, 
5L, 4L, 6L), X6 = c(4L, 6L, 3L, 5L, 5L, 4L, 3L, 7L, 4L, 7L, 3L, 
7L, 6L, 5L, 4L, 4L, 5L, 5L, 5L, 6L, 5L, 6L, 4L, 5L, 7L, 5L, 3L, 
3L, 4L, 4L, 4L, 6L, 3L, 7L, 4L, 5L, 5L, 3L, 1L, 4L, 4L, 2L, 7L, 
6L, 5L, 6L, 3L, 6L, 5L, 3L, 2L, 7L, 4L, 5L, 4L, 7L, 2L, 2L, 5L, 
4L, 6L, 4L, 7L, 2L, 7L, 6L, 4L, 4L, 6L, 6L, 4L, 8L, 5L, 5L, 4L, 
5L, 5L, 3L, 7L, 4L, 5L, 6L, 4L, 5L, 2L, 3L, 5L, 3L, 8L, 2L, 4L, 
4L, 4L, 3L, 7L, 5L, 8L, 3L, 4L, 5L), X7 = c(4L, 2L, 2L, 4L, 5L, 
5L, 7L, 6L, 5L, 6L, 6L, 4L, 5L, 5L, 6L, 4L, 7L, 4L, 6L, 3L, 2L, 
5L, 7L, 5L, 5L, 5L, 9L, 7L, 9L, 5L, 4L, 6L, 8L, 4L, 5L, 4L, 4L, 
4L, 5L, 4L, 6L, 5L, 5L, 2L, 4L, 4L, 5L, 5L, 3L, 7L, 5L, 6L, 7L, 
5L, 3L, 5L, 5L, 4L, 6L, 3L, 5L, 3L, 4L, 3L, 4L, 5L, 5L, 7L, 7L, 
2L, 4L, 7L, 4L, 6L, 5L, 7L, 5L, 4L, 8L, 6L, 8L, 8L, 3L, 4L, 6L, 
3L, 6L, 6L, 5L, 2L, 5L, 1L, 7L, 2L, 6L, 7L, 3L, 4L, 7L, 3L), 
    Y1 = c(1.51381660212862, 0.504478762772218, -2.65378574088482, 
    1.43068637272239, -1.47684324896984, 1.58472803945312, -0.0922193857071082, 
    -0.823622560562235, -1.64806412194411, 0.944309458265371, 
    -0.356936363717069, -0.162015258645447, 0.12477819226619, 
    -0.288471307345202, -1.18190619708113, -2.39292785982372, 
    0.851281094397896, 0.941804900216781, -0.285843452847721, 
    -0.637399290834473, 1.01562437034261, -0.762266388284812, 
    0.57482379687021, -1.35903991270023, 0.901967856080433, 0.29778523652812, 
    -0.00277763319220751, -0.231661006943, 1.17555880002615, 
    1.1776067492362, 1.2999024919628, 2.39038283158516, -0.650154555398476, 
    -0.953405711072244, 2.07296020843138, 0.186067377477045, 
    1.27493204613466, 0.233083651874369, -1.35590587652704, 1.08696059670825, 
    0.597820644193322, -1.19390157791241, -0.452467982110459, 
    -0.325648996612789, 0.0285958447740426, -0.550033308585066, 
    -0.541233659925362, 0.213664554548748, 0.0896115607405887, 
    -2.22657886242746, -0.835933260512375, -0.3410168395334, 
    0.50787381134198, 0.531258570313673, -0.40901169495662, -0.810128911311157, 
    -0.432058431678758, -0.10219858745039, -0.254360597321428, 
    -0.154028509912877, -0.154412368645572, -2.30203996365628, 
    1.46595811311111, -1.28869300561031, -1.21053214855949, -0.268363291058948, 
    0.683392481853822, -0.319669130901838, 0.645175024187205, 
    -0.411699887457752, -0.191809472206714, -0.862892281569729, 
    -0.496653266818923, -1.82777471788403, -0.316512824335762, 
    -1.92056252600781, 0.162658786066582, 2.80586044363066, -1.90191012546795, 
    -1.37325883300246, -0.293687705724852, -0.891121986533703, 
    0.236105127173931, 1.7496394242358, 1.30113476661601, -0.681215096624127, 
    1.19455442244611, 0.850906768469736, 0.555565335896252, -0.883430008481552, 
    0.824345772604795, 0.687913712610013, 1.33576821950678, -0.808845875084407, 
    -0.0994675295096023, -0.797674770999511, -1.7176681053504, 
    -0.312094291087325, 0.480884995744134, 0.3981995835995), 
    Y2 = c(-1.53285914020452, 1.29122978508191, 0.921606245351757, 
    -0.724085415151949, 0.573933753842347, -1.75990865905637, 
    -0.913661932893341, 0.843278144910845, -0.427945566890583, 
    -0.785931108082936, 0.18126807207226, -0.98233866170379, 
    -1.69942556383645, 0.161515532679225, -0.205045971136367, 
    -0.197708729131696, 0.761530789270692, -0.361650445168573, 
    -0.284077129912569, -1.4375454413273, -1.87641468387563, 
    -0.466142684630522, -1.32675782016916, 0.0766439433592152, 
    -0.345061291806643, 1.20492953816262, -0.498983588300403, 
    -0.39017069355203, -0.585536568506376, -0.268542109333508, 
    -0.304956745639016, 0.6672974421784, -1.33856731439226, 0.260863470997641, 
    -0.229908534712624, 1.87494902300616, -1.58721820502111, 
    -1.27757830338367, -0.164744698727624, 0.822502689857642, 
    0.487841959844427, -0.644277240904474, 0.0252163852645532, 
    -1.98028441396859, 0.303527219762318, 1.27316308828519, 0.0240727927001065, 
    -0.42804136638075, -1.20095767996442, -0.632935247840562, 
    1.3552165382172, -0.343889633450751, -0.62589000162234, 1.36744315735339, 
    -1.79969327521372, -0.31448607863348, 0.0738981401409877, 
    -0.699306260552063, -0.699259392290336, 0.312939740584129, 
    0.61517017083612, 0.159816208307784, -0.0454478235356775, 
    -0.84835290813545, 0.60453967324254, -0.933994665859771, 
    -0.122862888262648, 0.499170848210818, -1.04662060255234, 
    0.24416562707757, -0.239847908251422, 0.892507692301903, 
    1.52229141743761, -1.34797780685531, 0.720419062498517, 0.0347853939462854, 
    0.0636886378960745, 1.09832215170708, 0.526957518899046, 
    -0.0574283783582475, -0.293913833682497, -0.308886513025504, 
    -0.0843275557037345, -0.761732958784744, 1.20579213927605, 
    0.53737587291791, 0.669172456819161, 0.1720175113333, 0.207695456368839, 
    0.129107054181012, 1.22029130711617, -0.388441420234782, 
    -1.08556449077231, 0.612473701706874, 0.81564534791706, -1.32961150152966, 
    0.859407793312078, -0.412360608341253, -0.188597814992205, 
    0.605501867368945)), row.names = c(NA, -100L), class = "data.frame")

【讨论】：

【解决方案2】：

您可以在这两个功能上尝试lapply。例如：

cols <- c('GPC', 'WGC', 'TW') # or use the column numbers

mod <- lapply(df[, cols], function(x) lmer(x ~ env/rep + gen*env + (1|env:rep:row) + (1|env:rep:col), data = df))

GEmean <- lapply(df[ , cols], function(x) cast(gen ~ env, data = df, value = x, fun = mean))

输出应该是分析结果列表。

【讨论】：