如何根据 Pr(>F) 筛选模型列表
How to filter a list of models based on Pr(>F)
我运行一个模型基本上就是这个
models <- mclapply(frms, function(x) anova(lm(x, data = mrna.pcs)))
现在我想根据 Pr(>F)
过滤模型
型号class是list
这是模特str
str(models)
List of 248
$ PC1 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 205562 63770
..$ Mean Sq: num [1:2] 205562 343
..$ F value: num [1:2] 600 NA
..$ Pr(>F) : num [1:2] 4.34e-60 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC1"
$ PC2 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 1098 185549
..$ Mean Sq: num [1:2] 1098 998
..$ F value: num [1:2] 1.1 NA
..$ Pr(>F) : num [1:2] 0.296 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC2"
$ PC3 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 6023 56650
..$ Mean Sq: num [1:2] 6023 305
..$ F value: num [1:2] 19.8 NA
..$ Pr(>F) : num [1:2] 1.5e-05 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC3"
$ PC4 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 88.1 48006.7
..$ Mean Sq: num [1:2] 88.1 258.1
..$ F value: num [1:2] 0.341 NA
..$ Pr(>F) : num [1:2] 0.56 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC4"
$ PC5 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 390 31192
..$ Mean Sq: num [1:2] 390 168
..$ F value: num [1:2] 2.33 NA
..$ Pr(>F) : num [1:2] 0.129 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC5"
$ PC6 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 58.3 24470
..$ Mean Sq: num [1:2] 58.3 131.6
..$ F value: num [1:2] 0.443 NA
..$ Pr(>F) : num [1:2] 0.506 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC6"
$ PC7 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 21.9 19772.5
..$ Mean Sq: num [1:2] 21.9 106.3
..$ F value: num [1:2] 0.206 NA
..$ Pr(>F) : num [1:2] 0.65 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC7"
$ PC8 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 7.39 17396.15
..$ Mean Sq: num [1:2] 7.39 93.53
..$ F value: num [1:2] 0.0791 NA
..$ Pr(>F) : num [1:2] 0.779 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC8"
$ PC1 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 717 268616
..$ Mean Sq: num [1:2] 358 1452
..$ F value: num [1:2] 0.247 NA
..$ Pr(>F) : num [1:2] 0.782 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC1"
$ PC2 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 238 186409
..$ Mean Sq: num [1:2] 119 1008
..$ F value: num [1:2] 0.118 NA
..$ Pr(>F) : num [1:2] 0.889 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC2"
$ PC3 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 5461 57211
..$ Mean Sq: num [1:2] 2731 309
..$ F value: num [1:2] 8.83 NA
..$ Pr(>F) : num [1:2] 0.000218 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC3"
$ PC4 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 3845 44250
..$ Mean Sq: num [1:2] 1922 239
..$ F value: num [1:2] 8.04 NA
..$ Pr(>F) : num [1:2] 0.00045 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC4"
$ PC5 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 1804 29778
..$ Mean Sq: num [1:2] 902 161
..$ F value: num [1:2] 5.61 NA
..$ Pr(>F) : num [1:2] 0.00433 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC5"
$ PC6 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 7.65 24520.66
..$ Mean Sq: num [1:2] 3.82 132.54
..$ F value: num [1:2] 0.0288 NA
..$ Pr(>F) : num [1:2] 0.972 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC6"
$ PC7 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 378 19416
..$ Mean Sq: num [1:2] 189 105
..$ F value: num [1:2] 1.8 NA
..$ Pr(>F) : num [1:2] 0.168 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC7"
$ PC8 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 239 17165
..$ Mean Sq: num [1:2] 119.4 92.8
..$ F value: num [1:2] 1.29 NA
..$ Pr(>F) : num [1:2] 0.279 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC8"
$ PC1 ~ Index :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 23 164
..$ Sum Sq : num [1:2] 30056 239277
..$ Mean Sq: num [1:2] 1307 1459
..$ F value: num [1:2] 0.896 NA
..$ Pr(>F) : num [1:2] 0.604 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC1"
$ PC2 ~ Index :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 23 164
..$ Sum Sq : num [1:2] 8402 178245
..$ Mean Sq: num [1:2] 365 1087
..$ F value: num [1:2] 0.336 NA
..$ Pr(>F) : num [1:2] 0.998 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC2"
我尝试过滤的是基于pvalue的模型是这样的
myp2<-lapply(models,function(x)x$"Pr(>F)")
myp2<-lapply(models,function(x)x$"Pr(>F)" < 0.05,) this gives me this
Error in FUN(X[[i]], ...) : unused argument (alist()) I know the code is not right.
我的问题是如何将 pvalue 参数传递到只能过滤重要模型的模型中
如有任何建议或帮助，我们将不胜感激。
我的示例数据子集
structure(list(Mouse.ID = c("DO.0661", "DO.0669", "DO.0670",
"DO.0673", "DO.0674", "DO.0676", "DO.0677", "DO.0682", "DO.0683",
"DO.0685", "DO.0686", "DO.0692", "DO.0693", "DO.0696", "DO.0698",
"DO.0701", "DO.0704", "DO.0709", "DO.0710", "DO.0711"), Sex = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = c("F", "M"), class = "factor"), fAge = structure(c(2L,
3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L,
3L, 3L, 2L), .Label = c("6", "12", "18"), class = "factor"),
Index = structure(c(21L, 24L, 11L, 20L, 12L, 19L, 20L, 7L,
1L, 7L, 6L, 15L, 19L, 23L, 14L, 17L, 8L, 22L, 13L, 12L), .Label = c("AR001",
"AR002", "AR003", "AR004", "AR005", "AR006", "AR007", "AR008",
"AR009", "AR010", "AR011", "AR012", "AR013", "AR014", "AR015",
"AR016", "AR018", "AR019", "AR020", "AR021", "AR022", "AR023",
"AR025", "AR027"), class = "factor"), Lane = structure(c(6L,
2L, 4L, 5L, 5L, 4L, 8L, 8L, 8L, 4L, 2L, 2L, 1L, 1L, 2L, 3L,
7L, 4L, 8L, 1L), .Label = c("1", "2", "3", "4", "5", "6",
"7", "8"), class = "factor"), Gen = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), .Label = c("8", "9", "10", "11", "12"), class = "factor"),
PC1 = c(-23.147618298858, -23.004329868562, -17.0024755772689,
-23.9178589007844, -56.7766982399411, -34.3969872418573,
-27.7082679050298, -34.32038042076, -6.54582754257061, -48.2738527700051,
-51.350816410461, -23.1430204310663, -44.8168212771171, -34.9912596308964,
-57.2869816005964, -35.9007859727558, -13.396023721849, -70.4151952469644,
-3.95389163762967, -35.2820334506896), PC2 = c(40.5243564641241,
2.99206119995141, -61.4176842149059, 7.10965422446634, 7.28461966315024,
-64.1955797075099, 9.48345862615554, -1.04318789593829, 29.0090598234213,
-72.8866334170873, -3.21615600827421, 0.792597778173725,
-5.14192513442733, -11.7269589504179, 6.55428703944617, -11.5180102658871,
33.3869522894233, -35.1229326772949, 15.996339264987, -11.8901043502155
), PC3 = c(-17.0598627155672, -22.1038475592448, -6.25238299099893,
23.500307567532, 53.4553992426852, -20.1077749520339, -11.8816581457792,
-5.73256447673161, -22.0636009501435, 0.688509203223446,
16.5309171320498, -19.983643792547, -9.04327584423542, -2.27657333476154,
37.6402580806145, 3.45415683648683, -32.247947130388, 64.7524458379641,
-22.9483534394309, -12.2002153235215), PC4 = c(-5.37605681469604,
28.8757760174757, 1.96723351126677, 10.1757811517044, 7.63553142427313,
-0.61083387825962, -2.14595568269526, 6.96007000414511, -5.55019443290321,
10.7590865244751, -10.6766589136731, 2.57313118560919, -3.80955622632714,
-3.66495004673328, 21.0056059162486, -6.43937479210278, -9.20567548365632,
16.1413805847049, 4.77454270484041, 2.14994000686116), PC5 = c(2.49156058897602,
-2.2801673669604, -5.45494631567109, -5.44682692111089, -7.21616736676726,
-11.0786655194642, 3.89806778409165, 6.1416402328447, -7.6800051817927,
-1.30037456136107, -3.73786692756896, -19.2389148951544,
9.07153121652293, -10.2899662479029, 0.579736383131339, -0.0725346819879087,
16.3956001897781, -12.6980354901866, 2.24690751602866, 26.4308764499693
), PC6 = c(-11.625850369587, 1.54093546690149, -4.87370378395642,
-22.0735137415442, -2.44337914021456, 0.619440592140127,
10.0537326783752, 4.27431733991133, 13.6314815937122, 4.15399959062463,
-10.1029165139482, 3.79816714568195, 11.054055138545, -8.56784129106846,
-16.5277734318821, -11.1264688073482, -10.4604427054892,
-9.80324924496993, -6.23395120489922, 11.8384546696797),
PC7 = c(7.20873385839409, -17.719801994905, -0.811301497692041,
7.55418040146638, -4.68437054723712, 1.1158744957288, -15.1982758555559,
5.25257260525755, -8.31670233486223, -3.86077542839162, -5.29923744674506,
-16.0223534779217, -18.0399629122521, -17.9420689996937,
-14.3059444168904, -14.3249976727842, 12.4030641816896, 0.629537064989641,
1.01109826318526, -5.35255467845748), PC8 = c(-7.19678837565302,
6.24827779166403, 0.224651092284126, 6.10960416152842, -14.6615234719377,
-0.410198021192528, 10.3006326038467, 7.37866876496142, -12.4177204278112,
-11.712973299024, -2.00299875954171, 3.19952937463445, 8.81158436770453,
11.7845383750873, -4.79906390420115, 9.7890992316383, -6.26723664234847,
-3.97353277391602, -7.12621186623398, -7.33366271961528)), row.names = c(NA,
-20L), class = c("tbl_df", "tbl", "data.frame"))
生成模型的代码
iv <- c("Sex", "fAge", "Index", "Lane", "Gen")
dv <- paste0('PC', 1:8)
rhs <- unlist(sapply(1:length(iv), function(m) apply(combn(iv, m = m), 2, paste, collapse = ' * ')))
frms <- with(expand.grid(dv, rhs), paste(Var1, Var2, sep = ' ~ '))
frms
models <- mclapply(frms, function(x) anova(lm(x, data = mrna.pcs)))
你或许可以使用
pvals <- sapply(models,function(x)x[["Pr(>F)"]][1])
models[pvals < 0.05]
我运行一个模型基本上就是这个
models <- mclapply(frms, function(x) anova(lm(x, data = mrna.pcs)))
现在我想根据 Pr(>F)
型号class是list
这是模特str
str(models)
List of 248
$ PC1 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 205562 63770
..$ Mean Sq: num [1:2] 205562 343
..$ F value: num [1:2] 600 NA
..$ Pr(>F) : num [1:2] 4.34e-60 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC1"
$ PC2 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 1098 185549
..$ Mean Sq: num [1:2] 1098 998
..$ F value: num [1:2] 1.1 NA
..$ Pr(>F) : num [1:2] 0.296 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC2"
$ PC3 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 6023 56650
..$ Mean Sq: num [1:2] 6023 305
..$ F value: num [1:2] 19.8 NA
..$ Pr(>F) : num [1:2] 1.5e-05 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC3"
$ PC4 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 88.1 48006.7
..$ Mean Sq: num [1:2] 88.1 258.1
..$ F value: num [1:2] 0.341 NA
..$ Pr(>F) : num [1:2] 0.56 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC4"
$ PC5 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 390 31192
..$ Mean Sq: num [1:2] 390 168
..$ F value: num [1:2] 2.33 NA
..$ Pr(>F) : num [1:2] 0.129 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC5"
$ PC6 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 58.3 24470
..$ Mean Sq: num [1:2] 58.3 131.6
..$ F value: num [1:2] 0.443 NA
..$ Pr(>F) : num [1:2] 0.506 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC6"
$ PC7 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 21.9 19772.5
..$ Mean Sq: num [1:2] 21.9 106.3
..$ F value: num [1:2] 0.206 NA
..$ Pr(>F) : num [1:2] 0.65 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC7"
$ PC8 ~ Sex :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 1 186
..$ Sum Sq : num [1:2] 7.39 17396.15
..$ Mean Sq: num [1:2] 7.39 93.53
..$ F value: num [1:2] 0.0791 NA
..$ Pr(>F) : num [1:2] 0.779 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC8"
$ PC1 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 717 268616
..$ Mean Sq: num [1:2] 358 1452
..$ F value: num [1:2] 0.247 NA
..$ Pr(>F) : num [1:2] 0.782 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC1"
$ PC2 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 238 186409
..$ Mean Sq: num [1:2] 119 1008
..$ F value: num [1:2] 0.118 NA
..$ Pr(>F) : num [1:2] 0.889 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC2"
$ PC3 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 5461 57211
..$ Mean Sq: num [1:2] 2731 309
..$ F value: num [1:2] 8.83 NA
..$ Pr(>F) : num [1:2] 0.000218 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC3"
$ PC4 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 3845 44250
..$ Mean Sq: num [1:2] 1922 239
..$ F value: num [1:2] 8.04 NA
..$ Pr(>F) : num [1:2] 0.00045 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC4"
$ PC5 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 1804 29778
..$ Mean Sq: num [1:2] 902 161
..$ F value: num [1:2] 5.61 NA
..$ Pr(>F) : num [1:2] 0.00433 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC5"
$ PC6 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 7.65 24520.66
..$ Mean Sq: num [1:2] 3.82 132.54
..$ F value: num [1:2] 0.0288 NA
..$ Pr(>F) : num [1:2] 0.972 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC6"
$ PC7 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 378 19416
..$ Mean Sq: num [1:2] 189 105
..$ F value: num [1:2] 1.8 NA
..$ Pr(>F) : num [1:2] 0.168 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC7"
$ PC8 ~ fAge :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 2 185
..$ Sum Sq : num [1:2] 239 17165
..$ Mean Sq: num [1:2] 119.4 92.8
..$ F value: num [1:2] 1.29 NA
..$ Pr(>F) : num [1:2] 0.279 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC8"
$ PC1 ~ Index :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 23 164
..$ Sum Sq : num [1:2] 30056 239277
..$ Mean Sq: num [1:2] 1307 1459
..$ F value: num [1:2] 0.896 NA
..$ Pr(>F) : num [1:2] 0.604 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC1"
$ PC2 ~ Index :Classes ‘anova’ and 'data.frame': 2 obs. of 5 variables:
..$ Df : int [1:2] 23 164
..$ Sum Sq : num [1:2] 8402 178245
..$ Mean Sq: num [1:2] 365 1087
..$ F value: num [1:2] 0.336 NA
..$ Pr(>F) : num [1:2] 0.998 NA
..- attr(*, "heading")= chr [1:2] "Analysis of Variance Table\n" "Response: PC2"
我尝试过滤的是基于pvalue的模型是这样的
myp2<-lapply(models,function(x)x$"Pr(>F)")
myp2<-lapply(models,function(x)x$"Pr(>F)" < 0.05,) this gives me this
Error in FUN(X[[i]], ...) : unused argument (alist()) I know the code is not right.
我的问题是如何将 pvalue 参数传递到只能过滤重要模型的模型中
如有任何建议或帮助，我们将不胜感激。
我的示例数据子集
structure(list(Mouse.ID = c("DO.0661", "DO.0669", "DO.0670",
"DO.0673", "DO.0674", "DO.0676", "DO.0677", "DO.0682", "DO.0683",
"DO.0685", "DO.0686", "DO.0692", "DO.0693", "DO.0696", "DO.0698",
"DO.0701", "DO.0704", "DO.0709", "DO.0710", "DO.0711"), Sex = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = c("F", "M"), class = "factor"), fAge = structure(c(2L,
3L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 3L, 2L,
3L, 3L, 2L), .Label = c("6", "12", "18"), class = "factor"),
Index = structure(c(21L, 24L, 11L, 20L, 12L, 19L, 20L, 7L,
1L, 7L, 6L, 15L, 19L, 23L, 14L, 17L, 8L, 22L, 13L, 12L), .Label = c("AR001",
"AR002", "AR003", "AR004", "AR005", "AR006", "AR007", "AR008",
"AR009", "AR010", "AR011", "AR012", "AR013", "AR014", "AR015",
"AR016", "AR018", "AR019", "AR020", "AR021", "AR022", "AR023",
"AR025", "AR027"), class = "factor"), Lane = structure(c(6L,
2L, 4L, 5L, 5L, 4L, 8L, 8L, 8L, 4L, 2L, 2L, 1L, 1L, 2L, 3L,
7L, 4L, 8L, 1L), .Label = c("1", "2", "3", "4", "5", "6",
"7", "8"), class = "factor"), Gen = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), .Label = c("8", "9", "10", "11", "12"), class = "factor"),
PC1 = c(-23.147618298858, -23.004329868562, -17.0024755772689,
-23.9178589007844, -56.7766982399411, -34.3969872418573,
-27.7082679050298, -34.32038042076, -6.54582754257061, -48.2738527700051,
-51.350816410461, -23.1430204310663, -44.8168212771171, -34.9912596308964,
-57.2869816005964, -35.9007859727558, -13.396023721849, -70.4151952469644,
-3.95389163762967, -35.2820334506896), PC2 = c(40.5243564641241,
2.99206119995141, -61.4176842149059, 7.10965422446634, 7.28461966315024,
-64.1955797075099, 9.48345862615554, -1.04318789593829, 29.0090598234213,
-72.8866334170873, -3.21615600827421, 0.792597778173725,
-5.14192513442733, -11.7269589504179, 6.55428703944617, -11.5180102658871,
33.3869522894233, -35.1229326772949, 15.996339264987, -11.8901043502155
), PC3 = c(-17.0598627155672, -22.1038475592448, -6.25238299099893,
23.500307567532, 53.4553992426852, -20.1077749520339, -11.8816581457792,
-5.73256447673161, -22.0636009501435, 0.688509203223446,
16.5309171320498, -19.983643792547, -9.04327584423542, -2.27657333476154,
37.6402580806145, 3.45415683648683, -32.247947130388, 64.7524458379641,
-22.9483534394309, -12.2002153235215), PC4 = c(-5.37605681469604,
28.8757760174757, 1.96723351126677, 10.1757811517044, 7.63553142427313,
-0.61083387825962, -2.14595568269526, 6.96007000414511, -5.55019443290321,
10.7590865244751, -10.6766589136731, 2.57313118560919, -3.80955622632714,
-3.66495004673328, 21.0056059162486, -6.43937479210278, -9.20567548365632,
16.1413805847049, 4.77454270484041, 2.14994000686116), PC5 = c(2.49156058897602,
-2.2801673669604, -5.45494631567109, -5.44682692111089, -7.21616736676726,
-11.0786655194642, 3.89806778409165, 6.1416402328447, -7.6800051817927,
-1.30037456136107, -3.73786692756896, -19.2389148951544,
9.07153121652293, -10.2899662479029, 0.579736383131339, -0.0725346819879087,
16.3956001897781, -12.6980354901866, 2.24690751602866, 26.4308764499693
), PC6 = c(-11.625850369587, 1.54093546690149, -4.87370378395642,
-22.0735137415442, -2.44337914021456, 0.619440592140127,
10.0537326783752, 4.27431733991133, 13.6314815937122, 4.15399959062463,
-10.1029165139482, 3.79816714568195, 11.054055138545, -8.56784129106846,
-16.5277734318821, -11.1264688073482, -10.4604427054892,
-9.80324924496993, -6.23395120489922, 11.8384546696797),
PC7 = c(7.20873385839409, -17.719801994905, -0.811301497692041,
7.55418040146638, -4.68437054723712, 1.1158744957288, -15.1982758555559,
5.25257260525755, -8.31670233486223, -3.86077542839162, -5.29923744674506,
-16.0223534779217, -18.0399629122521, -17.9420689996937,
-14.3059444168904, -14.3249976727842, 12.4030641816896, 0.629537064989641,
1.01109826318526, -5.35255467845748), PC8 = c(-7.19678837565302,
6.24827779166403, 0.224651092284126, 6.10960416152842, -14.6615234719377,
-0.410198021192528, 10.3006326038467, 7.37866876496142, -12.4177204278112,
-11.712973299024, -2.00299875954171, 3.19952937463445, 8.81158436770453,
11.7845383750873, -4.79906390420115, 9.7890992316383, -6.26723664234847,
-3.97353277391602, -7.12621186623398, -7.33366271961528)), row.names = c(NA,
-20L), class = c("tbl_df", "tbl", "data.frame"))
生成模型的代码
iv <- c("Sex", "fAge", "Index", "Lane", "Gen")
dv <- paste0('PC', 1:8)
rhs <- unlist(sapply(1:length(iv), function(m) apply(combn(iv, m = m), 2, paste, collapse = ' * ')))
frms <- with(expand.grid(dv, rhs), paste(Var1, Var2, sep = ' ~ '))
frms
models <- mclapply(frms, function(x) anova(lm(x, data = mrna.pcs)))
你或许可以使用
pvals <- sapply(models,function(x)x[["Pr(>F)"]][1])
models[pvals < 0.05]