multifactor anovaはエラーコードを返し続けます

Question

私は、序数データを別の列の連続データと比較する必要のあるデータセットを扱っています。すなわち、個人は（実際には年齢別に）分類されており、異なる年齢の範囲を2つの異なるテスト値と比較する必要があります。私は多因子のanovaを実行しようとしていて、運がなかった。

はまず、私は、各年齢カテゴリのサブセットと、これを試してみました：

aov.first.molar<-aov(carbon.combo~first.m.cat.1+first.m.cat.2+first.m.cat.3+first.m.cat.4+first.m.cat.5) 
Error in model.frame.default(formula = carbon.combo ~ first.m.cat.1 + : 
invalid type (list) for variable 'first.m.cat.1' 

だから、サブセットは動作しませんでしたので、私はちょうどそれが魔法のようにカテゴリ別に整理するかどうちょうど見るために、列ヘッダーを使用してみました。 ..

> aov.albania.first<-aov(albania$AgeCat_first~albania$juv_deltaC_dentine+albania$Adult_deltaC_collagen) 
Warning messages: 
1: In model.response(mf, "numeric") : 
using type = "numeric" with a factor response will be ignored 
2: In Ops.factor(y, z$residuals) : ‘-’ not meaningful for factors 

> summary(aov.albania.first) 
Error in levels(x)[x] : only 0's may be mixed with negative subscripts 

これは明らかにうまくいかなかったし、私が間違っていることもわからない。私はすべてを要素として設定しましたが、なぜコードが機能していないのか分かりません。 私のテストデータの性質が否定的であるという事実と関係があるのだろうかと思います。私はデータを変更せずにそれを修正する方法がわかりません

ここに私のデータは、要求通りです。すみません、ごめんなさい、私はそれをより良くフォーマットする方法がわかりません。行列にそれを助けた目を向けると、私はまだ

structure(list(Number = structure(1:10, .Label = c("142-c-1", 
"142-c-3", "142-c-5", "156-c-1", "156-c-4", "156-c-6", "157-c-1", 
"157-c-3", "157-c-5", "157-c-6", "158-c-3", "158-c-6", "178-c-1/A", 
"178-c-2/A", "178-c-2/b", "178-c-3/b", "178-c-4/b", "186-c-2/a", 
"186-c-2/b", "186-c-3/b", "186-c-4/b", "186-c-5/b", "186-c-6/b", 
"192-c-1", "192-c-2", "192-c-3", "192-c-4", "192-c-5", "205-c-1", 
"205-c-2", "205-c-3", "205-c-4", "205-c-5", "205-c-6", "210-c-1", 
"210-c-2", "210-c-3", "210-c-4", "210-c-5", "215-c-1", "215-c-2", 
"215-c-3", "215-c-4", "215-c-5", "215-c-6", "215-c-7", "270-c-1", 
"270-c-2", "270-c-3", "270-c-4", "270-c-5", "295-c-1", "295-c-3", 
"295-c-4", "353-c-2", "353-c-3", "353-c-4", "353-c-5", "353-c-6", 
"382-c-1", "390-c-1", "390-c-2", "390-c-3"), class = "factor"), 
ToothID = structure(c(3L, 3L, 3L, 8L, 8L, 8L, 7L, 7L, 7L, 
7L), .Label = c("LI2", "LM1", "LM1-2", "LM3", "LP1-2", "M2", 
"RM1-2", "RM2"), class = "factor"), sex = structure(c(2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("F", "M"), class = "factor"), 
Al.Qahtani.category = structure(c(2L, 5L, 8L, 2L, 5L, 8L, 
2L, 6L, 7L, 8L), .Label = c("AC", "CR 1/2", "CR 3/4", "CRC", 
"R 1/2", "R 1/4", "R 3/4", "RC", "Ri ", "unk"), class = "factor"), 
AgeCat_first = structure(c(1L, 2L, 3L, 2L, 3L, 4L, 1L, 2L, 
2L, 3L), .Label = c("1", "2", "3", "4", "5"), class = "factor"), 
AgeCat_second = c(2L, 3L, 4L, 2L, 3L, 4L, 2L, 3L, 4L, 4L), 
sample_age_first = structure(c(9L, 18L, 23L, 17L, 27L, 6L, 
10L, 13L, 21L, 23L), .Label = c("10.5 to 16.5", "11.5 to 14.5", 
"11.5 to 15.5", "11.5 to 18.5", "11.5 to 19.5", "12.5 to 15.5", 
"12.5 to 19.5", "15.5 to 20.5", "1.5 to 2.5", "1.5 to 3.5", 
"17.5 to 22.5", "2.5 to 4.5", "3.5 to 6.5", "3.5 to 7.5", 
" 4.5 to 6.5 ", "4.5 to 6.5", "4.5 to 7.5", "4.5 to 8.5", 
"6.5 to 11.5", "6.5 to 8.5", "6.5 to 9.5", "7.5 to 10.5", 
"8.5 to 10.5", "8.5 to 11.5", "8.5 to 12.5", "9.5 to 12.5", 
"9.5 to 13.5", "9.5 to 15.5", "unk"), class = "factor"), 
sample_age_second = structure(c(16L, 25L, 7L, 15L, 26L, 7L, 
15L, 22L, 2L, 7L), .Label = c("10.5 to 16.5", "11.5 to 13.5", 
"11.5 to 14.5", "11.5 to 15.5", "11.5 to 18.5", "11.5 to 19.5", 
"12.5 to 15.5", "12.5 to 19.5", "14.5 to 17.5", "15.5 to 20.5", 
"1.5 to 3.5", "17.5 to 22.5", "3.5 to 6.5", "4.5 to 6.5", 
"4.5 to 7.5", "4.5 to7.5", " 5.5 to 6.5 ", "6.5 to 11.5", 
"6.5 to 8.5", "6.5 to 9.5", "7.5 to 11.5", "7.5 to 12.5", 
"8.5 to 12.5", "9.5 to 12.5", "9.5 to12.5", "9.5 to 13.5", 
"9.5 to 15.5", "unk"), class = "factor"), AgeCat_adult = c(9L, 
9L, 9L, 8L, 8L, 8L, 7L, 7L, 7L, 7L), age_at_death = structure(c(3L, 
3L, 3L, 2L, 2L, 2L, 1L, 1L, 1L, 1L), .Label = c("18-30", 
"31-45", ">45", "Adolescent", "Ind"), class = "factor"), 
weight_percent_.N = c(11.5, 6.6, 6.8, 7.8, 8.7, 9.4, 5.6, 
5.6, 9.1, 3.9), weight_percent_C = c(37.8, 26.2, 29.5, 32.7, 
34.7, 34.4, 22, 30.7, 46.8, 22.7), juv_deltaN_dentine = c(4.54, 
4.45, NA, 4.03, 5.73, 6.81, 5.03, 4.58, 0.3, NA), juv_deltaC_dentine = c(-22.042, 
-22.865, -24.345, -23.557, -23.24, -22.282, -22.85, -22.697, 
-25.439, -25.776), juv_proxy = c(7.958, 7.135, 5.655, 6.443, 
6.76, 7.718, 7.15, 7.303, 4.561, 4.224), Adult_deltaC_collagen = c(-18.62, 
-18.62, -18.62, -18.9, -18.9, -18.9, -18.64, -18.64, -18.64, 
-18.64), adult_proxy = c(11.38, 11.38, 11.38, 11.1, 11.1, 
11.1, 11.36, 11.36, 11.36, 11.36), Adult_deltaC_apatite = c(12.29, 
12.29, 12.29, -10.23, -10.23, -10.23, -10.73, -10.73, -10.73, 
-10.73), Adult_deltaN = c(-18.62, -18.62, -18.62, -18.9, 
-18.9, -18.9, -18.64, -18.64, -18.64, -18.64), apatite_collagen_spacing = c(8.66, 
8.66, 8.66, 7.67, 7.67, 7.67, 7.74, 7.74, 7.74, 7.74), Adult_percent_C = structure(c(2L, 
2L, 2L, 6L, 6L, 6L, 7L, 7L, 7L, 7L), .Label = c("14.31%", 
"22.35%", "33.96%", "34.58%", "36.60%", "39.07%", "39.51%", 
"42.12%", "42.17%", "42.29%", "42.81%", "44.01%", "44.72%", 
"45.52%"), class = "factor"), Adult_percent_N = structure(c(14L, 
14L, 14L, 4L, 4L, 4L, 5L, 5L, 5L, 5L), .Label = c("12.16%", 
"12.30%", "13.04%", "13.78%", "14.20%", "14.89%", "14.97%", 
"15.13%", "15.18%", "15.66%", "15.85%", "16.10%", "4.60%", 
"7.98%"), class = "factor"), Adult_CN_ratio = c(3.27, 3.27, 
3.27, 3.31, 3.31, 3.31, 3.25, 3.25, 3.25, 3.25), delta_18O = c(-5.5, 
-5.5, -5.5, -4.79, -4.79, -4.79, -5.39, -5.39, -5.39, -5.39 
), CP = c(0.17, 0.17, 0.17, 0.21, 0.21, 0.21, 0.2, 0.2, 0.2, 
0.2), IR_SF = c(3.33, 3.33, 3.33, 3.12, 3.12, 3.12, 3.19, 
3.19, 3.19, 3.19), adult_bone_sampled = structure(c(2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("femur", "humerus", 
"occipital", "temporal", "tibia"), class = "factor")), .Names = c("Number", 
"ToothID", "sex", "Al.Qahtani.category", "AgeCat_first", "AgeCat_second", 
"sample_age_first", "sample_age_second", "AgeCat_adult", "age_at_death", 
"weight_percent_.N", "weight_percent_C", "juv_deltaN_dentine", 
"juv_deltaC_dentine", "juv_proxy", "Adult_deltaC_collagen", "adult_proxy", 
"Adult_deltaC_apatite", "Adult_deltaN", "apatite_collagen_spacing", 
"Adult_percent_C", "Adult_percent_N", "Adult_CN_ratio", "delta_18O", 
"CP", "IR_SF", "adult_bone_sampled"), row.names = c(NA, 10L), class = "data.frame") 

Answer 1

あなたのデータ... anovに問題があり、私はすでに要因になって、特定のものを見つけることができないggplotています2番目の質問に対応し、この答えも同様です。

aovの機能は、カテゴリに応じて応答を測定することによって行われます。したがって、式はvariable ~ factorとして設計する必要があります。

aov.albania.first <- aov(juv_deltaC_dentine + Adult_deltaC_collagen ~ AgeCat_first, 
    data = albania) 

summary(aov.albania.first) 
      Df Sum Sq Mean Sq F value Pr(>F) 
AgeCat_first 3 6.480 2.160 1.667 0.272 
Residuals  6 7.773 1.296 

最初の質問の問題は、これと似ている可能性があります。さらに、str(first.m.cat.1)をチェックし、変数をvectorに再フォーマットします。