如果我考虑相同的线性模型，我不明白为什么 aov 和 anova 会提供不同的结果。我首先使用aov函数执行此操作：
res.aov <- aov(mRNA ~ Time, data= ID1.4.5.6.7)
summary(res.aov)
输出是：
Df Sum Sq Mean Sq F value Pr(>F)
Time 1 0.263 0.26332 6.228 0.0146 *
Residuals 83 3.509 0.04228
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
然后我使用anova函数执行此操作：
model0 <- lm(mRNA ~ 1, data=ID1.4.5.6.7)
model1 <- lm(mRNA ~ Time, data=ID1.4.5.6.7)
model3 <- lm(mRNA ~ Time + Gene , data=ID1.4.5.6.7)
anova_df <- anova(model0,model1,model3)
anova_df[,"model"] <- c("Intercept","Time","Time+Gene")
anova_df
anova(model0,model1,model3)
输出如下：
Analysis of Variance Table
Model 1: mRNA ~ 1
Model 2: mRNA ~ Time
Model 3: mRNA ~ Time + Gene
Res.Df RSS Df Sum of Sq F Pr(>F) model
1 84 3.7727 1
2 83 3.5094 1 0.26332 7.2152 0.0088083 2
3 79 2.8831 4 0.62629 4.2902 0.0034166 3
所以首先我不明白为什么对于模型 mRNA~Time，我们得到不同的F值和不同的p 值？（aov 函数分别给出 6.228 和 0.0146，而 anova 7.2152 和 0.0088083）。
其次，如果我写这行代码：
anova_df[,"model"] <- c("Intercept","Time","Time+Gene")
为什么它不在输出数据框的列模型下打印名称“Intercept”、“Time”和“Time+Gene”？
我的数据集：
structure(list(Gene = c("ID-1", "ID-1", "ID-1", "ID-1", "ID-1",
"ID-1", "ID-1", "ID-1", "ID-1", "ID-1", "ID-1", "ID-1", "ID-1",
"ID-1", "ID-1", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4",
"ID-4", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4", "ID-4",
"ID-4", "ID-4", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5",
"ID-5", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5", "ID-5",
"ID-5", "ID-5", "ID-5", "ID-5", "ID-6", "ID-6", "ID-6", "ID-6",
"ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-6",
"ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-6", "ID-7", "ID-7",
"ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7",
"ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7", "ID-7"
), mRNA = c(-0.181385669, -0.059647494, 0.104476117, -0.052190978,
-0.040484945, 0.194226742, -0.501601326, 0.102342605, -0.127143845,
-0.008523742, -0.102946211, -0.042894028, 0.002922923, -0.134394347,
-0.214204393, -0.138122686, 0.203242361, 0.097935502, 0.147068146,
-0.089430917, 0.331565412, -0.034572422, -0.129896329, 0.324191,
0.470108479, -0.027268223, 0.232304713, 0.090348708, 0.070848402,
0.181540708, -0.502255367, -0.267631441, -0.368647839, -0.040910404,
-0.003983171, -0.003983171, -0.003983171, -0.14980589, -0.119449612,
-0.309154214, -0.487589361, 0.272803506, -0.421733575, -0.467108567,
0.024868338, -0.156025729, -0.044680175, -0.206716896, -0.272014193,
-0.230499883, -0.238597397, -0.118130949, 0.349957464, 0.349957464,
0.349957464, 0.172048587, -0.186226994, 0.16113822, -0.293029136,
-0.111636253, -0.044189887, 0.081555274, -0.048106079, -0.05853566,
0.010407814, -0.066981809, -0.09828484, -0.315190986, -0.005102456,
0.221556197, 0.206584568, 0.206584568, 0.206584568, 0.102649006,
-0.011777384, -0.36963487, -0.054853074, -0.230240699, -0.210508323,
-0.208889919, -0.050763372, 0.023073782, -0.095118984, -0.091076071,
-0.330257395), Time = c(2, 2, 2, 3, 3, 2, 3, 3, 4, 4, 4, 4, 5,
5, 5, 2, 2, 2, 3, 3, 2, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 2, 2, 2,
1, 1, 1, 3, 3, 2, 3, 3, 4, 4, 4, 5, 5, 5, 5, 2, 2, 2, 1, 1, 1,
3, 3, 2, 3, 3, 4, 4, 4, 4, 5, 5, 5, 2, 2, 2, 1, 1, 1, 3, 2, 3,
3, 4, 4, 4, 4, 5, 5, 5, 5), predicted_mRNA = c(-0.00551000342030954,
-0.00551000342030954, -0.00551000342030954, -0.0302695238715682,
-0.0302695238715682, -0.00551000342030954, -0.0302695238715682,
-0.0302695238715682, -0.0550290443228268, -0.0550290443228268,
-0.0550290443228268, -0.0550290443228268, -0.129307605676603,
-0.129307605676603, -0.129307605676603, -0.00551000342030954,
-0.00551000342030954, -0.00551000342030954, -0.0302695238715682,
-0.0302695238715682, -0.00551000342030954, -0.0302695238715682,
-0.0302695238715682, -0.0550290443228268, -0.0550290443228268,
-0.0550290443228268, -0.0550290443228268, -0.129307605676603,
-0.129307605676603, -0.129307605676603, -0.129307605676603, -0.00551000342030954,
-0.00551000342030954, -0.00551000342030954, 0.0192495170309491,
0.0192495170309491, 0.0192495170309491, -0.0302695238715682,
-0.0302695238715682, -0.00551000342030954, -0.0302695238715682,
-0.0302695238715682, -0.0550290443228268, -0.0550290443228268,
-0.0550290443228268, -0.129307605676603, -0.129307605676603,
-0.129307605676603, -0.129307605676603, -0.00551000342030954,
-0.00551000342030954, -0.00551000342030954, 0.0192495170309491,
0.0192495170309491, 0.0192495170309491, -0.0302695238715682,
-0.0302695238715682, -0.00551000342030954, -0.0302695238715682,
-0.0302695238715682, -0.0550290443228268, -0.0550290443228268,
-0.0550290443228268, -0.0550290443228268, -0.129307605676603,
-0.129307605676603, -0.129307605676603, -0.00551000342030954,
-0.00551000342030954, -0.00551000342030954, 0.0192495170309491,
0.0192495170309491, 0.0192495170309491, -0.0302695238715682,
-0.00551000342030954, -0.0302695238715682, -0.0302695238715682,
-0.0550290443228268, -0.0550290443228268, -0.0550290443228268,
-0.0550290443228268, -0.129307605676603, -0.129307605676603,
-0.129307605676603, -0.129307605676603)), row.names = c(NA, -85L
), class = "data.frame")