如何从R中的重复测量方差分析模型中获得残差

问题描述

通常从aov()开始使用summary()函数可以得到残差.

Normally from aov() you can get residuals after using summary() function on it.

但是当我使用重复测量方差分析和公式不同时如何获得残差?

But how can I get residuals when I use Repeated measures ANOVA and formula is different?

## as a test, not particularly sensible statistically
npk.aovE <- aov(yield ~  N*P*K + Error(block), npk)
npk.aovE
summary(npk.aovE)
Error: block
          Df Sum Sq Mean Sq F value Pr(>F)
N:P:K      1   37.0   37.00   0.483  0.525
Residuals  4  306.3   76.57

Error: Within
          Df Sum Sq Mean Sq F value  Pr(>F)
N          1 189.28  189.28  12.259 0.00437 **
P          1   8.40    8.40   0.544 0.47490
K          1  95.20   95.20   6.166 0.02880 *
N:P        1  21.28   21.28   1.378 0.26317
N:K        1  33.14   33.14   2.146 0.16865
P:K        1   0.48    0.48   0.031 0.86275
Residuals 12 185.29   15.44
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

直观summary(npk.aovE)$residuals返回NULL.有人可以帮我吗?

Intuitial summary(npk.aovE)$residuals return NULL..Can anyone can help me with this?

推荐答案

很抱歉，我以太快的速度阅读了您的示例.我建议的内容对于带有aov()的多层模型是不可能的.请尝试以下操作:

I apologize I read your example way too quickly. What I suggested is not possible with multilevel models with aov(). Try the following:

> npk.pr <- proj(npk.aovE)
> npk.pr[[3]][, "Residuals"]

这是一个更简单的可复制性，如果遇到相同的问题，任何人都可以弄乱:

Here's a simpler reproducible anyone can mess around with if they run into the same issue:

x1 <- gl(8, 4)
block <- gl(2, 16)
y <- as.numeric(x1) + rnorm(length(x1))
d <- data.frame(block, x1, y)

m <- aov(y ~ x1 + Error(block), d)
m.pr <- proj(m)
m.pr[[3]][, "Residuals"]

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