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（关于SO的第一个问题，对任何错误深表歉意！）

我正在尝试使用线性混合模型分析数据，该模型考虑块（图）和基因型（线）效应，并通过 Welch-Satterthwaite 调整自由度来解释 WT（控制）的不同复制率。这是一个不完整的块设计，每个块中都包含 WT。

我用lmerTest::lmer, 来构建模型

library(lmerTest)

biomass.lmer <- lmer(sqrt(Leaf_area) ~ Line + (1 | Plot) ,
                     data = biomass.allmeans)
anova(biomass.lmer)

到目前为止，一切都很好。考虑了 df 的 Satterthwaite 校正。但是当我尝试进行事后 Dunnett 测试时

dunnett.test <- glht(anova(biomass.lmer), linfct = mcp( Line = "Dunnett"), alternative = "two.sided") 
summary(dunnett.test)

这没用。glht无法处理由 . 返回的 S4 对象lmerTest::anova。但是，lmerTest::lmer也lme4::lmer确实返回了一个有效对象，但后者不能考虑到 Satterthwaite 校正。

问题

我错过了什么吗？非常感谢有关如何将校正模型的方差分析转换为有效对象或使用不同方式对 S4 对象进行 Dunnett 测试的任何建议。

如建议的那样，数据（biomass.allmeans）：

structure(list(Line = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("WT", "11-008", 
"14-005", "2-94", "7-028", "8-93"), class = "factor"), Plot = structure(c(1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 2L, 4L, 5L, 8L, 
9L, 10L, 11L, 2L, 3L, 5L, 7L, 8L, 10L, 12L, 1L, 3L, 5L, 6L, 7L, 
9L, 12L, 1L, 2L, 4L, 6L, 7L, 10L, 11L, 12L, 1L, 3L, 4L, 6L, 8L, 
9L, 11L), .Label = c("1", "2", "3", "4", "5", "6", "7", "8", 
"9", "10", "11", "12"), class = "factor"), Leaf_area = c(3579.5, 
3316.25, 4018.25, 3381, 3697.75, 2938.75, 2487.75, 1529.25, 2514.75, 
2713.5, 2037.5, 3696.25, 3350, 1790.75, 1270, 2706.75, 2517.25, 
4715, 3409.75, 3488.75, 2583.75, 1863, 1749.25, 2001, 2530.25, 
3012, 3514.25, 2871.25, 1740.75, 2745.25, 3279, 2826.5, 3744.25, 
1297.25, 1123.75, 691.5, 1263, 684.666666666667, 891.25, 873.5, 
1461, 5217.75, 4867.25, 4629.75, 3748, 2693.75, 1615.5, 4407)), .Names = c("Line", 
"Plot", "Leaf_area"), class = "data.frame", row.names = c(NA, 
-48L))
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1 回答 1

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我anova.lmerModLmerTest在其中看到，namespace但由于某种原因我无法访问它。R不认为该功能存在。抱歉，我只能使用 运行它stats::anova，但我不确定这是实际问题。?glht正在寻找一个模型。所以如果我只是用lmer它工作的模型运行它。

stats::anova(biomass.lmer)
Type III Analysis of Variance Table with Satterthwaite's method
     Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
Line 4131.8  826.36     5 33.434  18.084 1.146e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

重要的结果，包括对 df 的 Satterthwaite 校正，因此请运行 post-hoc。

dunnett.test <- glht((biomass.lmer), linfct = mcp(Line = "Dunnett"), alternative = "two.sided")

 General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts

Linear Hypotheses:
                 Estimate
11-008 - WT == 0  -0.6996
14-005 - WT == 0  -5.1471
2-94 - WT == 0    -0.2379
7-028 - WT == 0  -23.7876
8-93 - WT == 0     7.5590

summary(dunnett.test)

     Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: Dunnett Contrasts

Fit: lmer(formula = sqrt(Leaf_area) ~ Line + (1 | Plot), data = biomass.allmeans)

Linear Hypotheses:
                 Estimate Std. Error z value Pr(>|z|)    
11-008 - WT == 0  -0.6996     3.2885  -0.213   0.9998    
14-005 - WT == 0  -5.1471     3.2885  -1.565   0.4198    
2-94 - WT == 0    -0.2379     3.2885  -0.072   1.0000    
7-028 - WT == 0  -23.7876     3.1372  -7.582   <0.001 ***
8-93 - WT == 0     7.5590     3.2885   2.299   0.0943 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)
于 2018-07-20T14:47:56.613 回答