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我正在尝试对线性混合模型进行成对比较。我通过以下代码安装了它们

models_list_1 <- out_long %>%
  group_by(signals) %>%
  do(fit = lmerTest::lmer(value ~ COND + (1|COND), data = .)) %>% 
  pull(fit) %>% 
  lapply(., function(x) summary(x)) %>% 
  setNames(sort(unique(out_long$signals)))

现在，如果我要使用 Tukey adjstemnt 为存储在此列表中的每个元素计算成对比较，我应该怎么做？

我试过以下代码

lapply(model_list_1[[i]], lsmeans, pairwise ~ COND + (1 |COND), adjust="tukey")

但我一直在获取以下错误

Error in (function (object, at, cov.reduce = mean, cov.keep = get_emm_option("cov.keep"),  : 
  Can't handle an object of class  “character” 
 Use help("models", package = "emmeans") for information on supported models.

在这里，我让我正在处理的数据集。

> dput(head(out_long, 50))
structure(list(ID = c("01", "01", "01", "04", "04", "04", "06", 
"06", "06", "07", "07", "07", "08", "08", "08", "09", "09", "09", 
"10", "10", "10", "11", "11", "11", "12", "12", "12", "13", "13", 
"13", "15", "15", "15", "16", "16", "16", "17", "17", "17", "18", 
"18", "18", "19", "19", "19", "21", "21", "21", "22", "22"), 
    GR = c("RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
    "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
    "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
    "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
    "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
    "RP"), SES = c("V", "V", "V", "V", "V", "V", "V", "V", "V", 
    "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
    "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
    "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
    "V", "V", "V", "V", "V"), COND = c("NEG-CTR", "NEG-NOC", 
    "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
    "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
    "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
    "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
    "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
    "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
    "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
    "NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC"
    ), signals = c("P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
    "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
    "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
    "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
    "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
    "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
    "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
    "P3FCz", "P3FCz", "P3FCz"), value = c(-11.6312151716924, 
    -11.1438413285935, -3.99591470944713, -0.314155675382471, 
    0.238885648959708, 5.03749946898385, -0.213621915029167, 
    -2.96032491743069, -1.97168681693488, -2.83109425298642, 
    1.09291198163802, -6.692991645215, 4.23849942428043, 2.9898889629932, 
    3.5510699900835, 9.57481668808606, 5.4167795618285, 1.7067607715475, 
    -6.13036076093477, -2.82955734597919, -2.50672211111696, 
    0.528517585832501, 8.16418133488309, 1.88777321897925, -7.73588468896919, 
    -9.83058052401056, -6.97442700196932, 1.27327945355082, 2.11962397764132, 
    0.524299677616254, -1.83310726842883, 0.658810483381172, 
    -0.261373488428192, 4.37524298634374, 0.625555654900511, 
    3.19617639836154, 0.0405517582137798, -3.29357103412113, 
    -0.381435057304614, -5.73445509910268, -6.1129152355645, 
    -2.45744234877604, 2.95352732001065, 0.527721249096473, 1.91803490989119, 
    -3.46703346467546, -2.40438419043702, -5.35374408162217, 
    -7.27028665849262, -7.1532211375959)), row.names = c(NA, 
-50L), class = c("tbl_df", "tbl", "data.frame"))
>

为了更清楚，例如第一个对象包含如下汇总统计信息：

models_list_1[[1]]
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: value ~ COND + (1 | COND)
   Data: .

REML criterion at convergence: 408.5

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.6477 -0.6313  0.0030  0.4443  3.6366 

Random effects:
 Groups   Name        Variance Std.Dev.
 COND     (Intercept)  1.013   1.007   
 Residual             14.896   3.860   
Number of obs: 75, groups:  COND, 3

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)  
(Intercept)   2.4710     1.2686 72.0000   1.948   0.0553 .
CONDNEG-NOC   0.5535     1.7940 72.0000   0.309   0.7586  
CONDNEU-NOC  -1.7600     1.7940 72.0000  -0.981   0.3299  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) CONDNEG
CONDNEG-NOC -0.707        
CONDNEU-NOC -0.707  0.500 
optimizer (nloptwrap) convergence code: 0 (OK)
unable to evaluate scaled gradient
 Hessian is numerically singular: parameters are not uniquely determined
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