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我想计算我在 R 中的贝叶斯混合效应模型中不同级别的参数之间的对比，并产生贝叶斯因子。我的结果（Jud）是二元的（1=是/同步，0=否/不同步），参数 SOAsF 是一个具有 6 个级别（0、100、200、300、400、500）的因子。

遵循不同的教程/功能[#1]、[#2]和[#3]，这是我的代码，具有 3 种不同的方式：

 library(emmeans)
    library(brms)
    library(modelbased)

    brm_acc_1<-brm(Jud ~ SOAsF +(1|pxID),data =dat_long, family=bernoulli("logit"), prior = set_prior('normal(0,10)'), iter = 2000, chains=4,  save_all_pars = TRUE)
    summary(brm_acc_1)
    brms::conditional_effects(brm_acc_1)

    ####1           
    groups <- emmeans(brm_acc_1, ~ SOAsF)
    group_diff <- pairs(groups)
    (groups_all <- rbind(groups, group_diff))        
    bayesfactor_parameters(groups_all, prior = brm_acc_1, direction = "two-sided", effects = c("fixed", "random", "all"))

    ####2   
    ppc <- pp_check(brm_acc_1, type = "stat_grouped", group = "SOAsF")
    #contrast 200 - 300
    contrast_300_200 <- ppc$data$value[ppc$data$group == "200"] - ppc$data$value[ppc$data$group == "300"]
    quantile(contrast_300_200*100, probs = c(.5, .025, .975))

    ####3
    h_1 <- hypothesis(brm_acc_1, "SOAsF200 < SOAsF300")
    print(h1, digits = 4)
    h2 <- hypothesis(brm_acc_1, "SOAsF200 > SOAsF300")
    print(h2, digits = 4)

结果：

在此处输入图像描述

 ####1
# Bayes Factor (Savage-Dickey density ratio)

    Parameter    |       BF
    -----------------------
    0, .         | 8.08e-03
    100, .       |     0.61
    200, .       | 7.29e+03
    300, .       |    67.77
    400, .       |    21.81
    500, .       |     0.28
    ., 0 - 100   |     2.75
    ., 0 - 200   | 1.90e+05
    ., 0 - 300   |   410.42
    ., 0 - 400   |   570.11
    ., 0 - 500   |     1.03
    ., 100 - 200 |      0.5
    ., 100 - 300 |     0.05
    ., 100 - 400 |     0.02
    ., 100 - 500 | 7.13e-03
    ., 200 - 300 |     0.01
    ., 200 - 400 |     0.01
    ., 200 - 500 |     1.11
    ., 300 - 400 | 7.21e-03
    ., 300 - 500 |      0.1
    ., 400 - 500 |     0.04

    * Evidence Against The Null: [0]

  ####2
   50%      2.5%     97.5% 
 1.988631 -3.707585  7.680694 

  ####3

Hypothesis Tests for class b:
                Hypothesis Estimate Est.Error CI.Lower CI.Upper
1 (SOAsF200)-(SOAsF... < 0   0.0881    0.0903  -0.0612   0.2372
  Evid.Ratio Post.Prob Star
1     0.1919     0.161     
---
'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
'*': For one-sided hypotheses, the posterior probability exceeds 95%;
for two-sided hypotheses, the value tested against lies outside the 95%-CI.
Posterior probabilities of point hypotheses assume equal prior probabilities.

Hypothesis Tests for class b:
                Hypothesis Estimate Est.Error CI.Lower CI.Upper
1 (SOAsF200)-(SOAsF... > 0   0.0881    0.0903  -0.0612   0.2372
  Evid.Ratio Post.Prob Star
1     5.2112     0.839     
---
'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
'*': For one-sided hypotheses, the posterior probability exceeds 95%;
for two-sided hypotheses, the value tested against lies outside the 95%-CI.
Posterior probabilities of point hypotheses assume equal prior probabilities.

因此，以对比 200 与 300 为例。与 SOA 300 上呈现的声音相比，SOA 200 上呈现的声音是否同样被判断为同步（是）？

方式 #1 似乎提供了零假设 SOA 200 - SOA 300 = 0 且 BF = 0.01 的证据；所以他们似乎同样被认为是同步的？

方式 #2 似乎几乎没有提供零假设 SOA 200 = SOA 300 的证据，证据为 1.988631% 95%CI [-3.707585, 7.680694]。

方式 #3 似乎提供了支持替代假设 SOA 200 > SOA 300 或 SOA 200 - SOA 300 < 0 且 BF = 5.2112 的证据。

我是否发现差异是因为 #1 是双面的，而 #3 是单面的？

但是，我没有设法单面运行＃1（方向=“左”或“右”）

bayesfactor_parameters(groups_all, prior = brm_acc_1, direction = ">",  effects = c("fixed", "random", "all") )
Computation of Bayes factors: sampling priors, please wait...
Error in `$<-.data.frame`(`*tmp*`, "ind", value = 8L) : 
  replacement has 1 row, data has 0

或 #3 双面（假设（brm_acc_1，“SOAsF200 - SOAsF300 = 0”））

Hypothesis Tests for class b:
               Hypothesis Estimate Est.Error CI.Lower CI.Upper
1 (SOAsF200-SOAsF300) = 0   0.0881    0.0903  -0.0914   0.2682
  Evid.Ratio Post.Prob Star
1         NA        NA     
---
'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
'*': For one-sided hypotheses, the posterior probability exceeds 95%;
for two-sided hypotheses, the value tested against lies outside the 95%-CI.
Posterior probabilities of point hypotheses assume equal prior probabilities.

我被卡住了，任何帮助将不胜感激。谢谢。

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