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使用glmer逻辑回归模型预测连续变量达到特定概率的点？

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使用glmer逻辑回归模型预测连续变量达到特定概率的点？,r,prediction,predict,mixed-models,R,Prediction,Predict,Mixed Models,我有一个与记忆实验相关的数据集，其中参与者、方框、试验次数（试验）、试块（区块）、年龄组和性别作为分类变量，间隔作为连续变量，以及二元结果（SuccessBinary），如这个较小的示例数据集所示： data <- structure(list(Participant = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,

我有一个与记忆实验相关的数据集，其中参与者、方框、试验次数（试验）、试块（区块）、年龄组和性别作为分类变量，间隔作为连续变量，以及二元结果（SuccessBinary），如这个较小的示例数据集所示：

data <- structure(list(Participant = structure(c(1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("1", 
    "2", "3", "4", "5", "6", "7", "8", "9", "10"), class = "factor"), 
        Block = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L), .Label = c("1", "2", "3"), class = "factor"), 
        Trial = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
        13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 
        6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
        19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
        13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 
        6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
        19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
        13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 
        6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
        19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
        13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 1L, 2L, 3L, 4L, 5L, 
        6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
        19L, 20L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
        13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L), Box = structure(c(4L, 
        3L, 4L, 3L, 2L, 4L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 
        4L, 3L, 1L, 4L, 4L, 2L, 4L, 1L, 2L, 1L, 3L, 1L, 3L, 1L, 2L, 
        3L, 2L, 3L, 4L, 1L, 4L, 3L, 2L, 4L, 4L, 3L, 2L, 4L, 1L, 2L, 
        3L, 1L, 4L, 1L, 3L, 2L, 3L, 1L, 4L, 2L, 1L, 3L, 2L, 4L, 4L, 
        3L, 4L, 3L, 2L, 4L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 
        4L, 3L, 1L, 4L, 4L, 2L, 4L, 1L, 2L, 1L, 3L, 1L, 3L, 1L, 2L, 
        3L, 2L, 3L, 4L, 1L, 4L, 3L, 2L, 4L, 4L, 3L, 2L, 4L, 1L, 2L, 
        3L, 1L, 4L, 1L, 3L, 2L, 3L, 1L, 4L, 2L, 1L, 3L, 2L, 4L, 4L, 
        3L, 4L, 3L, 2L, 4L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 2L, 1L, 
        4L, 3L, 1L, 4L, 4L, 2L, 4L, 1L, 2L, 1L, 3L, 1L, 3L, 1L, 2L, 
        3L, 2L, 3L, 4L, 1L, 4L, 3L, 2L, 4L, 4L, 3L, 2L, 4L, 1L, 2L, 
        3L, 1L, 4L, 1L, 3L, 2L, 3L, 1L, 4L, 2L, 1L, 3L, 2L, 4L), .Label = c("1", 
        "2", "3", "4"), class = "factor"), Interval = c(240L, 120L, 
        0L, 60L, 120L, 30L, 0L, 30L, 60L, 0L, 240L, 30L, 240L, 60L, 
        30L, 120L, 60L, 0L, 240L, 120L, 30L, 0L, 240L, 120L, 30L, 
        240L, 0L, 60L, 240L, 30L, 240L, 30L, 120L, 60L, 120L, 0L, 
        60L, 120L, 60L, 0L, 60L, 30L, 240L, 120L, 60L, 0L, 240L, 
        0L, 240L, 120L, 60L, 30L, 0L, 30L, 0L, 120L, 240L, 120L, 
        60L, 30L, 240L, 120L, 0L, 60L, 120L, 30L, 0L, 30L, 60L, 0L, 
        240L, 30L, 240L, 60L, 30L, 120L, 60L, 0L, 240L, 120L, 30L, 
        0L, 240L, 120L, 30L, 240L, 0L, 60L, 240L, 30L, 240L, 30L, 
        120L, 60L, 120L, 0L, 60L, 120L, 60L, 0L, 60L, 30L, 240L, 
        120L, 60L, 0L, 240L, 0L, 240L, 120L, 60L, 30L, 0L, 30L, 0L, 
        120L, 240L, 120L, 60L, 30L, 240L, 120L, 0L, 60L, 120L, 30L, 
        0L, 30L, 60L, 0L, 240L, 30L, 240L, 60L, 30L, 120L, 60L, 0L, 
        240L, 120L, 30L, 0L, 240L, 120L, 30L, 240L, 0L, 60L, 240L, 
        30L, 240L, 30L, 120L, 60L, 120L, 0L, 60L, 120L, 60L, 0L, 
        60L, 30L, 240L, 120L, 60L, 0L, 240L, 0L, 240L, 120L, 60L, 
        30L, 0L, 30L, 0L, 120L, 240L, 120L, 60L, 30L), SuccessBinary = c(1L, 
        0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 
        1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 
        1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 
        1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 
        1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 
        0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
        0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
        0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
        0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 
        1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 
        1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
        1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L), 
        AgeGroup = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
        3L, 3L, 3L, 3L, 3L, 3L), .Label = c("1-5", "11-15", "6-10"
        ), class = "factor"), Sex = structure(c(2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
        2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
        1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("F", 
        "M"), class = "factor")), row.names = c(NA, -180L), spec = structure(list(
        cols = structure(list(Dog = structure(list(), class = c("collector_integer", 
        "collector")), Block = structure(list(), class = c("collector_integer", 
        "collector")), Trial = structure(list(), class = c("collector_integer", 
        "collector")), Box = structure(list(), class = c("collector_integer", 
        "collector")), Interval = structure(list(), class = c("collector_integer", 
        "collector")), Visit = structure(list(), class = c("collector_character", 
        "collector")), Outcome = structure(list(), class = c("collector_character", 
        "collector")), SuccessBinary = structure(list(), class = c("collector_integer", 
        "collector")), Age = structure(list(), class = c("collector_integer", 
        "collector")), AgeGroup = structure(list(), class = c("collector_character", 
        "collector")), Sex = structure(list(), class = c("collector_character", 
        "collector"))), .Names = c("Dog", "Block", "Trial", "Box", 
        "Interval", "Visit", "Outcome", "SuccessBinary", "Age", "AgeGroup", 
        "Sex")), default = structure(list(), class = c("collector_guess", 
        "collector"))), .Names = c("cols", "default"), class = "col_spec"), class = c("tbl_df", 
    "tbl", "data.frame"), .Names = c("Participant", "Block", "Trial", 
    "Box", "Interval", "SuccessBinary", "AgeGroup", "Sex"))
因此，区间（我感兴趣的主要变量）、方框2和试验（我还计算了优势比和CI——未显示）有显著影响

我现在想知道的是如何预测成功概率达到0.25的时间间隔（这是本实验中偶然期望的值）

此外，如果这样做是可能和有效的，我还想检查每个测量间隔的成功概率是否与0.25显著不同（我知道为了做到这一点，我必须创建一个新模型，将间隔作为分类变量而不是连续变量，但除此之外，我不知道如何做到。）

提前感谢您的帮助和建议

model <- glmer(SuccessBinary ~ Interval + Block + Box + Trial + AgeGroup + Sex +
           + (1 | Participant),
           data = data, family = binomial, nAGQ=1)

Fixed effects:
               Estimate Std. Error z value Pr(>|z|)    
(Intercept)    1.049030   0.391677   2.678   0.0074 ** 
Interval      -0.008192   0.001142  -7.171 7.43e-13 ***
Block2         0.206471   0.212581   0.971   0.3314    
Block3         0.223291   0.224761   0.993   0.3205    
Box2          -0.521759   0.251940  -2.071   0.0384 *  
Box3          -0.236628   0.248939  -0.951   0.3418    
Box4           0.191712   0.248141   0.773   0.4398    
Trial         -0.038932   0.015401  -2.528   0.0115 *  
AgeGroup11-15 -0.413951   0.374989  -1.104   0.2696    
AgeGroup6-10  -0.116284   0.338754  -0.343   0.7314    
SexM           0.159609   0.306192   0.521   0.6022