Início / coding / Perguntas / 78052172
Accepted
Chris Ruehlemann
Asked: 2024-02-24 19:12:23 +0800 CST

Plotar modelo de efeitos mistos com preditor binário e variável de resposta binária

  • 772

Estou procurando uma maneira agradável e informativa de visualizar um modelo misto em que a variável de resposta e a variável preditora sejam binárias.

m_0 <- glmer(Preselected_0 ~ N_G_altnt_Q_YN + (N_G_altnt_Q_YN | File / Person_anon), family = "binomial", 
                    data = df)

O gráfico que recebo ao usar plot_modelé este:

library(sjPlot)

plot_model(m_0, type = "eff", terms = c("N_G_altnt_Q_YN"), #pred.type = "fe", ci.lvl = .68, line.size = 1.2,
           title = ""
)

insira a descrição da imagem aqui

O tipo de gráfico que eu gostaria de obter é este: ou, se isso não for possível ou aconselhável com o preditor binário, alguma outra visualização que seja visualmente mais atraente e informativa - qualquer ajuda será apreciada!

insira a descrição da imagem aqui

Dados:

df <- structure(list(N_G_altnt_Q_YN = 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, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 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, 2L, 1L, 1L, 1L, 
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 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, 2L, 1L, 1L, 1L, 
1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L), levels = c("0", "1"), class = "factor"), 
    Preselected_0 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 
    2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 
    2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 
    2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    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, 2L, 2L, 1L, 2L, 
    1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 
    2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 
    1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 
    2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L), levels = c("YES", "NO"
    ), class = "factor"), File = c("F01", "F01", "F01", "F01", 
    "F01", "F01", "F01", "F01", "F01", "F01", "F01", "F01", "F01", 
    "F01", "F01", "F01", "F01", "F01", "F01", "F01", "F01", "F01", 
    "F01", "F01", "F01", "F01", "F04", "F04", "F04", "F04", "F04", 
    "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", 
    "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", 
    "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", 
    "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", 
    "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", 
    "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", "F04", 
    "F04", "F04", "F04", "F04", "F04", "F07", "F07", "F07", "F07", 
    "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", 
    "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", 
    "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", 
    "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", 
    "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", "F07", 
    "F07", "F07", "F07", "F07", "F08", "F08", "F08", "F08", "F08", 
    "F08", "F08", "F08", "F08", "F08", "F08", "F08", "F08", "F08", 
    "F08", "F08", "F08", "F08", "F08", "F08", "F08", "F08", "F08", 
    "F08", "F08", "F08", "F08", "F08", "F08", "F08", "F08", "F08", 
    "F08", "F08", "F08", "F08", "F08", "F08", "F08", "F12", "F12", 
    "F12", "F12", "F12", "F12", "F12", "F12", "F12", "F12", "F12", 
    "F12", "F12", "F12", "F12", "F12", "F12", "F12", "F12", "F12", 
    "F12", "F12", "F12", "F12", "F12", "F12", "F12", "F12", "F16", 
    "F16", "F16", "F16", "F16", "F16", "F16", "F16", "F16", "F16", 
    "F16", "F16", "F16", "F16", "F16", "F16", "F16", "F16", "F16", 
    "F18", "F18", "F18", "F18", "F18", "F18", "F18", "F18", "F18", 
    "F18", "F18", "F18", "F18", "F18", "F18", "F18", "F18", "F18", 
    "F18", "F18", "F18", "F18", "F18", "F20", "F20", "F20", "F20", 
    "F20", "F20", "F20", "F20", "F20", "F20", "F20", "F20", "F20", 
    "F20", "F20", "F20", "F20", "F20", "F20", "F22", "F22", "F22", 
    "F22", "F22", "F22", "F22", "F22", "F22", "F22", "F23", "F23", 
    "F23", "F23", "F23", "F23", "F23", "F23", "F23", "F23", "F23", 
    "F23", "F23", "F23", "F23", "F23", "F23", "F23", "F23", "F23", 
    "F23", "F23", "F23", "F23", "F23", "F23", "F23", "F23", "F23", 
    "F23", "F23", "F23", "F23", "F23", "F23", "F23", "F23", "F23", 
    "F23", "F19", "F19", "F19", "F19", "F19", "F19", "F19", "F19", 
    "F19", "F19", "F19", "F16"), Person_anon = c("GGGGGGGGGGGGGGGl", 
    "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", 
    "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", "GGGGGGGGGGGGGGGl", "IIIIIIIIIIIIt", 
    "IIIIIIIIIIIIt", "KKKKKKKKKKr", "IIIIIIIIIIIIt", "KKKKKKKKKKr", 
    "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", "GGGGGGGGGGGGGGGl", 
    "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", "KKKKKKKKKKr", "IIIIIIIIIIIIt", 
    "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", "IIIIIIIIIIIIt", 
    "IIIIIIIIIIIIt", "DDDDDDDDDDDDe", "AAAAAAAAAAAAAn", "DDDDDDDDDDDDe", 
    "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", 
    "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "CCCCCCCCCCx", "CCCCCCCCCCx", 
    "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", 
    "DDDDDDDDDDDDe", "AAAAAAAAAAAAAn", "DDDDDDDDDDDDe", "CCCCCCCCCCx", 
    "CCCCCCCCCCx", "CCCCCCCCCCx", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", 
    "AAAAAAAAAAAAAn", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "AAAAAAAAAAAAAn", 
    "DDDDDDDDDDDDe", "CCCCCCCCCCx", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", 
    "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "CCCCCCCCCCx", "DDDDDDDDDDDDe", 
    "AAAAAAAAAAAAAn", "CCCCCCCCCCx", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", 
    "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "CCCCCCCCCCx", "CCCCCCCCCCx", 
    "CCCCCCCCCCx", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", 
    "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", 
    "CCCCCCCCCCx", "DDDDDDDDDDDDe", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", 
    "CCCCCCCCCCx", "AAAAAAAAAAAAAn", "CCCCCCCCCCx", "DDDDDDDDDDDDe", 
    "CCCCCCCCCCx", "DDDDDDDDDDDDe", "CCCCCCCCCCx", "DDDDDDDDDDDDe", 
    "CCCCCCCCCCx", "LLLLLLLLLLLLLn", "LLLLLLLLLLLLLn", "LLLLLLLLLLLLLn", 
    "CCCCCCCCCCx", "LLLLLLLLLLLLLn", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", 
    "AAAAAAAAAAAAAn", "LLLLLLLLLLLLLn", "LLLLLLLLLLLLLn", "AAAAAAAAAAAAAn", 
    "AAAAAAAAAAAAAn", "CCCCCCCCCCx", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", 
    "AAAAAAAAAAAAAn", "CCCCCCCCCCx", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", 
    "AAAAAAAAAAAAAn", "CCCCCCCCCCx", "AAAAAAAAAAAAAn", "LLLLLLLLLLLLLn", 
    "LLLLLLLLLLLLLn", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "LLLLLLLLLLLLLn", 
    "AAAAAAAAAAAAAn", "CCCCCCCCCCx", "CCCCCCCCCCx", "CCCCCCCCCCx", 
    "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "CCCCCCCCCCx", 
    "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", 
    "CCCCCCCCCCx", "CCCCCCCCCCx", "LLLLLLLLLLLLLn", "AAAAAAAAAAAAAn", 
    "CCCCCCCCCCx", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", 
    "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", "AAAAAAAAAAAAAn", 
    "AAAAAAAAAAAAAn", "LLLLLLLLLLLLLn", "LLLLLLLLLLLLLo", "LLLLLLLLLLLLLo", 
    "LLLLLLLLLLLLLn", "LLLLLLLLLLLLLn", "LLLLLLLLLLLLLo", "NNNNNNNNNNNr", 
    "NNNNNNNNNNNr", "LLLLLLLLLLLLLn", "LLLLLLLLLLLLLn", "NNNNNNNNNNNr", 
    "NNNNNNNNNNNr", "NNNNNNNNNNNr", "NNNNNNNNNNNr", "NNNNNNNNNNNr", 
    "LLLLLLLLLLLLLo", "LLLLLLLLLLLLLo", "NNNNNNNNNNNr", "LLLLLLLLLLLLLo", 
    "NNNNNNNNNNNr", "NNNNNNNNNNNr", "NNNNNNNNNNNr", "NNNNNNNNNNNr", 
    "NNNNNNNNNNNr", "LLLLLLLLLLLLLn", "LLLLLLLLLLLLLn", "NNNNNNNNNNNr", 
    "LLLLLLLLLLLLLo", "LLLLLLLLLLLLLo", "LLLLLLLLLLLLLo", "LLLLLLLLLLLLLn", 
    "LLLLLLLLLLLLLo", "NNNNNNNNNNNr", "NNNNNNNNNNNr", "LLLLLLLLLLLLLn", 
    "LLLLLLLLLLLLLn", "NNNNNNNNNNNr", "LLLLLLLLLLLLLn", "NNNNNNNNNNNr", 
    "NNNNNNNNNNNr", "LLLLLLLLLLLLLo", "LLLLLLLLLLLLLo", "CCCCCCCCCCx", 
    "LLLLLLLLLLLLLo", "DDDDDDDDDDDDe", "CCCCCCCCCCx", "LLLLLLLLLLLLLo", 
    "LLLLLLLLLLLLLo", "LLLLLLLLLLLLLo", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", 
    "DDDDDDDDDDDDe", "CCCCCCCCCCx", "DDDDDDDDDDDDe", "CCCCCCCCCCx", 
    "CCCCCCCCCCx", "CCCCCCCCCCx", "CCCCCCCCCCx", "DDDDDDDDDDDDe", 
    "CCCCCCCCCCx", "CCCCCCCCCCx", "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", 
    "DDDDDDDDDDDDe", "DDDDDDDDDDDDe", "LLLLLLLLLLLLLo", "DDDDDDDDDDDDe", 
    "DDDDDDDDDDDDe", "CCCCCCCCCCx", "CCCCCCCCCCx", "AAAAAAAAAAo", 
    "AAAAAAAAAAo", "AAAAAAAAAAo", "AAAAAAAAAAo", "AAAAAAAAAAo", 
    "AAAAAAAAAAo", "CCCCCCCCCCx", "CCCCCCCCCCCCCCx", "AAAAAAAAAAo", 
    "AAAAAAAAAAo", "AAAAAAAAAAo", "AAAAAAAAAAo", "AAAAAAAAAAo", 
    "AAAAAAAAAAo", "CCCCCCCCCCCCCCx", "AAAAAAAAAAo", "CCCCCCCCCCCCCCx", 
    "SSSSSSSSSSd", "SSSSSSSSSSd", "GGGGGGGGGGGGGi", "SSSSSSSSSSd", 
    "SSSSSSSSSSd", "AAAAAAAAAAo", "SSSSSSSSSSd", "SSSSSSSSSSd", 
    "AAAAAAAAAAo", "SSSSSSSSSSd", "AAAAAAAAAAo", "SSSSSSSSSSd", 
    "SSSSSSSSSSd", "SSSSSSSSSSd", "AAAAAAAAAAo", "SSSSSSSSSSd", 
    "GGGGGGGGGGGGGi", "GGGGGGGGGGGGGi", "GGGGGGGGGGGGGi", "GGGGGGGGGGGGGi", 
    "GGGGGGGGGGGGGi", "GGGGGGGGGGGGGi", "SSSSSSSSSSd", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", 
    "JJJJJJJJJJJJy", "JJJJJJJJJJJJy", "JJJJJJJJJJJJy", "JJJJJJJJJJJJJJJJJJd", 
    "JJJJJJJJJJJJJJJJJJd", "JJJJJJJJJJJJJJJJJJd", "JJJJJJJJJJJJy", 
    "JJJJJJJJJJJJJJJJJJd", "JJJJJJJJJJJJy", "JJJJJJJJJJJJy", 
    "LLLLLLLLLLn", "CCCCCCCCCCCCd", "CCCCCCCCCCCCd", "CCCCCCCCCCCCd", 
    "CCCCCCCCCCCCd", "CCCCCCCCCCCCd", "OOOOOOOOOOOOm", "OOOOOOOOOOOOm", 
    "LLLLLLLLLLn", "CCCCCCCCCCCCd", "OOOOOOOOOOOOm", "CCCCCCCCCCCCd", 
    "CCCCCCCCCCCCd", "LLLLLLLLLLn", "CCCCCCCCCCCCd", "CCCCCCCCCCCCd", 
    "CCCCCCCCCCCCd", "LLLLLLLLLLn", "LLLLLLLLLLn", "LLLLLLLLLLn", 
    "OOOOOOOOOOOOm", "CCCCCCCCCCCCd", "LLLLLLLLLLn", "CCCCCCCCCCCCd", 
    "CCCCCCCCCCCCd", "OOOOOOOOOOOOm", "CCCCCCCCCCCCd", "CCCCCCCCCCCCd", 
    "CCCCCCCCCCCCd", "CCCCCCCCCCCCd", "CCCCCCCCCCCCd", "CCCCCCCCCCCCd", 
    "CCCCCCCCCCCCd", "CCCCCCCCCCCCd", "LLLLLLLLLLn", "LLLLLLLLLLn", 
    "LLLLLLLLLLn", "LLLLLLLLLLn", "OOOOOOOOOOOOm", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", "LLLLLLLLLLLLLLLLLLLl", 
    "LLLLLLLLLLLLLLLLLLLl", "AAAAAAAAAAo")), row.names = c(NA, 
-332L), class = c("tbl_df", "tbl", "data.frame"))

1 respostas

  1. Best Answer

    Você pode fazer isso facilmente ajustando seu resultado categórico como um efeito fictício codificado 0/1 real. Isso é o que acontece de qualquer maneira dentro de qualquer rotina de ajuste, mas são os predictmétodos subsequentes que não saberão o que fazer com onde queremos ir:

    ## This is what the design matrix looks like inside glmer()
unique(model.matrix(~N_G_altnt_Q_YN, data=df))
#>  (Intercept) N_G_altnt_Q_YN1
#>          1               0
#>          1               1

## Manually code our effect to be 0/1 (numeric)
df$X <- as.integer(df$N_G_altnt_Q_YN == 1)

## Fit this numeric effect
m_1 <- glmer(Preselected_0 ~ X + (X | File / Person_anon),
             family = "binomial", data = df)

    Não vou mostrar isso aqui, mas isso não muda nada nas estimativas fixas ou aleatórias do seu modelo, simplesmente removeu o tipo de fator do preditor. Se você tivesse mais de dois níveis nesse fator, teria que criar mais manequins, mas o mesmo princípio se aplica (e, novamente, é isso que model.matrixacontecerá dentro de qualquer modelo).

    A grande vantagem é que agora podemos usar predictoutros valores além de 0/1 - mesmo fora desses limites, se você quiser, embora claramente isso não fizesse sentido.

    ## Helper function to back-transform log odds
expit <- function(x) 1/(1+exp(-x))

## Desired prediction range
x <- seq(0, 1, length.out = 2E2)

pred <- predict(m_1, data.frame(X = x), re.form = ~0, se.fit = TRUE) |>
   do.call(cbind, args = _) |>
   cbind(x) |>
   as.data.frame() |>
   dplyr::mutate(est = expit(fit),
                 ## Calculate Wald 95% confidence bounds
                 lower = expit(fit - se.fit*qnorm(.975)),
                 upper = expit(fit + se.fit*qnorm(.975)))

    Fizemos algumas coisas na última chamada: solicitamos médias previstas de efeito fixo e erros padrão em todo o intervalo de previsão e calculamos um intervalo de confiança de 95% do tipo Wald usando-os. Finalmente, tudo foi transformado da escala logarítmica de probabilidades para a escala de resposta (probabilidade). Você precisa executar essa etapa por último porque não pode calcular esse intervalo de confiança diretamente na escala de resposta.

    Agora só falta produzir um enredo. Vou me ater às rotinas básicas em vez de, por exemplo ggplot2:

    plot(c(0,1), c(0,1), type="n", xlab="", ylab="", xaxt="none")
axis(1, labels=c("no", "yes"), at=0:1)
polygon(c(x, rev(x)), c(pred$upper, rev(pred$lower)), col="grey90", lty=0)
lines(pred$x, pred$est, lwd=3)

    gráfico de previsão

    Algumas notas finais:

    • Você tem um aviso de convergência neste modelo. Presumo que sejam dados fictícios, mas é algo que você desejará investigar (talvez reduzir a complexidade do seu efeito aleatório).
    • Tenho certeza de que tais previsões fazem sentido, mas podem estar erradas, pois seriam a média em uma população dividida entre os dois resultados nessa proporção. Obviamente, no nível de observação individual, você só poderá ter um dos dois resultados.
    • 2

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