如何繪製既有連續變量又有分類變量的二項式GLM預測

我有一個R中的二項式GLM，其中有幾個預測變量是連續的和分類的。如何繪製既有連續變量又有分類變量的二項式GLM預測

響應變量是「Presence」，它是二進制的（0/1）。長度是一個連續變量，而其他所有的都是絕對的。

我想繪製最終模型中每個變量的預測，特別是對於「長度」，但我有困難。

我的數據如下：

MyData<-structure(list(site = structure(c(3L, 1L, 3L, 2L, 1L, 4L, 3L, 
4L, 1L, 2L, 4L, 5L, 5L, 1L, 4L, 3L, 2L, 4L, 1L, 4L, 5L, 1L, 5L, 
4L, 3L, 1L, 3L, 5L, 5L, 4L, 4L, 3L, 1L, 5L, 1L, 3L, 1L, 4L, 4L, 
3L, 4L, 4L, 2L, 3L, 1L, 4L, 2L, 1L, 1L, 4L, 4L, 4L, 1L, 3L, 3L, 
2L, 1L, 4L, 2L, 5L, 5L, 3L, 3L, 2L, 5L, 2L, 4L, 5L, 2L, 4L, 4L, 
2L, 5L, 2L, 3L, 5L, 4L, 4L, 5L, 1L, 1L, 3L, 2L, 4L, 3L, 1L, 4L, 
3L, 1L, 4L, 3L, 3L, 4L, 5L, 1L, 3L, 2L, 3L, 2L, 3L, 2L, 1L, 1L, 
5L, 5L, 1L, 5L, 2L, 3L, 4L, 4L, 3L, 2L, 3L, 3L, 5L, 3L, 3L, 3L, 
5L, 1L, 5L, 2L, 3L, 4L, 5L, 5L, 1L, 4L, 2L, 5L, 3L, 2L, 5L, 4L, 
3L, 3L, 3L, 1L, 1L, 4L, 1L, 2L, 4L, 5L, 1L, 1L, 2L, 2L, 5L, 3L, 
4L, 4L, 1L, 5L, 2L, 4L, 3L, 1L, 1L, 3L, 2L, 1L, 3L, 4L, 3L, 1L, 
5L, 3L, 3L, 3L, 4L, 1L, 1L, 3L, 4L, 3L, 1L, 1L, 1L, 1L, 5L, 1L, 
3L, 4L, 3L, 2L, 1L, 1L, 2L, 5L, 2L, 1L, 5L, 3L, 1L, 4L, 1L, 3L, 
3L, 3L, 3L, 5L, 1L, 4L, 1L, 1L, 3L, 3L, 4L, 1L, 3L, 3L, 4L, 2L, 
5L, 5L, 5L, 1L, 4L, 4L, 3L, 1L, 2L, 3L, 1L, 3L, 1L, 1L, 4L, 3L, 
1L, 1L, 5L, 3L, 1L), .Label = c("R1a", "R1b", "R2", "Za", "Zb" 
), class = "factor"), species = structure(c(1L, 1L, 3L, 3L, 3L, 
1L, 3L, 1L, 4L, 3L, 1L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 
1L, 1L, 4L, 3L, 4L, 3L, 1L, 1L, 1L, 1L, 1L, 4L, 1L, 3L, 1L, 4L, 
3L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 1L, 1L, 1L, 
1L, 3L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 1L, 3L, 1L, 3L, 1L, 1L, 1L, 
1L, 1L, 2L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 
3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 1L, 3L, 3L, 4L, 3L, 1L, 
1L, 3L, 1L, 1L, 4L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 4L, 
1L, 3L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 4L, 3L, 1L, 1L, 3L, 
1L, 1L, 4L, 1L, 3L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 3L, 3L, 3L, 3L, 
1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 1L, 2L, 2L, 3L, 3L, 3L, 3L, 1L, 
3L, 1L, 4L, 3L, 1L, 4L, 1L, 1L, 3L, 1L, 1L, 3L, 1L, 1L, 3L, 3L, 
1L, 4L, 3L, 4L, 3L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 2L, 3L, 4L, 3L, 
1L, 1L, 4L, 1L, 1L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 2L, 4L, 3L, 3L, 
1L, 3L, 1L, 4L, 1L, 1L, 4L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 1L, 
1L, 1L, 3L, 1L, 1L, 1L, 3L), .Label = c("Monogyna", "Other", 
"Prunus", "Rosa"), class = "factor"), aspect = structure(c(4L, 
4L, 4L, 4L, 4L, 3L, 4L, 3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 3L, 4L, 
3L, 4L, 3L, 1L, 4L, 4L, 3L, 2L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L, 
4L, 4L, 2L, 4L, 3L, 3L, 1L, 3L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 
3L, 3L, 3L, 4L, 1L, 3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 3L, 4L, 1L, 
4L, 3L, 4L, 4L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 3L, 3L, 4L, 4L, 4L, 
2L, 4L, 3L, 3L, 4L, 3L, 4L, 4L, 3L, 4L, 3L, 3L, 4L, 4L, 3L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 1L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L, 
3L, 2L, 3L, 1L, 2L, 5L, 2L, 4L, 4L, 4L, 3L, 3L, 1L, 2L, 4L, 3L, 
4L, 4L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 1L, 
4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 4L, 
4L, 4L, 3L, 3L, 3L, 4L, 4L, 3L, 4L, 2L, 3L, 4L, 4L, 2L, 3L, 2L, 
4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 2L, 4L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 
3L, 4L, 2L, 5L, 3L, 4L, 2L, 4L, 4L, 4L, 3L, 3L, 3L, 4L, 4L, 2L, 
4L, 3L, 4L, 4L, 3L, 4L, 4L, 4L, 3L, 2L, 4L), .Label = c("East", 
"Flat", "North", "South", "West"), class = "factor"), length = c(260L, 
60L, 60L, 40L, 240L, 80L, 30L, 100L, 100L, 200L, 70L, 50L, 60L, 
35L, 120L, 60L, 500L, 40L, 20L, 70L, 250L, 80L, 50L, 130L, 350L, 
170L, 50L, 60L, 90L, 50L, 40L, 110L, 60L, 70L, 70L, 500L, 140L, 
50L, 50L, 360L, 50L, 150L, 60L, 270L, 280L, 130L, 130L, 50L, 
60L, 30L, 70L, 70L, 60L, 400L, 20L, 30L, 70L, 160L, 340L, 100L, 
210L, 60L, 70L, 130L, 50L, 40L, 50L, 80L, 390L, 40L, 110L, 130L, 
40L, 230L, 120L, 70L, 80L, 80L, 90L, 70L, 150L, 120L, 50L, 100L, 
120L, 10L, 40L, 80L, 180L, 160L, 200L, 40L, 70L, 90L, 50L, 40L, 
80L, 80L, 70L, 480L, 90L, 60L, 100L, 140L, 190L, 20L, 70L, 360L, 
70L, 130L, 60L, 50L, 320L, 210L, 130L, 180L, 90L, 20L, 300L, 
90L, 50L, 130L, 70L, 70L, 40L, 40L, 50L, 40L, 100L, 20L, 70L, 
100L, 340L, 70L, 110L, 40L, 230L, 200L, 80L, 35L, 110L, 200L, 
50L, 110L, 100L, 50L, 150L, 110L, 50L, 50L, 40L, 70L, 80L, 60L, 
100L, 90L, 40L, 300L, 140L, 180L, 140L, 40L, 190L, 100L, 170L, 
40L, 120L, 15L, 70L, 340L, 40L, 40L, 70L, 60L, 130L, 140L, 170L, 
120L, 90L, 130L, 210L, 50L, 180L, 120L, 100L, 50L, 90L, 70L, 
360L, 80L, 30L, 170L, 70L, 300L, 40L, 130L, 120L, 90L, 40L, 40L, 
140L, 80L, 400L, 70L, 80L, 60L, 420L, 320L, 200L, 40L, 50L, 70L, 
50L, 80L, 50L, 110L, 100L, 120L, 170L, 20L, 110L, 20L, 20L, 30L, 
30L, 90L, 150L, 80L, 40L, 90L, 300L, 30L, 70L, 50L, 90L, 200L 
), sun = structure(c(1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 
3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 
3L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 2L, 1L, 
1L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 3L, 
1L, 3L, 3L, 2L, 1L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 
2L, 1L, 1L, 3L, 3L, 1L, 1L, 1L, 3L, 2L, 1L, 3L, 1L, 1L, 3L, 3L, 
1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 1L, 
3L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 
3L, 1L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 
1L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 
3L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 
3L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 
1L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L, 
3L, 3L), .Label = c("Half", "Shade", "Sun"), class = "factor"), 
    leaf = structure(c(2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 
    2L, 4L, 4L, 4L, 2L, 2L, 2L, 4L, 4L, 2L, 2L, 4L, 2L, 2L, 1L, 
    2L, 2L, 4L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 
    2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 
    2L, 4L, 1L, 2L, 4L, 1L, 2L, 4L, 2L, 4L, 2L, 2L, 2L, 1L, 4L, 
    4L, 1L, 4L, 1L, 2L, 4L, 3L, 2L, 2L, 2L, 2L, 4L, 2L, 4L, 2L, 
    2L, 2L, 2L, 2L, 4L, 1L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 
    1L, 4L, 2L, 2L, 1L, 4L, 2L, 2L, 2L, 1L, 4L, 2L, 2L, 1L, 1L, 
    1L, 2L, 4L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 
    4L, 2L, 2L, 4L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 
    2L, 2L, 1L, 2L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 4L, 4L, 1L, 1L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 4L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 
    1L, 1L, 2L, 1L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 4L, 1L, 2L, 
    4L, 2L, 2L, 1L, 2L, 2L, 4L, 2L, 4L, 4L, 2L, 2L, 1L, 2L, 2L, 
    2L, 2L, 4L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 4L, 1L, 1L, 2L, 
    1L, 2L, 2L, 2L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 2L, 4L, 2L, 
    2L), .Label = c("Large", "Medium", "Scarce", "Small"), class = "factor"), 
    Presence = c(0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
    0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
    1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
    0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
    0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 
    0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
    1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 
    0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
    0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
    0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
    1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 0L 
    )), .Names = c("site", "species", "aspect", "length", "sun", 
"leaf", "Presence"), row.names = c(NA, 236L), class = "data.frame")

（注意，這是簡化的數據集，我已經刪除了選型過程中掉落的變量）

的最佳模式是：

model <- glm(Presence ~ site + species + aspect + length + sun 
       + leaf, data=MyData, family=binomial)

我嘗試以下，但它希望其他變量太多，所以我得到一個錯誤：

plot(MyData$length, MyData$Presence) 
mydat1 <- data.frame(length = seq(from = 10, to = 500, by = 1) 
pred1 <- predict(model, newdata = mydat1, type = "response") 
lines(MyData$length, pred1)

所以，我試圖指定所有的變量，但隨後只把一個水平線通過存在數據點（這意味着需要指定因子變量的我想所有可能的組合）：

plot(MyData$length, MyData$Presence) 
mydat2 <- data.frame(length = seq(from = 10, to = 500, by = 1), 
        site = "R1a", 
        species = "Monogyna", 
        aspect = "Flat", 
        sun = "Sun", 
        leaf = "Scarce") 
pred2 <- predict(model, newdata = mydat2, type = "response") 
lines(MyData$length, pred2)

最後，我想下面的代碼：

pred <- predict(model, type = "response") 
par(mfrow=c(2,2)) 
for(i in names(MyData)){ 
    plot(MyData[,i],pred,xlab=i, ylab="Probability") 
}

我被這最後一個困惑，因爲我無法獲得曲線，加上輸出讓我預測值甚至不是在最佳的模型變量。

我在這個模型下應該期待的是一個正弦曲線，我想。但那不是我得到的。

我怎樣才能產生一個有意義的預測情節？

任何幫助將不勝感激。

來源

2017-02-12 Tilen

我會使用effects包來獲得單個預測變量的更簡單的結果。這裏是如何：

library(effects) 
fit <- as.data.frame(effect('length', model, xlevels = 100))

繪圖容易（雖然注意overplotting）：

plot(MyData$length, MyData$Presence) 
lines(fit$length, fit$fit)

或者我們可以使用ggplot2：

library(ggplot2) 
ggplot() + 
    geom_count(aes(length, Presence), MyData) + 
    geom_line(aes(length, fit), fit, size = 1, col = 'red') + 
    geom_ribbon(aes(length, ymin = lower, ymax = upper), fit, alpha = 0.15) + 
    scale_size_area()

我們可以看到長度的影響並不是很大。

來源

2017-02-12 18:56:52 Axeman

謝謝@Axeman，非常感謝！雖然你會如何解釋這個情節？（我沒有在情節本身的術語的意思，但在關係到一個事實，即在模型中其他幾個變量因素）這是長給出了所有其他預測的平均值的效果？或者對於其他預測指標的基線水平......？另外，我不知道，如果你熟悉的第三張的代碼，我給了，但如果你是，你能看他有什麼評論？ – Tilen

除了@Axeman之外，別人還有其他想法嗎？ – Tilen

如何繪製既有連續變量又有分類變量的二項式GLM預測

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