破棒模型的趋势

Question

我正在使用折断的棒模型进行拟合，并想使用emtrends()提取断点之前和之后的斜率值。此处的代码是数据和分析的简化玩具版本。我不太清楚如何获得斜率-断点前后的值似乎相同。我究竟做错了什么？

library(ggplot2)
library(emmeans)

## toy data
df <- structure(list(Year = c(11, 11, 13, 13,  15,  15,  16,  16,  17, 
17, 18, 18, 14, 14), YearFac = structure(c(1L, 1L, 2L, 2L, 
4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 3L, 3L), .Label = c("11", 
"13", "14", "15", "16", "17", "18"), class = "factor"), Class = c("A", "B",    "A", 
"B", "A", "B", "A", "B", "A", "B", "A", "B", "A", "B"), Mean = c(3.5, 3.7,    3.7, 4.2, 3.7, 
4.5, 3.3, 4.9, 3.2, 5.8, 3.2, 6.3, NA, NA), YearPostTest = c(0, 0, 0, 
0, 1, 1, 2, 2, 3, 3, 4, 4, 0, 0)), row.names = c(3L, 4L, 5L, 7L, 8L, 
10L, 11L, 13L, 14L, 16L, 17L, 19L, 20L, 21L), class = "data.frame")

# breakpoint model
mod <- lm(Mean ~ Year + YearPostTest + Year:Class + 
                            YearPostTest:Class, data = df)

df$Pred <- predict(mod, newdata = df)
# plot data and predictions
ggplot(df) +
 geom_point(aes(x = Year, y = Mean, colour = Class)) +
 geom_line(aes(x = Year, y = Pred, colour = Class))

# make a new dataset with a few values - specifically, want to see slopes for A and for B 
# classes before and after breakpoint
new <- data.frame(YearPostTest = c(0, 1, 0, 1), 
   Year = c(13, 18, 13, 18), Class = c("A", "A", "B", "B")) 
emtrends(mod, ~Class|YearPostTest, var = "Year", data = new, 
   covnest = TRUE, cov.reduce = FALSE)

Answer 1

您的方法不起作用，因为斜率同时取决于Year和YearPostTest，并且在计算差商时> brok.line = function(x, knot) + cbind(x, (x > knot) * (x - knot)) > modmod = lm(Mean ~ brok.line(Year, 14) * Class, data = df) > emtrends(modmod, ~ Class | Year, var = "Year", data = new, cov.reduce = FALSE) Year = 13: Class Year.trend SE df lower.CL upper.CL A 0.0875 0.0893 6 -0.131 0.30593 B 0.0875 0.0893 6 -0.131 0.30593 Year = 18: Class Year.trend SE df lower.CL upper.CL A -0.1663 0.0662 6 -0.328 -0.00426 B 0.5487 0.0662 6 0.387 0.71074 Confidence level used: 0.95 保持不变。

最简单的方法是编写一个创建虚线的函数：

data

附录

要知道的另一件事是，指定at并不是> emtrends(modmod, ~ Class | Year, var = "Year", + at = list(Year = c(13, 18))) 规范的替代。我们可以通过

获得与上述完全相同的结果

cov.reduce = FALSE

它在您的示例中起作用的唯一原因是因为mod产生了相同的一组协变量值。但是，请注意，对于原始模型> summary(ref_grid(mod, data = new, cov.reduce = FALSE, nesting = NULL)) Year YearPostTest Class prediction SE df 13 0 A 3.68 0.1073 7 18 0 A 4.06 0.3458 7 13 1 A 3.44 0.0916 7 18 1 A 3.82 0.2512 7 13 0 B 3.96 0.1073 7 18 0 B 4.45 0.3458 7 13 1 B 4.41 0.0916 7 18 1 B 4.90 0.2512 7 ：

new

即使new只有4行，data数据集也产生了8个案例。这是因为参考网格包含预测因子级别的所有可能组合，而不仅仅是mod中出现的那些。

还有一件事

我注意到modmod和mod并不完全相同，因为Class排除了Class的主要影响。在这个特定的例子中，这种影响很小。但通常，您应该在模型中包括> year0 = data.frame(Year = c(0,0), YearPostTest = c(0,0), Class = c("A","B")) > predict(mod, newdata = year0) 1 2 2.68125 2.68125 > predict(modmod, newdata = year0) 1 2 2.54375 2.81875 ，因为否则您会假设两个类具有相同的截距：

map(res => res['List'])