在R中绘制回归输出时指定不同组的端点

Question

此问题是交叉验证的migrated，因为可以在堆栈溢出中回答。Migrated<< [12小时前。

我希望能为我的硕士学位论文提供一些回归输出方面的帮助。我正在评估大象对木质植被的影响，尤其是与人工水坑有关的影响。除了随着距水坑距离的增加而普遍减少外，所涉及的两种植被类型之间的影响也大不相同。

我想出了一种使用visreg绘制此图的令人满意的方法。在下面显示的模型输出中，到水坑的距离和蔬菜类型都说明了损坏，因此我尝试同时显示两者。但是，问题在于，我只有距离红色植被类型最远的水坑（x轴）距离有样本。如您所见，蓝色蔬菜类型的回归线超出了该植被类型的最后一点。无论如何，我可以让蓝线停在距水坑的距离（x轴值）比红线更短的距离处来避免这种情况吗？

请参见模型代码，并在visreg图下方绘制图。

样本数据和代码

> dput(vegdata[21:52, c(4,7,33)])
structure(list(distance = c(207L, 202L, 501L, 502L, 1001L, 1004L, 
2010L, 1997L, 4003L, 3998L, 202L, 194L, 499L, 494L, 1004L, 1000L, 
2008L, 1993L, 4008L, 3998L, 493L, 992L, 1941L, 2525L, 485L, 978L, 
1941L, 3024L, 495L, 978L, 1977L, 2952L), vegtype = structure(c(1L, 
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label =  c("teak", 
"term"), class = "factor"), toedl = c(35.48031025, 47.30482718, 
25.16709533, 22.29360164, 17.6546533, 12.81605101, 20.34136734, 
18.45809334, 11.3578081, 3.490830751, 60.54870317, 44.9863128, 
18.81010698, 20.4777188, 30.36994386, 18.7417214, 21.52247156, 
18.29685939, 30.26217664, 8.945486104, 43.95749178, 43.54799495, 
44.42693993, 50.06207783, 48.05538594, 35.31220933, 52.37339094, 
40.51569938, 41.45677007, 58.86629306, 37.80203313, 46.35633342
)), row.names = 21:52, class = "data.frame")

m1<-lm(toedl~vegtype+distance, data=vegdata)
summary(m1)

library(visreg)
visreg(oedl6, 'sexactd', by='vegtype',overlay=TRUE, gg=TRUE,    points=list(size=2.5), ylab='% old elephant damage', xlab='distance from waterhole')

Answer 1

library(ggplot2)
library(visreg)

# Create reproducible data example
allData <- data.frame(vegtype = rep(c("t1", "t2"), each = 10),
                      oedl = c(seq(from = 35, to = 20, length.out = 10),
                               seq(from = 20, to = 5, length.out = 10)),
                      sexactd = c(seq(from = -1, to = 1, length.out = 10),
                                  seq(from = -1, to = 2, length.out = 10)))

# Make linear model
oedl6 <- lm(formula = oedl ~ sexactd + vegtype, data = allData)

# Predict the data using the linear model
odelPred <- cbind(allData, predict(oedl6, interval = 'confidence'))

ggplot(odelPred, aes(sexactd, oedl, color = vegtype, fill = vegtype)) +
  geom_point() + geom_line(aes(sexactd, fit)) + 
  geom_ribbon(aes(ymin = lwr, ymax = upr), alpha = 0.3)

Answer 2

library(tidyverse)
ggplot(vegdata, aes(toedl, distance, color = vegtype)) + 
  geom_point() +
  geom_smooth(method = 'lm')

由reprex package（v0.3.0）在2019-12-13创建