R：在ggplot中使轴一致
R: Making Axis Consistent in ggplot
我正在使用 R 编程语言。
我生成了一些随机数据并向数据添加了多项式回归线：
# PLOT 1
library(ggplot2)
x = rnorm(15, 2,2)
y = rnorm(15,7,2)
df = data.frame(x,y)
p <-ggplot(df, aes(x, y))
p <- p + geom_point(alpha=2/10, shape=21, fill="blue", colour="black", size=5)
#Add a loess smoother
p + stat_smooth(method="lm", se=TRUE, fill=NA, formula=y ~ poly(x, 6, raw=TRUE),colour="red") + ggtitle("Original Data: Polynomial Regression Model")
现在，我想向该数据添加一个异常值，重新拟合多项式回归并绘制数据：
# PLOT 2
x = rnorm(1,13,1)
y = rnorm(1, 13,1)
df_1 = data.frame(x,y)
df = rbind(df, df_1)
p <-ggplot(df, aes(x, y))
p <- p + geom_point(alpha=2/10, shape=21, fill="blue", colour="black", size=5)
#Add a loess smoother
p + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 6, raw=TRUE),colour="red") + ggtitle("Modified Data: Polynomial Regression Model")
我的问题：问题是，现在坐标轴已经变得太大了，以至于数据看起来像一条“扁线”：
我试图通过限制轴的大小来解决这个问题：
p + stat_smooth(method="lm", se=TRUE, fill=NA, formula=y ~ poly(x, 6, raw=TRUE),colour="red") + ggtitle("Modified Data: Polynomial Regression Model")+ scale_y_continuous(limits = c(min(df$y),max(df$y)))
但我现在收到以下警告消息：
Warning message:
Removed 35 rows containing missing values (geom_smooth).
我的问题：为什么当我尝试修复轴时行被删除？有没有更好的方法来解决这个问题？
谢谢！
当您拟合 stat_smooth()（或 geom_smooth()）曲线时，您实际上是在创建数据点，即您正在生成直线将遵循的坐标列表。当您更改 y 轴限制时，其中一些坐标最终会超出限制并被删除。因此，超出限制的不是您原来的 16 个点，而是 geom_smooth() 线的 'calculated' 坐标。
这是一个示例，显示了 stat_smooth() 在 ggplot 对象（“p2”）中创建的新 'internal' 数据：
library(ggplot2)
x = rnorm(15, 2,2)
y = rnorm(15,7,2)
df = data.frame(x,y)
p <- ggplot(df, aes(x, y)) +
geom_point(alpha=2/10, shape=21,
fill="blue", colour="black",
size=5) +
geom_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 6, raw=TRUE),
colour="red") +
ggtitle("Original Data: Polynomial Regression Model")
p
x = rnorm(1,13,1)
y = rnorm(1, 13,1)
df_1 = data.frame(x,y)
df = rbind(df, df_1)
p2 <-ggplot(df, aes(x, y)) +
geom_point(alpha=2/10, shape=21,
fill="blue", colour="black",
size=5) +
stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 6, raw=TRUE),
colour="red") +
ggtitle("Modified Data: Polynomial Regression Model") +
scale_y_continuous(limits = c(min(df$y),max(df$y)))
p2
#> Warning: Removed 37 rows containing missing values (geom_smooth).
ggplot_build(p2)$data[[2]]
#> x y ymin ymax se flipped_aes PANEL group
#> 1 -0.23985422 6.710141 NA NA 2.432118 FALSE 1 -1
#> 2 -0.07381912 6.765093 2.975488 10.554698 1.675217 FALSE 1 -1
#> 3 0.09221597 6.827052 3.150370 10.503735 1.625299 FALSE 1 -1
#> 4 0.25825106 6.891454 3.020561 10.762347 1.711151 FALSE 1 -1
#> 5 0.42428615 6.955375 3.107609 10.803142 1.700928 FALSE 1 -1
#> 6 0.59032125 7.017292 3.429403 10.605180 1.586047 FALSE 1 -1
#> 7 0.75635634 7.076844 3.875816 10.277873 1.415034 FALSE 1 -1
#> 8 0.92239143 7.134617 4.321137 9.948098 1.243716 FALSE 1 -1
#> 9 1.08842652 7.191933 4.663619 9.720246 1.117656 FALSE 1 -1
#> 10 1.25446161 7.250651 4.856861 9.644442 1.058189 FALSE 1 -1
#> 11 1.42049671 7.312991 4.923653 9.702330 1.056221 FALSE 1 -1
#> 12 1.58653180 7.381355 4.924840 9.837869 1.085917 FALSE 1 -1
#> 13 1.75256689 7.458168 4.912355 10.003980 1.125392 FALSE 1 -1
#> 14 1.91860198 7.545735 4.908758 10.182712 1.165691 FALSE 1 -1
#> 15 2.08463707 7.646104 4.911585 10.380623 1.208810 FALSE 1 -1
#> 16 2.25067217 7.760941 4.907110 10.614772 1.261553 FALSE 1 -1
#> 17 2.41670726 7.891421 4.884922 10.897920 1.329041 FALSE 1 -1
#> 18 2.58274235 8.038130 4.847749 11.228511 1.410327 FALSE 1 -1
#> 19 2.74877744 8.200977 4.813006 NA 1.497673 FALSE 1 -1
#> 20 2.91481253 8.379121 4.807386 NA 1.578907 FALSE 1 -1
#> 21 3.08084763 8.570910 4.858931 NA 1.640902 FALSE 1 -1
#> 22 3.24688272 8.773830 4.989796 NA 1.672755 FALSE 1 -1
#> 23 3.41291781 8.984466 5.210253 NA 1.668413 FALSE 1 -1
#> 24 3.57895290 9.198480 5.512992 NA 1.629192 FALSE 1 -1
#> 25 3.74498800 9.410595 5.866693 NA 1.566603 FALSE 1 -1
#> 26 3.91102309 9.614597 6.209727 NA 1.505143 FALSE 1 -1
#> 27 4.07705818 9.803344 6.451282 NA 1.481799 FALSE 1 -1
#> 28 4.24309327 9.968788 6.496294 NA 1.535037 FALSE 1 -1
#> 29 4.40912836 10.102016 6.295586 NA 1.682655 FALSE 1 -1
#> 30 4.57516346 10.193293 5.875238 NA 1.908822 FALSE 1 -1
#> 31 4.74119855 10.232124 5.314615 NA 2.173814 FALSE 1 -1
#> 32 4.90723364 10.207325 4.710764 NA 2.429787 FALSE 1 -1
#> 33 5.07326873 10.107110 4.159409 NA 2.629216 FALSE 1 -1
#> 34 5.23930382 9.919186 3.745953 NA 2.728914 FALSE 1 -1
#> 35 5.40533892 9.630862 3.530453 NA 2.696722 FALSE 1 -1
#> 36 5.57137401 9.229169 3.502837 NA 2.531359 FALSE 1 -1
#> 37 5.73740910 8.700996 3.447417 NA 2.322376 FALSE 1 -1
#> 38 5.90344419 8.033234 NA NA 2.378236 FALSE 1 -1
#> 39 6.06947929 7.212932 NA NA 3.159180 FALSE 1 -1
#> 40 6.23551438 6.227470 NA NA 4.805205 FALSE 1 -1
#> 41 6.40154947 5.064739 NA NA 7.234080 FALSE 1 -1
#> 42 6.56758456 3.713337 NA NA 10.414227 FALSE 1 -1
#> 43 6.73361965 NA NA NA 14.368943 FALSE 1 -1
#> 44 6.89965475 NA NA NA 19.144526 FALSE 1 -1
#> 45 7.06568984 NA NA NA 24.794757 FALSE 1 -1
#> 46 7.23172493 NA NA NA 31.373858 FALSE 1 -1
#> 47 7.39776002 NA NA NA 38.932662 FALSE 1 -1
#> 48 7.56379511 NA NA NA 47.516027 FALSE 1 -1
#> 49 7.72983021 NA NA NA 57.160715 FALSE 1 -1
#> 50 7.89586530 NA NA NA 67.893447 FALSE 1 -1
#> 51 8.06190039 NA NA NA 79.728997 FALSE 1 -1
#> 52 8.22793548 NA NA NA 92.668271 FALSE 1 -1
#> 53 8.39397057 NA NA NA 106.696330 FALSE 1 -1
#> 54 8.56000567 NA NA NA 121.780347 FALSE 1 -1
#> 55 8.72604076 NA NA NA 137.867491 FALSE 1 -1
#> 56 8.89207585 NA NA NA 154.882727 FALSE 1 -1
#> 57 9.05811094 NA NA NA 172.726539 FALSE 1 -1
#> 58 9.22414604 NA NA NA 191.272557 FALSE 1 -1
#> 59 9.39018113 NA NA NA 210.365110 FALSE 1 -1
#> 60 9.55621622 NA NA NA 229.816685 FALSE 1 -1
#> 61 9.72225131 NA NA NA 249.405299 FALSE 1 -1
#> 62 9.88828640 NA NA NA 268.871784 FALSE 1 -1
#> 63 10.05432150 NA NA NA 287.916987 FALSE 1 -1
#> 64 10.22035659 NA NA NA 306.198879 FALSE 1 -1
#> 65 10.38639168 NA NA NA 323.329573 FALSE 1 -1
#> 66 10.55242677 NA NA NA 338.872263 FALSE 1 -1
#> 67 10.71846186 NA NA NA 352.338062 FALSE 1 -1
#> 68 10.88449696 NA NA NA 363.182766 FALSE 1 -1
#> 69 11.05053205 NA NA NA 370.803517 FALSE 1 -1
#> 70 11.21656714 NA NA NA 374.535386 FALSE 1 -1
#> 71 11.38260223 NA NA NA 373.647868 FALSE 1 -1
#> 72 11.54863733 NA NA NA 367.341284 FALSE 1 -1
#> 73 11.71467242 NA NA NA 354.743103 FALSE 1 -1
#> 74 11.88070751 NA NA NA 334.904184 FALSE 1 -1
#> 75 12.04674260 NA NA NA 306.794933 FALSE 1 -1
#> 76 12.21277769 NA NA NA 269.301428 FALSE 1 -1
#> 77 12.37881279 NA NA NA 221.221573 FALSE 1 -1
#> 78 12.54484788 NA NA NA 161.261789 FALSE 1 -1
#> 79 12.71088297 NA NA NA 88.038458 FALSE 1 -1
#> 80 12.87691806 11.047284 5.267979 NA 2.554776 FALSE 1 -1
#> colour fill size linetype weight alpha
#> 1 red NA 1 1 1 0.4
#> 2 red NA 1 1 1 0.4
#> 3 red NA 1 1 1 0.4
#> 4 red NA 1 1 1 0.4
#> 5 red NA 1 1 1 0.4
#> 6 red NA 1 1 1 0.4
#> 7 red NA 1 1 1 0.4
#> 8 red NA 1 1 1 0.4
#> 9 red NA 1 1 1 0.4
#> 10 red NA 1 1 1 0.4
#> 11 red NA 1 1 1 0.4
#> 12 red NA 1 1 1 0.4
#> 13 red NA 1 1 1 0.4
#> 14 red NA 1 1 1 0.4
#> 15 red NA 1 1 1 0.4
#> 16 red NA 1 1 1 0.4
#> 17 red NA 1 1 1 0.4
#> 18 red NA 1 1 1 0.4
#> 19 red NA 1 1 1 0.4
#> 20 red NA 1 1 1 0.4
#> 21 red NA 1 1 1 0.4
#> 22 red NA 1 1 1 0.4
#> 23 red NA 1 1 1 0.4
#> 24 red NA 1 1 1 0.4
#> 25 red NA 1 1 1 0.4
#> 26 red NA 1 1 1 0.4
#> 27 red NA 1 1 1 0.4
#> 28 red NA 1 1 1 0.4
#> 29 red NA 1 1 1 0.4
#> 30 red NA 1 1 1 0.4
#> 31 red NA 1 1 1 0.4
#> 32 red NA 1 1 1 0.4
#> 33 red NA 1 1 1 0.4
#> 34 red NA 1 1 1 0.4
#> 35 red NA 1 1 1 0.4
#> 36 red NA 1 1 1 0.4
#> 37 red NA 1 1 1 0.4
#> 38 red NA 1 1 1 0.4
#> 39 red NA 1 1 1 0.4
#> 40 red NA 1 1 1 0.4
#> 41 red NA 1 1 1 0.4
#> 42 red NA 1 1 1 0.4
#> 43 red NA 1 1 1 0.4
#> 44 red NA 1 1 1 0.4
#> 45 red NA 1 1 1 0.4
#> 46 red NA 1 1 1 0.4
#> 47 red NA 1 1 1 0.4
#> 48 red NA 1 1 1 0.4
#> 49 red NA 1 1 1 0.4
#> 50 red NA 1 1 1 0.4
#> 51 red NA 1 1 1 0.4
#> 52 red NA 1 1 1 0.4
#> 53 red NA 1 1 1 0.4
#> 54 red NA 1 1 1 0.4
#> 55 red NA 1 1 1 0.4
#> 56 red NA 1 1 1 0.4
#> 57 red NA 1 1 1 0.4
#> 58 red NA 1 1 1 0.4
#> 59 red NA 1 1 1 0.4
#> 60 red NA 1 1 1 0.4
#> 61 red NA 1 1 1 0.4
#> 62 red NA 1 1 1 0.4
#> 63 red NA 1 1 1 0.4
#> 64 red NA 1 1 1 0.4
#> 65 red NA 1 1 1 0.4
#> 66 red NA 1 1 1 0.4
#> 67 red NA 1 1 1 0.4
#> 68 red NA 1 1 1 0.4
#> 69 red NA 1 1 1 0.4
#> 70 red NA 1 1 1 0.4
#> 71 red NA 1 1 1 0.4
#> 72 red NA 1 1 1 0.4
#> 73 red NA 1 1 1 0.4
#> 74 red NA 1 1 1 0.4
#> 75 red NA 1 1 1 0.4
#> 76 red NA 1 1 1 0.4
#> 77 red NA 1 1 1 0.4
#> 78 red NA 1 1 1 0.4
#> 79 red NA 1 1 1 0.4
#> 80 red NA 1 1 1 0.4
由 reprex package (v2.0.1)
创建于 2022-03-17
问题是您的模型具有非常宽的置信区间。您通过设置 fill = NA 使置信带不可见，但它仍然“存在”并扩展了 y 轴限制。
您试图通过限制范围来“解决”这个问题，但最终也切断了回归线的一部分。相反，通过设置 se = FALSE:
关闭置信带
p <- p + geom_point(alpha=2/10, shape=21, fill="blue", colour="black", size=5)
p + stat_smooth(
method="lm",
se=FALSE,
formula=y ~ poly(x, 6, raw=TRUE),
colour="red"
) +
ggtitle("Modified Data: Polynomial Regression Model")
我正在使用 R 编程语言。
我生成了一些随机数据并向数据添加了多项式回归线：
# PLOT 1
library(ggplot2)
x = rnorm(15, 2,2)
y = rnorm(15,7,2)
df = data.frame(x,y)
p <-ggplot(df, aes(x, y))
p <- p + geom_point(alpha=2/10, shape=21, fill="blue", colour="black", size=5)
#Add a loess smoother
p + stat_smooth(method="lm", se=TRUE, fill=NA, formula=y ~ poly(x, 6, raw=TRUE),colour="red") + ggtitle("Original Data: Polynomial Regression Model")
现在，我想向该数据添加一个异常值，重新拟合多项式回归并绘制数据：
# PLOT 2
x = rnorm(1,13,1)
y = rnorm(1, 13,1)
df_1 = data.frame(x,y)
df = rbind(df, df_1)
p <-ggplot(df, aes(x, y))
p <- p + geom_point(alpha=2/10, shape=21, fill="blue", colour="black", size=5)
#Add a loess smoother
p + stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 6, raw=TRUE),colour="red") + ggtitle("Modified Data: Polynomial Regression Model")
我的问题：问题是，现在坐标轴已经变得太大了，以至于数据看起来像一条“扁线”：
我试图通过限制轴的大小来解决这个问题：
p + stat_smooth(method="lm", se=TRUE, fill=NA, formula=y ~ poly(x, 6, raw=TRUE),colour="red") + ggtitle("Modified Data: Polynomial Regression Model")+ scale_y_continuous(limits = c(min(df$y),max(df$y)))
但我现在收到以下警告消息：
Warning message:
Removed 35 rows containing missing values (geom_smooth).
我的问题：为什么当我尝试修复轴时行被删除？有没有更好的方法来解决这个问题？
谢谢！
当您拟合 stat_smooth()（或 geom_smooth()）曲线时，您实际上是在创建数据点，即您正在生成直线将遵循的坐标列表。当您更改 y 轴限制时，其中一些坐标最终会超出限制并被删除。因此，超出限制的不是您原来的 16 个点，而是 geom_smooth() 线的 'calculated' 坐标。
这是一个示例，显示了 stat_smooth() 在 ggplot 对象（“p2”）中创建的新 'internal' 数据：
library(ggplot2)
x = rnorm(15, 2,2)
y = rnorm(15,7,2)
df = data.frame(x,y)
p <- ggplot(df, aes(x, y)) +
geom_point(alpha=2/10, shape=21,
fill="blue", colour="black",
size=5) +
geom_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 6, raw=TRUE),
colour="red") +
ggtitle("Original Data: Polynomial Regression Model")
p
x = rnorm(1,13,1)
y = rnorm(1, 13,1)
df_1 = data.frame(x,y)
df = rbind(df, df_1)
p2 <-ggplot(df, aes(x, y)) +
geom_point(alpha=2/10, shape=21,
fill="blue", colour="black",
size=5) +
stat_smooth(method="lm", se=TRUE, fill=NA,
formula=y ~ poly(x, 6, raw=TRUE),
colour="red") +
ggtitle("Modified Data: Polynomial Regression Model") +
scale_y_continuous(limits = c(min(df$y),max(df$y)))
p2
#> Warning: Removed 37 rows containing missing values (geom_smooth).
ggplot_build(p2)$data[[2]]
#> x y ymin ymax se flipped_aes PANEL group
#> 1 -0.23985422 6.710141 NA NA 2.432118 FALSE 1 -1
#> 2 -0.07381912 6.765093 2.975488 10.554698 1.675217 FALSE 1 -1
#> 3 0.09221597 6.827052 3.150370 10.503735 1.625299 FALSE 1 -1
#> 4 0.25825106 6.891454 3.020561 10.762347 1.711151 FALSE 1 -1
#> 5 0.42428615 6.955375 3.107609 10.803142 1.700928 FALSE 1 -1
#> 6 0.59032125 7.017292 3.429403 10.605180 1.586047 FALSE 1 -1
#> 7 0.75635634 7.076844 3.875816 10.277873 1.415034 FALSE 1 -1
#> 8 0.92239143 7.134617 4.321137 9.948098 1.243716 FALSE 1 -1
#> 9 1.08842652 7.191933 4.663619 9.720246 1.117656 FALSE 1 -1
#> 10 1.25446161 7.250651 4.856861 9.644442 1.058189 FALSE 1 -1
#> 11 1.42049671 7.312991 4.923653 9.702330 1.056221 FALSE 1 -1
#> 12 1.58653180 7.381355 4.924840 9.837869 1.085917 FALSE 1 -1
#> 13 1.75256689 7.458168 4.912355 10.003980 1.125392 FALSE 1 -1
#> 14 1.91860198 7.545735 4.908758 10.182712 1.165691 FALSE 1 -1
#> 15 2.08463707 7.646104 4.911585 10.380623 1.208810 FALSE 1 -1
#> 16 2.25067217 7.760941 4.907110 10.614772 1.261553 FALSE 1 -1
#> 17 2.41670726 7.891421 4.884922 10.897920 1.329041 FALSE 1 -1
#> 18 2.58274235 8.038130 4.847749 11.228511 1.410327 FALSE 1 -1
#> 19 2.74877744 8.200977 4.813006 NA 1.497673 FALSE 1 -1
#> 20 2.91481253 8.379121 4.807386 NA 1.578907 FALSE 1 -1
#> 21 3.08084763 8.570910 4.858931 NA 1.640902 FALSE 1 -1
#> 22 3.24688272 8.773830 4.989796 NA 1.672755 FALSE 1 -1
#> 23 3.41291781 8.984466 5.210253 NA 1.668413 FALSE 1 -1
#> 24 3.57895290 9.198480 5.512992 NA 1.629192 FALSE 1 -1
#> 25 3.74498800 9.410595 5.866693 NA 1.566603 FALSE 1 -1
#> 26 3.91102309 9.614597 6.209727 NA 1.505143 FALSE 1 -1
#> 27 4.07705818 9.803344 6.451282 NA 1.481799 FALSE 1 -1
#> 28 4.24309327 9.968788 6.496294 NA 1.535037 FALSE 1 -1
#> 29 4.40912836 10.102016 6.295586 NA 1.682655 FALSE 1 -1
#> 30 4.57516346 10.193293 5.875238 NA 1.908822 FALSE 1 -1
#> 31 4.74119855 10.232124 5.314615 NA 2.173814 FALSE 1 -1
#> 32 4.90723364 10.207325 4.710764 NA 2.429787 FALSE 1 -1
#> 33 5.07326873 10.107110 4.159409 NA 2.629216 FALSE 1 -1
#> 34 5.23930382 9.919186 3.745953 NA 2.728914 FALSE 1 -1
#> 35 5.40533892 9.630862 3.530453 NA 2.696722 FALSE 1 -1
#> 36 5.57137401 9.229169 3.502837 NA 2.531359 FALSE 1 -1
#> 37 5.73740910 8.700996 3.447417 NA 2.322376 FALSE 1 -1
#> 38 5.90344419 8.033234 NA NA 2.378236 FALSE 1 -1
#> 39 6.06947929 7.212932 NA NA 3.159180 FALSE 1 -1
#> 40 6.23551438 6.227470 NA NA 4.805205 FALSE 1 -1
#> 41 6.40154947 5.064739 NA NA 7.234080 FALSE 1 -1
#> 42 6.56758456 3.713337 NA NA 10.414227 FALSE 1 -1
#> 43 6.73361965 NA NA NA 14.368943 FALSE 1 -1
#> 44 6.89965475 NA NA NA 19.144526 FALSE 1 -1
#> 45 7.06568984 NA NA NA 24.794757 FALSE 1 -1
#> 46 7.23172493 NA NA NA 31.373858 FALSE 1 -1
#> 47 7.39776002 NA NA NA 38.932662 FALSE 1 -1
#> 48 7.56379511 NA NA NA 47.516027 FALSE 1 -1
#> 49 7.72983021 NA NA NA 57.160715 FALSE 1 -1
#> 50 7.89586530 NA NA NA 67.893447 FALSE 1 -1
#> 51 8.06190039 NA NA NA 79.728997 FALSE 1 -1
#> 52 8.22793548 NA NA NA 92.668271 FALSE 1 -1
#> 53 8.39397057 NA NA NA 106.696330 FALSE 1 -1
#> 54 8.56000567 NA NA NA 121.780347 FALSE 1 -1
#> 55 8.72604076 NA NA NA 137.867491 FALSE 1 -1
#> 56 8.89207585 NA NA NA 154.882727 FALSE 1 -1
#> 57 9.05811094 NA NA NA 172.726539 FALSE 1 -1
#> 58 9.22414604 NA NA NA 191.272557 FALSE 1 -1
#> 59 9.39018113 NA NA NA 210.365110 FALSE 1 -1
#> 60 9.55621622 NA NA NA 229.816685 FALSE 1 -1
#> 61 9.72225131 NA NA NA 249.405299 FALSE 1 -1
#> 62 9.88828640 NA NA NA 268.871784 FALSE 1 -1
#> 63 10.05432150 NA NA NA 287.916987 FALSE 1 -1
#> 64 10.22035659 NA NA NA 306.198879 FALSE 1 -1
#> 65 10.38639168 NA NA NA 323.329573 FALSE 1 -1
#> 66 10.55242677 NA NA NA 338.872263 FALSE 1 -1
#> 67 10.71846186 NA NA NA 352.338062 FALSE 1 -1
#> 68 10.88449696 NA NA NA 363.182766 FALSE 1 -1
#> 69 11.05053205 NA NA NA 370.803517 FALSE 1 -1
#> 70 11.21656714 NA NA NA 374.535386 FALSE 1 -1
#> 71 11.38260223 NA NA NA 373.647868 FALSE 1 -1
#> 72 11.54863733 NA NA NA 367.341284 FALSE 1 -1
#> 73 11.71467242 NA NA NA 354.743103 FALSE 1 -1
#> 74 11.88070751 NA NA NA 334.904184 FALSE 1 -1
#> 75 12.04674260 NA NA NA 306.794933 FALSE 1 -1
#> 76 12.21277769 NA NA NA 269.301428 FALSE 1 -1
#> 77 12.37881279 NA NA NA 221.221573 FALSE 1 -1
#> 78 12.54484788 NA NA NA 161.261789 FALSE 1 -1
#> 79 12.71088297 NA NA NA 88.038458 FALSE 1 -1
#> 80 12.87691806 11.047284 5.267979 NA 2.554776 FALSE 1 -1
#> colour fill size linetype weight alpha
#> 1 red NA 1 1 1 0.4
#> 2 red NA 1 1 1 0.4
#> 3 red NA 1 1 1 0.4
#> 4 red NA 1 1 1 0.4
#> 5 red NA 1 1 1 0.4
#> 6 red NA 1 1 1 0.4
#> 7 red NA 1 1 1 0.4
#> 8 red NA 1 1 1 0.4
#> 9 red NA 1 1 1 0.4
#> 10 red NA 1 1 1 0.4
#> 11 red NA 1 1 1 0.4
#> 12 red NA 1 1 1 0.4
#> 13 red NA 1 1 1 0.4
#> 14 red NA 1 1 1 0.4
#> 15 red NA 1 1 1 0.4
#> 16 red NA 1 1 1 0.4
#> 17 red NA 1 1 1 0.4
#> 18 red NA 1 1 1 0.4
#> 19 red NA 1 1 1 0.4
#> 20 red NA 1 1 1 0.4
#> 21 red NA 1 1 1 0.4
#> 22 red NA 1 1 1 0.4
#> 23 red NA 1 1 1 0.4
#> 24 red NA 1 1 1 0.4
#> 25 red NA 1 1 1 0.4
#> 26 red NA 1 1 1 0.4
#> 27 red NA 1 1 1 0.4
#> 28 red NA 1 1 1 0.4
#> 29 red NA 1 1 1 0.4
#> 30 red NA 1 1 1 0.4
#> 31 red NA 1 1 1 0.4
#> 32 red NA 1 1 1 0.4
#> 33 red NA 1 1 1 0.4
#> 34 red NA 1 1 1 0.4
#> 35 red NA 1 1 1 0.4
#> 36 red NA 1 1 1 0.4
#> 37 red NA 1 1 1 0.4
#> 38 red NA 1 1 1 0.4
#> 39 red NA 1 1 1 0.4
#> 40 red NA 1 1 1 0.4
#> 41 red NA 1 1 1 0.4
#> 42 red NA 1 1 1 0.4
#> 43 red NA 1 1 1 0.4
#> 44 red NA 1 1 1 0.4
#> 45 red NA 1 1 1 0.4
#> 46 red NA 1 1 1 0.4
#> 47 red NA 1 1 1 0.4
#> 48 red NA 1 1 1 0.4
#> 49 red NA 1 1 1 0.4
#> 50 red NA 1 1 1 0.4
#> 51 red NA 1 1 1 0.4
#> 52 red NA 1 1 1 0.4
#> 53 red NA 1 1 1 0.4
#> 54 red NA 1 1 1 0.4
#> 55 red NA 1 1 1 0.4
#> 56 red NA 1 1 1 0.4
#> 57 red NA 1 1 1 0.4
#> 58 red NA 1 1 1 0.4
#> 59 red NA 1 1 1 0.4
#> 60 red NA 1 1 1 0.4
#> 61 red NA 1 1 1 0.4
#> 62 red NA 1 1 1 0.4
#> 63 red NA 1 1 1 0.4
#> 64 red NA 1 1 1 0.4
#> 65 red NA 1 1 1 0.4
#> 66 red NA 1 1 1 0.4
#> 67 red NA 1 1 1 0.4
#> 68 red NA 1 1 1 0.4
#> 69 red NA 1 1 1 0.4
#> 70 red NA 1 1 1 0.4
#> 71 red NA 1 1 1 0.4
#> 72 red NA 1 1 1 0.4
#> 73 red NA 1 1 1 0.4
#> 74 red NA 1 1 1 0.4
#> 75 red NA 1 1 1 0.4
#> 76 red NA 1 1 1 0.4
#> 77 red NA 1 1 1 0.4
#> 78 red NA 1 1 1 0.4
#> 79 red NA 1 1 1 0.4
#> 80 red NA 1 1 1 0.4
由 reprex package (v2.0.1)
创建于 2022-03-17问题是您的模型具有非常宽的置信区间。您通过设置 fill = NA 使置信带不可见，但它仍然“存在”并扩展了 y 轴限制。
您试图通过限制范围来“解决”这个问题，但最终也切断了回归线的一部分。相反，通过设置 se = FALSE:
p <- p + geom_point(alpha=2/10, shape=21, fill="blue", colour="black", size=5)
p + stat_smooth(
method="lm",
se=FALSE,
formula=y ~ poly(x, 6, raw=TRUE),
colour="red"
) +
ggtitle("Modified Data: Polynomial Regression Model")