Mag*_*and -1 r curvesmoothing curve-fitting best-fit-curve ggplot2
data <- dput(data): structure(list(x = 1:16, y = c(-79.62962963, -84.72222222, -88.42592593, -74.07407407, -29.62962963, 51.38888889, 79.62962963, 96.2962963, 87.96296296, 88.42592593, 73.14814815, 12.96296296, -63.42592593, -87.03703704, -87.5, -87.96296296)), .Names = c("x", "y"), row.names = c(NA, 16L), class = "data.frame")
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我在R中用ggplot2计算了我的数据集的平滑线:
p1 <- ggplot(data, aes(x=x(°), y=(%)))
library(splines)
library(MASS)
(p2 <- p1 + stat_smooth(method = "lm", formula = y ~ ns(x,3)) +
geom_point()
)
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如何计算平滑线曲线最大值的x值?
你需要对结果data.frame进行一些数学计算stat_smooth:
library(ggplot2)
library(splines)
library(MASS)
data <- structure(list(x = 1:16,
y = c(-79.62962963, -84.72222222, -88.42592593,
-74.07407407, -29.62962963, 51.38888889,
79.62962963, 96.2962963, 87.96296296,
88.42592593, 73.14814815, 12.96296296,
-63.42592593, -87.03703704, -87.5,
-87.96296296)), .Names = c("x", "y"),
row.names = c(NA, 16L), class = "data.frame")
p1 <- ggplot(data, aes(x=x, y=y))
p1 <- p1 + stat_smooth(method = "lm", formula = y ~ ns(x,3))
p1 <- p1 + geom_point()
p1
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gb <- ggplot_build(p1)
exact_x_value_of_the_curve_maximum <- gb$data[[1]]$x[which(diff(sign(diff(gb$data[[1]]$y)))==-2)+1]
p1 + geom_vline(xintercept=exact_x_value_of_the_curve_maximum)
exact_x_value_of_the_curve_maximum
[1] 9.164557
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还有更强大的方法,但您仍然需要该ggplot_build部分来获取数据.