在ggplot

Question

我在R中绘制了散点图，使用以下脚本比较预期值和观察值：

library(ggplot2)
library(dplyr)


r<-read_csv("Uni/MSci/Project/DATA/new data sheets/comparisons/for comarison 
graphs/R Regression/GAcAs.csv")
x<-r[1]
y<-r[2]

ggplot()+geom_point(aes(x=x,y=y))+ 
scale_size_area() + 
xlab("Expected") +
ylab("Observed") +
ggtitle("G - As x Ac")+ xlim(0, 40)+ylim(0, 40)

我的情节如下：

然后我想添加一个正交回归线（因为预期值和观察值都可能存在误差）。我使用以下方法计算了beta值：

v <- prcomp(cbind(x,y))$rotation
beta <- v[2,1]/v[1,1]

有没有办法在我的情节中添加正交回归线？

Answer 1

我不确定我是否完全理解这个问题，但如果您希望线段沿x轴和y轴显示错误，则可以使用geom_segment执行此操作。

这样的事情：

library(ggplot2)

df <- data.frame(x = rnorm(10), y = rnorm(10), w = rnorm(10, sd=.1))

ggplot(df, aes(x = x, y = y, xend = x, yend = y)) +
    geom_point() +
    geom_segment(aes(x = x - w, xend = x + w)) +
    geom_segment(aes(y = y - w, yend = y + w))

Answer 2

借鉴blog post＆amp;这answer。基本上，您需要Deming的{​​{1}}功能或MethComp prcomp个stats功能以及自定义功能perp.segment.coord。以下是从上述博客文章中获取的示例。

library(ggplot2)
library(MethComp)

data(airquality)
data <- na.exclude(airquality)

# Orthogonal, total least squares or Deming regression
deming <- Deming(y=airquality$Wind, x=airquality$Temp)[1:2]
deming  
#>  Intercept      Slope 
#> 24.8083259 -0.1906826

# Check with prcomp {stats}
r <- prcomp( ~ airquality$Temp + airquality$Wind )
slope <- r$rotation[2,1] / r$rotation[1,1]
slope   
#> [1] -0.1906826

intercept <- r$center[2] - slope*r$center[1]
intercept
#> airquality$Wind 
#>        24.80833

# https://stackoverflow.com/a/30399576/786542
perp.segment.coord <- function(x0, y0, ortho){
  # finds endpoint for a perpendicular segment from the point (x0,y0) to the line
  # defined by ortho as y = a + b*x
  a <- ortho[1]  # intercept
  b <- ortho[2]  # slope
  x1 <- (x0 + b*y0 - a*b)/(1 + b^2)
  y1 <- a + b*x1
  list(x0=x0, y0=y0, x1=x1, y1=y1)
}

perp.segment <- perp.segment.coord(airquality$Temp, airquality$Wind, deming)
perp.segment <- as.data.frame(perp.segment)

# plot
plot.y <- ggplot(data = data, aes(x = Temp, y = Wind)) + 
  geom_point() +
  geom_abline(intercept = deming[1],
              slope = deming[2]) +
  geom_segment(data = perp.segment, 
               aes(x = x0, y = y0, xend = x1, yend = y1), 
               colour = "blue") +
  theme_bw()

由reprex package（v0.2.0）创建于2018-03-19。

Answer 3

MethComp软件包似乎已不再维护（已从CRAN中删除）。 Russel88/COEF允许将stat_ / geom_summary与method="tls"一起使用以添加正交回归线。

基于此和wikipedia:Deming_regression，我创建了以下函数，这些函数允许使用1：以外的噪声比。

deming.fit <- function(x, y, noise_ratio = sd(y)/sd(x)) {
  if(missing(noise_ratio) || is.null(noise_ratio)) noise_ratio <- eval(formals(sys.function(0))$noise_ratio) # this is just a complicated way to write `sd(y)/sd(x)`
  delta <-  noise_ratio^2
  x_name <- deparse(substitute(x))

  s_yy <- var(y)
  s_xx <- var(x)
  s_xy <- cov(x, y)
  beta1 <- (s_yy - delta*s_xx + sqrt((s_yy - delta*s_xx)^2 + 4*delta*s_xy^2)) / (2*s_xy)
  beta0 <- mean(y) - beta1 * mean(x) 

  res <- c(beta0 = beta0, beta1 = beta1)
  names(res) <- c("(Intercept)", x_name)
  class(res) <- "Deming"
  res
}

deming <- function(formula, data, R = 100, noise_ratio = NULL, ...){
  ret <- boot::boot(
    data = model.frame(formula, data), 
    statistic = function(data, ind) {
      data <- data[ind, ]
      args <- rlang::parse_exprs(colnames(data))
      names(args) <- c("y", "x")
      rlang::eval_tidy(rlang::expr(deming.fit(!!!args, noise_ratio = noise_ratio)), data, env = rlang::current_env())
    },
    R=R
  )
  class(ret) <- c("Deming", class(ret))
  ret  
}

predictdf.Deming <- function(model, xseq, se, level) {
  pred <- as.vector(tcrossprod(model$t0, cbind(1, xseq)))
  if(se) {
    preds <- tcrossprod(model$t, cbind(1, xseq))
    data.frame(
      x = xseq,
      y = pred,
      ymin = apply(preds, 2, function(x) quantile(x, probs = (1-level)/2)),
      ymax = apply(preds, 2, function(x) quantile(x, probs = 1-((1-level)/2)))
    )
  } else {
    return(data.frame(x = xseq, y = pred))
  }
}

# unrelated hlper function to create a nicer plot:
fix_plot_limits <- function(p) p + coord_cartesian(xlim=ggplot_build(p)$layout$panel_params[[1]]$x.range, ylim=ggplot_build(p)$layout$panel_params[[1]]$y.range)

演示：

library(ggplot2)

#devtools::install_github("Russel88/COEF")
library(COEF)

fix_plot_limits(
    ggplot(data.frame(x = (1:5) + rnorm(100), y = (1:5) + rnorm(100)*2), mapping = aes(x=x, y=y)) +
      geom_point()
    ) +
  geom_smooth(method=deming, aes(color="deming"), method.args = list(noise_ratio=2)) +
  geom_smooth(method=lm, aes(color="lm")) +
  geom_smooth(method = COEF::tls, aes(color="tls"))

^{由reprex package（v0.3.0）于2019-12-04创建}