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将ggplot2中的密度图缩放为具有相同的X轴范围

purrr dplyr ggplot2 r

1

user1420372 · 技术社区 · 8 年前

我想覆盖两个密度图;一个是转换前的数据,另一个是转换后的数据。我不关心x和y值,只关心曲线的形状。

我想将给定预测器的两个图表叠加在一起,即使X轴是不同的。我发现很难从两个方面看问题。在现实中,也会有更多的情节,因此将未转换和转换的数据结合到一个是最好的解决方案。

library(tidyverse)
require(caret)
data(BloodBrain)
bbbTrans <- preProcess(select(bbbDescr, adistd, adistm, dpsa3, inthb), method = "YeoJohnson")
bbbTransData <- predict(bbbTrans, select(bbbDescr, adistd, adistm, dpsa3, inthb)) 
dat <- bbbTransData %>%
  gather(Predictor, Value) %>%
  mutate(Transformation = "Yeo-Johnson") %>%
  bind_rows(data.frame(gather(select(bbbDescr, adistd, adistm, dpsa3, inthb), Predictor, Value), Transformation = "NA", stringsAsFactors = FALSE))  



# For the predictor adistd, I would like the x-axis range to be 0:12.5 for the
# "Yeo-Johnson" transformation and 0:250 for no transformation.  In this plot, it
# is hard to see the shape of the transformed variables due to the different x-value range.
dat %>% ggplot(aes(x = Value, color = Transformation)) +  
  geom_density(aes(y = ..scaled..), position = "dodge") + 
  facet_wrap(~Predictor, scales = "free")


# i.e., I want to superimpose the 2 charts for a given Predictor on top of each other, even though the x-axis is different
# I find it hard to look across the two facets.  In reality, as well, there will be a lot more plots, so combining the non-transformed and transformed data into the one plot using colour would be the best solution.
  filter(dat, Transformation != 'NA') %>% ggplot(aes(x = Value, y = ..scaled..)) +  
  geom_density() + 
  facet_wrap(~Predictor, scales = "free")

  filter(dat, Transformation == 'NA') %>% ggplot(aes(x = Value, y = ..scaled..)) +  
  geom_density() + 
  facet_wrap(~Predictor, scales = "free")

编辑:我认为我需要的算法是(并且更喜欢使用tidyverse):

按预测/转换分组
获取每个的密度
将密度x转换为(x-xmin)/(xmax-xmin),以便在0到1之间
绘图转换密度$X,密度$Y

1 回复 | 直到 8 年前

1

pogibas 8 年前

可扩展的解决方案( base::scale )计算密度( stats::density )。 density 函数输出相同数量的等距点,以便我们可以从 0 到 1 (按运营商要求)。

# How many points we want 
nPoints <- 1e3

# Final result
res <- list()

# Using simple loop to scale and calculate density
combinations <- expand.grid(unique(dat$Predictor), unique(dat$Transformation))
for(i in 1:nrow(combinations)) {
    # Subset data
    foo <- subset(dat, Predictor == combinations$Var1[i] & Transformation == combinations$Var2[i])
    # Perform density on scaled signal
    densRes <- density(x = scale(foo$Value), n = nPoints)
    # Position signal from 1 to wanted number of points
    res[[i]] <- data.frame(x = 1:nPoints, y = densRes$y, 
                           pred = combinations$Var1[i], trans = combinations$Var2[i])
}
res <- do.call(rbind, res)
ggplot(res, aes(x / nPoints, y, color = trans, linetype = trans)) +
    geom_line(alpha = 0.5, size = 1) +
    facet_wrap(~ pred, scales = "free")