如何使用xyz不相似的数据在R中生成热图 [英] How to make a heatmap in R with xyz dissimilar data

问题描述

我正尝试使用 R中的真正热图建议，但我得到的错误：

Interp.old中的错误（x，y，z，xo = xo，yo = yo，ncp = 0，extrap = extrap，

：x和y的比例太不相似了

后面的代码行：

  s < -  interp（x，y，z）
  pre> 
 
 
我的数据是在黑暗的连续背景下构建的，希望获得像线一样的彩色热图，并且可以在 GNUplot 使用设置pm3d map 和设置hidden3d 。数据对应于分子生产模型（<$ c $在给定时间（ x ）中出现频率 z  
 
 
  1.000000000000e + 00 1e-8 0 
 
 1.000000000000e +00 5e-8 0 
 
 1.000000000000e + 00 1e-7 5 
 
 1.000000000000e + 00 5e-7 0 
 
 1.000000000000e + 00 1e-6 0 
 
 1.000000000000e + 00 5e-6 0 
 
 1.000000000000e + 00 1e-5 0 
 
 1.000000000000e + 00 5e-5 0 
 
 1.000000000000e + 00 1e-4 0 
 
 1.000000000000e + 00 5e-4 0 
 
 1.000000000000e + 00 1e-3 0 
 
 1.000000000000e + 00 5e-3 0 
 
 1.000000000000e +00 1e-2 0 
 
 1.000000000000e + 00 5e-2 0 
 
 1.000000000000e + 00 1e-1 0 
 
 1.000000000000e + 00 5e-1 0 
 
 1.000000000000e + 00 1e + 1 0 
 
 1.000000000000e + 00 5e + 1 0 
 
 1.000000000000e + 00 1e + 2 0 
 
 1.000000000000e + 00 5e + 2 0 
 
 1.000000000000e + 00 1e + 3 0 
 
 1.000000000000e + 00 5e + 3 0 
 
 1.000000000000e + 00 1e + 4 0 
 
 1.000000000000e + 00 5e + 4 0 
 
 1.000000000000e + 00 1e + 5 0 
 
 1.000000000000e + 00 5e + 5 0 
 
 1.000000000000e + 00 1e + 6 0 
 
 1.0000000 00000e + 00 5e + 6 0 
 
 1.000000000000e + 00 1e + 7 0 
 
 1.000000000000e + 00 5e + 7 0 
 
 1.000000000000e + 00 1e + 8 0 
 
 1.000000000000e + 00 5e + 8 0 
 
 2.000000000000e + 00 1e-8 0 
 
 2.000000000000e + 00 5e -8 0 
 
 2.000000000000e + 00 1e-7 0 
 
 2.000000000000e + 00 5e-7 5 
 
 2.000000000000e + 00 1e-6 0 
 
 2.000000000000e + 00 5e-6 0 
 
 2.000000000000e + 00 1e-5 0 
 
 2.000000000000e + 00 5e-5 0 
 
 2.000000000000e + 00 1e-4 0 
 
 2.000000000000e + 00 5e-4 0 
 
 2.000000000000e + 00 1e-3 0 
 
 2.000000000000e + 00 5e-3 0 
 
 2.000000000000e + 00 1e-2 0 
 
 2.000000000000e + 00 5e-2 0 
 
 2.000000000000e + 00 1e-1 0 
 
 2.000000000000e + 00 5e-1 0 
 
 2.000000000000e + 00 1e + 1 0 
 
 2.000000000000e + 00 5e + 1 0 
 
 2.000000000000e + 00 1e + 2 0 
 
 2.000000000000e + 00 5e + 2 0 
 
 2.000000000000e + 00 1e + 3 0 
 
 2.000000000000e + 00 5e + 3 0 
 
 2.000000000000e + 00 1e + 4 0 
 
 2.000000000000e + 00 5e + 4 0 
 
 2.000000000000e + 00 1e + 5 0 
 
 2.000000000000e + 00 5e + 5 0 
 
 2.000000000000e + 00 1e + 6 0 
 
 2.000000000000e + 00 5e + 6 0 
 
 2.000000000000e + 00 1e + 7 0 
 
 2.000000000000e + 00 5e + 7 0 
 
 2.000000000000e + 00 1e + 8 0 
 
 2.000000000000e + 00 5e + 8 0 
 
 3.000000000000e + 00 1e-8 0 
 
 3.000000000000e + 00 5e-8 0 
 
 3.000000000000e + 00 1e-7 0 
 
 3.000000000000 e + 00 5e-7 0 
 
 3.000000000000e + 00 1e-6 5 
 
 3.000000000000e + 00 5e-6 0 
 
 3.000000000000e + 00 1e-5 0 
 
 3.000000000000e + 00 5e-5 0 
 
 3.000000000000e + 00 1e-4 0 
 
 3.000000000000e + 00 5e -4 0 
 
 3.000000000000e + 00 1e-3 0 
 
 3.000000000000e + 00 5e-3 0 
 
 3.000000000000e + 00 1e-2 0 
 
 3.000000000000e + 00 5e-2 0 
 
 3.000000000000e + 00 1e-1 0 
 
 3.000000000000e +00 5e-1 0 
 
 3.000000000000e + 00 1e + 1 0 
 
 3.000000000000e + 00 5e + 1 0 
 
 3.000000000000e + 00 1e + 2 0 
 
 3.000000000000e + 00 5e + 2 0 
 
 3.000000000000e + 00 1e + 3 0 
 
 3.000000000000e + 00 5e + 3 0 
 
 3.000000000000e + 00 1e + 4 0 
 
 3.000000000000e + 00 5e + 4 0 
 
 3.000000000000e + 00 1e + 5 0 
 
 3.000000000000e + 00 5e + 5 0 
 
 3.000000000000e + 00 1e + 6 0 
 
 3.000000000000e + 00 5e + 6 0 
 
 3.000000000000e + 00 1e + 7 0 
 
 3.000000000000e + 00 5e + 7 0 
 
 3.000000000000e + 00 1e + 8 0 
 
 3.000000000000e + 00 5e + 8 0 
 
 4.000000000000e + 00 1e-8 0 
 
 4.000000000000e + 00 5e-8 0 
 
 4.000000000000e + 00 1e-7 0 
 
 4.000000000000e + 00 5e-7 0 
 
 4.000000000000e + 00 1e-6 0 
 
 4.000000000000e + 00 5e-6 5 
 
 4.000000000000e + 00 1e-5 0 
 
 4.000000000000e + 00 5e -5 0 
 
 4.000000000000e + 00 1e-4 0 
 
 4.000000000000e + 00 5e-4 0 
 
 4.000000000000e + 00 1e-3 0 
 
 4.000000000000e + 00 5e-3 0 
 
 4.000000000000e + 00 1e-2 0 
 
 4.000000000000e + 00 5e-2 0 
 
 4.000000000000e + 00 1e-1 0 
 
 4.000000000000e + 00 5e-1 0 
 
 4.000000000000e + 00 1e + 1 0 
 
 4.000000000000e + 00 5e + 1 0 
 
 4.000000000000e + 00 1e + 2 0 
 
 4.000000000000e + 00 5e + 2 0 
 
 4.000000000000e + 00 1e + 3 0 
 
 4.000000000000e + 00 5e + 3 0 
 
 4.000000000000e + 00 1e + 4 0 
 
 4.000000000000e + 00 5e + 4 0 
 
 4.000000000000e + 00 1e + 5 0 
 
 4.000000000000e + 00 5e + 5 0 
 
 4.000000000000 e + 00 1e + 6 0 
 
 4.000000000000e + 00 5e + 6 0 
 
 4.000000000000e + 00 1e + 7 0 
 
 4.000000000000e + 00 5e + 7 0 
 
 4.000000000000e + 00 1e + 8 0 
 
 4.000000000000e + 00 5e + 8 0 
  
第一个建议给了我一些类似于最初在 R中真实的热图，它是一些在不同的灰度色调中包含一些水平线的点。第二个似乎崩溃了。我收到了这条消息： 
 
 
 > Traceback：
 1：.Fortran（idsfft，as。整数（1），as.integer（ncp），as.integer（n），as.double（x），as.double（y），as.double（z），as.integer（nx），as.integer （ny），x = as.double（xo），y = as.double（yo），z = zo，整数（（31 + ncp）* n + nx * ny），double（5 * n） as.logical（misso），PACKAGE =akima）
 2：interp.old（x，y，z，xo = xo，yo = yo，ncp = 0，extrap = extrap，duplicate = duplicate，dupfun = 
 3：interp（x，y，z）
 
可能的操作：
 1：abort（如果启用了核心转储）
 2：正常R退出
 3：退出R而不保存工作区
 4：退出R保存工作区
  
 
 
 <我只是包括更多的数据，以防它有用。 
我这样调用数据框中的列：
 
 
 
 
 coso<  -  read.table（/ home / 
 
 
 
 
 > x< -coso [[1]] 
 
> y< -coso [[2]] 
  
这是我在GNUplot中的一个图表，我预计会用R获得更好的图表。
解决方案
 
建议的解决方案
 
 
 
使用 scale（）来转换x和y使用字段 
 $ b 
模拟
 
 
 
 >包在你的问题： 
 
 
 库（akima）
库（字段）
 $ b $ （20,4,3）
y < -  rnorm（20,5e-5,1e-5）
x < -  scale（x）＃注释掉这两行
y<  -  scale（y）＃来重现你的错误
z -norm（20）
 
s <-interp（x，y，z）
图像。情节
  
使用 ggplot2 ，改编自 
 
 
  library（akima）
 library（ggplot2）$ （20，4，3）
y < -  rnorm（20,5e-5,1e-5）
x < -  scale（x）＃comment出这两行
y<  -  scale（y）＃来重现你的错误
z < -  rnorm（20）
 
 t。 <  -  interp（x，y，z）
 t.df<  -  data.frame（t。）
 
 gt<  -  data.frame（expand.grid（X1 = t $ x，
 X2 = t。$ y），
z = c（t。$ z），
 value = cut（c（t。$ z），
 （a）（x1，x2，fill = value）bs = bs（min（z），max（z），0.25）））
 
p < -  ggplot（gt）+ 
 geom_tile ）+ 
 geom_contour（aes（x = X1，y = X2，z = z），color =black）
p 
  
 
 
 
校正轴标签
 
 
 
另一个问题，解决方案也描述为在重新缩放前使用正确的原始数据值标记轴。目前只适用于 ggplot 。
 
I am trying to use the A true heat map in R suggestions, however I get the error:

  
Error in interp.old(x, y, z, xo = xo, yo = yo, ncp = 0, extrap = extrap,

      : scales of x and y are too dissimilar
after the code line:
 s <- interp(x,y,z)
My data was constructed expecting to get coloured heat-map like lines in a dark continuous background, and works in GNUplot using set pm3d map and set hidden3d. The data corresponds to a model of molecules production (y) in a given time (x) with the frequency of appearance denoted by z. It looks like this:
1.000000000000e+00  1e-8    0

1.000000000000e+00  5e-8    0

1.000000000000e+00  1e-7    5

1.000000000000e+00  5e-7    0

1.000000000000e+00  1e-6    0

1.000000000000e+00  5e-6    0

1.000000000000e+00  1e-5    0

1.000000000000e+00  5e-5    0

1.000000000000e+00  1e-4    0

1.000000000000e+00  5e-4    0

1.000000000000e+00  1e-3    0

1.000000000000e+00  5e-3    0

1.000000000000e+00  1e-2    0

1.000000000000e+00  5e-2    0

1.000000000000e+00  1e-1    0

1.000000000000e+00  5e-1    0

1.000000000000e+00  1e+1    0

1.000000000000e+00  5e+1    0

1.000000000000e+00  1e+2    0

1.000000000000e+00  5e+2    0

1.000000000000e+00  1e+3    0

1.000000000000e+00  5e+3    0

1.000000000000e+00  1e+4    0

1.000000000000e+00  5e+4    0

1.000000000000e+00  1e+5    0

1.000000000000e+00  5e+5    0

1.000000000000e+00  1e+6    0

1.000000000000e+00  5e+6    0

1.000000000000e+00  1e+7    0

1.000000000000e+00  5e+7    0

1.000000000000e+00  1e+8    0

1.000000000000e+00  5e+8    0

2.000000000000e+00  1e-8    0

2.000000000000e+00  5e-8    0

2.000000000000e+00  1e-7    0

2.000000000000e+00  5e-7    5

2.000000000000e+00  1e-6    0

2.000000000000e+00  5e-6    0

2.000000000000e+00  1e-5    0

2.000000000000e+00  5e-5    0

2.000000000000e+00  1e-4    0

2.000000000000e+00  5e-4    0

2.000000000000e+00  1e-3    0

2.000000000000e+00  5e-3    0

2.000000000000e+00  1e-2    0

2.000000000000e+00  5e-2    0

2.000000000000e+00  1e-1    0

2.000000000000e+00  5e-1    0

2.000000000000e+00  1e+1    0

2.000000000000e+00  5e+1    0

2.000000000000e+00  1e+2    0

2.000000000000e+00  5e+2    0

2.000000000000e+00  1e+3    0

2.000000000000e+00  5e+3    0

2.000000000000e+00  1e+4    0

2.000000000000e+00  5e+4    0

2.000000000000e+00  1e+5    0

2.000000000000e+00  5e+5    0

2.000000000000e+00  1e+6    0

2.000000000000e+00  5e+6    0

2.000000000000e+00  1e+7    0

2.000000000000e+00  5e+7    0

2.000000000000e+00  1e+8    0

2.000000000000e+00  5e+8    0

3.000000000000e+00  1e-8    0

3.000000000000e+00  5e-8    0

3.000000000000e+00  1e-7    0

3.000000000000e+00  5e-7    0

3.000000000000e+00  1e-6    5

3.000000000000e+00  5e-6    0

3.000000000000e+00  1e-5    0

3.000000000000e+00  5e-5    0

3.000000000000e+00  1e-4    0

3.000000000000e+00  5e-4    0

3.000000000000e+00  1e-3    0

3.000000000000e+00  5e-3    0

3.000000000000e+00  1e-2    0

3.000000000000e+00  5e-2    0

3.000000000000e+00  1e-1    0

3.000000000000e+00  5e-1    0

3.000000000000e+00  1e+1    0

3.000000000000e+00  5e+1    0

3.000000000000e+00  1e+2    0

3.000000000000e+00  5e+2    0

3.000000000000e+00  1e+3    0

3.000000000000e+00  5e+3    0

3.000000000000e+00  1e+4    0

3.000000000000e+00  5e+4    0

3.000000000000e+00  1e+5    0

3.000000000000e+00  5e+5    0

3.000000000000e+00  1e+6    0

3.000000000000e+00  5e+6    0

3.000000000000e+00  1e+7    0

3.000000000000e+00  5e+7    0

3.000000000000e+00  1e+8    0

3.000000000000e+00  5e+8    0

4.000000000000e+00  1e-8    0

4.000000000000e+00  5e-8    0

4.000000000000e+00  1e-7    0

4.000000000000e+00  5e-7    0

4.000000000000e+00  1e-6    0

4.000000000000e+00  5e-6    5

4.000000000000e+00  1e-5    0

4.000000000000e+00  5e-5    0

4.000000000000e+00  1e-4    0

4.000000000000e+00  5e-4    0

4.000000000000e+00  1e-3    0

4.000000000000e+00  5e-3    0

4.000000000000e+00  1e-2    0

4.000000000000e+00  5e-2    0

4.000000000000e+00  1e-1    0

4.000000000000e+00  5e-1    0

4.000000000000e+00  1e+1    0

4.000000000000e+00  5e+1    0

4.000000000000e+00  1e+2    0

4.000000000000e+00  5e+2    0

4.000000000000e+00  1e+3    0

4.000000000000e+00  5e+3    0

4.000000000000e+00  1e+4    0

4.000000000000e+00  5e+4    0

4.000000000000e+00  1e+5    0

4.000000000000e+00  5e+5    0

4.000000000000e+00  1e+6    0

4.000000000000e+00  5e+6    0

4.000000000000e+00  1e+7    0

4.000000000000e+00  5e+7    0

4.000000000000e+00  1e+8    0

4.000000000000e+00  5e+8    0
The first suggestion gave me some ugly results similar the ones obtained at first in A true heat map in R which is a plot with some horizontal lines full of dots in different gray-scale tones. The second seems to crash. I got this message from it:
>Traceback:
1: .Fortran("idsfft", as.integer(1), as.integer(ncp), as.integer(n),     as.double(x),  as.double(y), as.double(z), as.integer(nx),     as.integer(ny), x = as.double(xo), y = as.double(yo), z = zo,     integer((31 + ncp) * n + nx * ny), double(5 * n), misso = as.logical(misso),     PACKAGE = "akima")
2: interp.old(x, y, z, xo = xo, yo = yo, ncp = 0, extrap = extrap,     duplicate = duplicate, dupfun = dupfun)
3: interp(x, y, z)

Possible actions:
1: abort (with core dump, if enabled)
2: normal R exit
3: exit R without saving workspace
4: exit R saving workspace
I just included more data in case it is helpful.
I am calling the columns from the data frame this way:

  
coso <- read.table("/home/libertad/mygraphs/two/two_1_90/coso.txt", header = FALSE,sep = "\t")


>x <-coso[[1]]

>y <-coso[[2]]
This is one of my graphs in GNUplot, I expected to get nicer ones with R.
解决方案
Suggested Solution

Use scale() to transform x and y to comparable scales.

Simulation

Using the fields package as in your question:
library(akima) 
library(fields) 

x <- rnorm(20, 4, 3)
y <- rnorm(20, 5e-5, 1e-5)
x <- scale(x) # comment out these two lines 
y <- scale(y) # to reproduce your error
z <- rnorm(20)

s <- interp(x,y,z)
image.plot(s)
Using ggplot2, adapted from my other answer here:
library(akima) 
library(ggplot2) 

x <- rnorm(20, 4, 3)
y <- rnorm(20, 5e-5, 1e-5)
x <- scale(x) # comment out these two lines 
y <- scale(y) # to reproduce your error
z <- rnorm(20)

t. <- interp(x,y,z)
t.df <- data.frame(t.)

gt <- data.frame( expand.grid(X1=t.$x, 
                              X2=t.$y), 
                  z=c(t.$z), 
                  value=cut(c(t.$z), 
                            breaks=seq(min(z),max(z),0.25)))

p <- ggplot(gt) + 
    geom_tile(aes(X1,X2,fill=value)) + 
    geom_contour(aes(x=X1,y=X2,z=z), colour="black") 
p


Correcting the Axis Labels

In another question, the solution is also described for labeling the axes with the correct values of the original data before the re-scaling. This currently only applies to ggplot.

                        
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