R 中比较两个矩阵的图

发布于 2024-09-24 16:10:25 字数 390 浏览 3 评论 0原文

我有两个矩阵（大约 300 x 100），我想绘制一个图表来查看第一个矩阵高于第二个矩阵的部分。

例如，我可以这样做：

# Calculate the matrices and put them into m1 and m2
# Note that the values are between -1 and 1
par(mfrow=c(1,3))
image(m1, zlim=c(-1,1))
image(m2, zlim=c(-1,1))
image(m1-m2, zlim=c(0,1))

这将仅在第三个图中绘制所需的区域，但我想做一些不同的事情，例如在第一个图上的这些区域周围画一条线，以便直接突出显示它们。

知道我该怎么做吗？

谢谢尼科

原文

I have two matrices (of approximately 300 x 100) and I would like to plot a graph to see the parts of the first one that are higher than those of the second.

I can do, for instance:

# Calculate the matrices and put them into m1 and m2
# Note that the values are between -1 and 1
par(mfrow=c(1,3))
image(m1, zlim=c(-1,1))
image(m2, zlim=c(-1,1))
image(m1-m2, zlim=c(0,1))

This will plot only the desired regions in the 3rd plot but I would like to do something a bit different, like putting a line around those areas over the first plot in order to highlight them directly there.

Any idea how I can do that?

Thank you
nico

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驱逐舰岛风号 2024-10-01 16:10:25

怎么样：

par(mfrow = c(1, 3))
image(m1, zlim = c(-1, 1))
contour(m1 - m2, add = TRUE)
image(m2, zlim = c(-1, 1))
contour(m1 - m2, add = TRUE)
image(m1 - m2, zlim = c(0, 1))
contour(m1 - m2, add = TRUE)

这会在区域周围添加等高线图。有点像在第三个图的区域周围放置圆环（可能想要摆弄轮廓的（n）层以获得更少的“圆”）。

How about:

par(mfrow = c(1, 3))
image(m1, zlim = c(-1, 1))
contour(m1 - m2, add = TRUE)
image(m2, zlim = c(-1, 1))
contour(m1 - m2, add = TRUE)
image(m1 - m2, zlim = c(0, 1))
contour(m1 - m2, add = TRUE)

This adds a contour map around the regions. Sort of puts rings around the areas of the 3rd plot (might want to fiddle with the (n)levels of the contours to get fewer 'circles').

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百合的盛世恋 2024-10-01 16:10:25

制作第三张图像的另一种方法可能是：

image(m1>m2)

这会生成一个 TRUE/FALSE 值矩阵，该矩阵被成像为 0/1，因此您有一个双色图像。不过，仍然不确定你的“划线”事情......

Another way of doing your third image might be:

image(m1>m2)

this produces a matrix of TRUE/FALSE values which gets imaged as 0/1, so you have a two-colour image. Still not sure about your 'putting a line around' thing though...

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九八野马 2024-10-01 16:10:25

这是我为执行类似操作而编写的一些代码。我想通过在阈值高于 0.95 的连续区域周围画一个框来突出显示它们，因此我得到了所有高于 0.95 的网格方块，并对它们进行了聚类。然后对聚类输出进行一些摆弄以获取区域的矩形坐标：

computeHotspots = function(xyz, thresh, minsize=1, margin=1){
### given a list(x,y,z), return a data frame where each row
### is a (xmin,xmax,ymin,ymax) of bounding box of a contiguous area
### over the given threshhold.
### or approximately. lets use the clustering tools in R...

  overs <- which(xyz$z>thresh,arr.ind=T)

  if(length(overs)==0){
    ## found no hotspots
    return(NULL)
  }

  if(length(overs)==2){
    ## found one hotspot
    xRange <- cbind(xyz$x[overs[,1]],xyz$x[overs[,1]])
    yRange <- cbind(xyz$y[overs[,2]],xyz$y[overs[,2]])
  }else{

    oTree <- hclust(dist(overs),method="single")
    oCut <- cutree(oTree,h=10)

    oXYc <- data.frame(x=xyz$x[overs[,1]],y=xyz$y[overs[,2]],oCut)

    xRange <- do.call("rbind",tapply(oXYc[,1],oCut,range))
    yRange <- do.call("rbind",tapply(oXYc[,2],oCut,range))

  }

### add user-margins
 xRange[,1] <- xRange[,1]-margin
 xRange[,2] <- xRange[,2]+margin
 yRange[,1] <- yRange[,1]-margin
 yRange[,2] <- yRange[,2]+margin

## put it all together
 xr <- apply(xRange,1,diff)
 xm <- apply(xRange,1,mean)
 xRange[xr<minsize,1] <- xm[xr<minsize]-(minsize/2)
 xRange[xr<minsize,2] <- xm[xr<minsize]+(minsize/2)

 yr <- apply(yRange,1,diff)
 ym <- apply(yRange,1,mean)
 yRange[yr<minsize,1] <- ym[yr<minsize]-(minsize/2)
 yRange[yr<minsize,2] <- ym[yr<minsize]+(minsize/2)

  cbind(xRange,yRange)

}

测试代码：

x=1:23
y=7:34
m1=list(x=x,y=y,z=outer(x,y,function(x,y){sin(x/3)*cos(y/3)}))
image(m1)
hs = computeHotspots(m1,0.95)

这应该为您提供一个矩形坐标矩阵：

> hs
  [,1] [,2] [,3] [,4]
1   13   15    8   11
2    3    6   17   20
3   22   24   18   20
4   13   16   27   30

现在您可以使用 rect: 在图像上绘制它们

image(m1)
rect(hs[,1],hs[,3],hs[,2],hs[,4])

并显示它们所在的位置应该是：

image(list(x=m1$x,y=m1$y,z=m1$z>0.95))
rect(hs[,1],hs[,3],hs[,2],hs[,4])

您当然可以调整它来绘制圆圈，但更复杂的形状会很棘手。当感兴趣的区域相当紧凑时，它效果最好。

巴里

Here's some code I wrote to do something similar. I wanted to highlight contiguous regions above a 0.95 threshold by drawing a box round them, so I got all the grid squares above 0.95 and did a clustering on them. Then do a bit of fiddling with the clustering output to get the rectangle coordinates of the regions:

computeHotspots = function(xyz, thresh, minsize=1, margin=1){
### given a list(x,y,z), return a data frame where each row
### is a (xmin,xmax,ymin,ymax) of bounding box of a contiguous area
### over the given threshhold.
### or approximately. lets use the clustering tools in R...

  overs <- which(xyz$z>thresh,arr.ind=T)

  if(length(overs)==0){
    ## found no hotspots
    return(NULL)
  }

  if(length(overs)==2){
    ## found one hotspot
    xRange <- cbind(xyz$x[overs[,1]],xyz$x[overs[,1]])
    yRange <- cbind(xyz$y[overs[,2]],xyz$y[overs[,2]])
  }else{

    oTree <- hclust(dist(overs),method="single")
    oCut <- cutree(oTree,h=10)

    oXYc <- data.frame(x=xyz$x[overs[,1]],y=xyz$y[overs[,2]],oCut)

    xRange <- do.call("rbind",tapply(oXYc[,1],oCut,range))
    yRange <- do.call("rbind",tapply(oXYc[,2],oCut,range))

  }

### add user-margins
 xRange[,1] <- xRange[,1]-margin
 xRange[,2] <- xRange[,2]+margin
 yRange[,1] <- yRange[,1]-margin
 yRange[,2] <- yRange[,2]+margin

## put it all together
 xr <- apply(xRange,1,diff)
 xm <- apply(xRange,1,mean)
 xRange[xr<minsize,1] <- xm[xr<minsize]-(minsize/2)
 xRange[xr<minsize,2] <- xm[xr<minsize]+(minsize/2)

 yr <- apply(yRange,1,diff)
 ym <- apply(yRange,1,mean)
 yRange[yr<minsize,1] <- ym[yr<minsize]-(minsize/2)
 yRange[yr<minsize,2] <- ym[yr<minsize]+(minsize/2)

  cbind(xRange,yRange)

}

Test code:

x=1:23
y=7:34
m1=list(x=x,y=y,z=outer(x,y,function(x,y){sin(x/3)*cos(y/3)}))
image(m1)
hs = computeHotspots(m1,0.95)

That should give you a matrix of rectangle coordinates:

> hs
  [,1] [,2] [,3] [,4]
1   13   15    8   11
2    3    6   17   20
3   22   24   18   20
4   13   16   27   30

Now you can draw them over the image with rect:

image(m1)
rect(hs[,1],hs[,3],hs[,2],hs[,4])

and to show they are where they should be:

image(list(x=m1$x,y=m1$y,z=m1$z>0.95))
rect(hs[,1],hs[,3],hs[,2],hs[,4])

You could of course adapt this to draw circles, but more complex shapes would be tricky. It works best when the regions of interest are fairly compact.

Barry

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