在 R 中随时间绘制概率密度热图

Question

Let's say I have the output of a monte-carlo simulation of one variable over several different iterations (think millions). For each iteration, I have the values of the variable at each point in time (ranging from t=1 to t=365).

I would like to produce the following plot: For each point in time, t, on the x axis and for each possible value "y" in a given range, set the color of x,y to "k" where "k" is a count of how many observations are within a vicinity of distance "d" to x,y.

I know you can easily make density heatmaps for 1D data, but is there a good package for doing this on 2 dimensions? Do I have to use kriging?

Edit: The data structure is currently a matrix.

                                     data matrix

                                      day number
             [,1]    [,2]         [,3]      [,4]       [,5]      ... [,365]
iteration    [1,]    0.000213   0.001218    0.000151   0.000108  ... 0.000101
             [2,]    0.000314   0.000281    0.000117   0.000103  ... 0.000305
             [3,]    0.000314   0.000281    0.000117   0.000103  ... 0.000305
             [4,]    0.000171   0.000155    0.000141   0.000219  ... 0.000201
              .
              .
              .
     [100000000,]    0.000141   0.000148    0.000144   0.000226  ... 0.000188

I want to, for each "day" have the pixels running vertically across that "day" to represent the probability density of the iteration's values for that day in color. The result should look like a heatmap.

Answer 1

Here is one solution to what I think you are after.

1. Generate data.

   myData <- mapply(rnorm, 1000, 200, mean=seq(-50,50,0.5))

This is a matrix with 1000 rows (observations) and 201 time points. In each time point the mean of data there shifts gradually from -50 to 50. By 0.5 each time.

2. Get densities.

   myDensities <- apply(myData, 2, density, from=-500, to=500)

This will give you a list of densities for each column. In order for them to be plottable side by side we specified the ranges (from -500 to 500) manually.

3. Obtain density values from the list.

   Ys <- sapply(myDensities, "[", "y")

This is again a list. You need to get a matrix from that.

4. Get matrix from list.

   img <- do.call(cbind, Ys)

This simply combines all Ys elements by column.

5. Plot.

   filled.contour(x=1:ncol(img), y=myDensities[[1]]$x, t(img))

I use filled.contour for that. But you can look around for other 2-D plot functions. I also used values obtained from the densities D[[1]]$x .

And here is the result:

The shift from -50 to 50 is visible.

Not sure if this can work well with millions of time points. But plotting million probably makes little sense since you will in any case by limited by the number of pixels. Some kind of pre-processing might be necessary.

Answer 2

Another way to present data over time is to create a video.

The following uses the same matrix data as Karolis:

library(av)

myData <- mapply(rnorm, 1000, 200, mean=seq(-50,50,0.5))

# create function that includes a for loop, the output from 
# each iteration of the for loop will become one frame in
# the animation.
make_plot <- function(myData){
  
  xrange = range(myData)
  
  for(i in seq_along(myData[1,])){
    
    d <- density(myData[,i],
                 bandwidth = 45) # returns the density data
    plot(d, 
         xlim=xrange, 
         ylim=c(0, 0.003), 
         main = paste("Density, day:",i))
 
  }
}

# create video
av_capture_graphics(make_plot(myData),
                    output = "Density change over time.mp4",
                    width = 720,
                    height = 480,
                    framerate = 120)