CODEWITHSUNDEEP
rplotggplot2bayesian
Crystal
Crystal

Reputation: 619

How to draw the pairwise marginal distribution for each pair of parameters in a grid using ggplot2

Assuming I have the posterior samples for each of the four parameters. My question is how to plot the pairwise marginal distribution on a grid of 4*4=16 with ggplot2?

I would like to creat a plot like the picture below but instead of the scatter plot I will use a pairwise marginal distributions. Organized in the form of this kind of grid. enter image description here

I am wondering can ggmcmc package achieve my goal?

Thanks in advance, guys!!

Upvotes: 2

Views: 1008

Answers (1)

Crystal
Crystal

Reputation: 619

After getting help from the previous comments, I post the code below in case other people would like to do the same thing as me.

Below is a simple dataset I create for demonstration.This is the dataset "df" with four variables x, y, z, w. We want to get the pairwise joint kernel density estimation. One easy way I find is to use ggpairs from GGally package based on the comments by user20650. The codes are below: It will create the following plot: enter image description here

ggpairs(df,upper = list(continuous = "density"),
         lower = list(combo = "facetdensity"))

        x           y           z             w
1   0.49916998 -0.07439680  0.37731097  0.0927331640
2   0.25281542 -1.35130718  1.02680343  0.8462638556
3   0.50950876 -0.22157249 -0.71134553 -0.6137126948
4   0.28740609 -0.17460743 -0.62504812 -0.7658094835
5   0.28220492 -0.47080289 -0.33799637 -0.7032576540
6  -0.06108038 -0.49756810  0.49099505  0.5606988283
7   0.29427440 -1.14998030  0.89409384  0.5656682378
8  -0.37378096 -1.37798177  1.22424964  1.0976507702
9   0.24306941 -0.41519951  0.17502049 -0.1261603208
10  0.45686871 -0.08291032  0.75929106  0.7457002259
11 -0.16567173 -1.16855088  0.59439600  0.6410396945
12  0.22274809 -0.19632766  0.27193362  0.5532901113
13  1.25555629  0.24633499 -0.39836999 -0.5945792966
14  1.30440121  0.05595755  1.04363679  0.7379212885
15 -0.53739075 -0.01977930  0.22634275  0.4699563173
16  0.17740551 -0.56039760 -0.03278126 -0.0002523205
17  1.02873716  0.05929581 -0.74931661 -0.8830775310
18 -0.13417946 -0.60421101 -0.24532606 -0.1951831558
19  0.11552305 -0.14462104  0.28545703 -0.2527437818
20  0.71783902 -0.12285529  1.23488185  1.3224880574

Upvotes: 3

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