2
$\begingroup$

I have a matrix with many rows (>18000) and few columns (6), where five are numerical and one is a binary factor and i'd like to find an efficient and simple way to visualize the relationship between the factor and the other variables.

I want to find out how observations with, for example, categorical value = 1 correlates with each numerical variable.

I tried a pca and visualized the result as a biplot and it kinda works, but i would like to try other solution possibly.

Do you guys have any idea ?

Improve this question
$\endgroup$

2 Answers 2

Reset to default
4
$\begingroup$

You've used the term "visualizing" so here are two approaches.

Given the large number of observations and just a few variables, plotting all-possible pairs of variables (say with the pairs function in R) with two different symbols associated with the binary variable might be informative but likely you'd need to plot a random sample of just 100 to 200 sample points.

# Generate some data
  library(MASS)
  n <- 200
  covmat <- matrix(c(1,    0,    0.5, -0.5, 0.5,
                     0,    1,    0.3, -0.4, 0.4,
                     0.5,  0.3,  1,   -0.5, 0.2,
                    -0.5, -0.4, -0.5,  1,   0.1,
                     0.5,  0.4,  0.2,  0.1, 1), nrow=5)
  x0 <- mvrnorm(n, c(0, 0, 0,  0, 0), covmat)
  x1 <- mvrnorm(n, c(1, 3, 0, -2, 0), covmat)

# All possible pairwise plots
  pch <- c(rep(1, n), rep(16,n))
  pairs(rbind(x0, x1), pch=pch)

All possible scatter plots

Alternatively with lots of observations a "summary" is needed (otherwise the above approach will not be readable.) A contour plot of the estimate of the bivariate density for each level of the binary variable might be informative.

library(ks)
par(mfrow=c(5,5), mai=c(0,0,0,0))
for (i in 1:5) {
for (j in 1:5) {
    if (i==j) {
       plot(c(0,1), c(0,1), type="n", axes=FALSE, xlab="", ylab="")
       text(0.5, 0.5, paste("var", i), font=2, cex=2)
    } else {
       p = c(i,j)
       plot(rbind(x0[, p], x1[, p]), type="n", axes=FALSE, xlab="", ylab="")
       plot(kde(x0[, p]), add=TRUE, col="gray", axes=FALSE)
       plot(kde(x1[, p]), add=TRUE, col="black", axes=FALSE)
    }
    box()
}}

All possible pairwise kernel density estimates

Improve this answer
$\endgroup$
1
  • 2
    $\begingroup$ Small multiple plots like these are very effective. I think these particular ones can be simplified a bit: (a) the plots below and above the diagonal are duplicates of each other; (b) the OP asks to visualize the relationship between the binary variable and each continuous variable in turn. It may be enough to have one row of panels. $\endgroup$
    – dipetkov
    Commented Nov 22, 2022 at 11:21
0
$\begingroup$

How about a logistic regression, where you predict the categorical based on the numerical columns. You can also do this regression for subsets, find the R^2 / explanatory value for each subset, to see which numerical values are useful. For nonlinear relationships, doing the same thing with a random forest, or even just a single decision tree, can be very effective.

Improve this answer
$\endgroup$
1
  • $\begingroup$ This is an interesting suggestion. However, as a first step it might be simpler/more effective to plot the data without analyzing it. Also, variance explained for a binary outcome is a somewhat complex concept. Much discussed on CV, see for example this brand new (possibly duplicate?) question: stats.stackexchange.com/q/596546/237901. $\endgroup$
    – dipetkov
    Commented Nov 22, 2022 at 11:41

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.