Dear List,
I am looking to perform exploratory analyses of two (relatively) large
datasets of categorical data. The first one is a binary 80x100 matrix, in
the form:
matrix(sample(c(0,1),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c(
"group1", "group2","group3", "group4","group5"), c("V.1", "V.2", "V.3",
"V.4", "V.5")))
and the second one is a multistate 750x1500 matrix, with up to 15
*unordered* states per variable, in the form:
matrix(sample(c(1:15),25,replace=TRUE), nrow = 5, ncol=5, dimnames = list(c(
"group1", "group2","group3", "group4","group5"), c("V.1", "V.2", "V.3",
"V.4", "V.5")))
Specifically, I am looking to see which pairs of variables are correlated.
For continuos data, I would use cor() and cov() to generate the correlation
matrix and the variance-covariance matrix, which I would then visualize with
symnum() or image(). However, it is not clear to me whether this approach is
suitable for categorical data of this kind.
Since I am new to R, I would greatly appreciate any input on how to approach
this task and on efficient visualization of the results.
Many thanks in advance,
Lara
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