I have a named numeric vector called Rs. The vector has 8 elements looking like: c(L2DA.L2DF= .637, L2DA.L2G= 0.553,...).
I can convert this named numeric vector to an 8x8 correlation matrix using metafor::vec2mat(Rs) (see below).
Question: But I wonder how to assign the rownames and colnames for that correlation matrix so that these names represent the names in my original named numeric vector?
For example, in the matrix, element [1,1] = 0.637 comes from first element of my vector, so it should have the colname L2DA and rowname L2DF, and so on.
library(metafor)
dat <- read.csv("https://raw.githubusercontent.com/ilzl/i/master/j.csv")
dat$var1.var2 <- apply(dat[c("var1","var2")],1,paste0,collapse=".")
res <- rma(ri~var1.var2+0,1, data=dat)
(Rs = setNames(coef(res),sub("var1.var2","",names(coef(res)))))
(R_matrix = vec2mat(Rs))
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] 1.0000000 0.637 0.5533333 0.4180000 0.5550000 0.5678947 0.4781481 0.3675000
[2,] 0.6370000 1.000 0.2440000 0.2900000 0.4840000 0.3500000 0.4750000 0.5700000
[3,] 0.5533333 0.244 1.0000000 0.2933333 0.5100000 0.3300000 0.4775000 0.5765714
[4,] 0.4180000 0.290 0.2933333 1.0000000 0.4627778 0.5212121 0.5569565 0.4928571
[5,] 0.5550000 0.484 0.5100000 0.4627778 1.0000000 0.4695652 0.5140625 0.5313793
[6,] 0.5678947 0.350 0.3300000 0.5212121 0.4695652 1.0000000 0.5194118 0.5258333
[7,] 0.4781481 0.475 0.4775000 0.5569565 0.5140625 0.5194118 1.0000000 0.4240000
[8,] 0.3675000 0.570 0.5765714 0.4928571 0.5313793 0.5258333 0.4240000 1.0000000
First, vec2mat is masking a problem:
Rs
# L2DA.L2DF L2DA.L2G L2DA.L2L L2DA.L2M L2DA.L2P L2DA.L2V L2DF.L2G L2DF.L2L L2DF.L2M L2DF.L2P L2DF.L2V L2G.L2L L2G.L2M L2G.L2P L2M.L2L
# 0.6370000 0.5533333 0.4180000 0.5550000 0.5678947 0.4781481 0.3675000 0.2440000 0.2900000 0.4840000 0.3500000 0.4750000 0.5700000 0.2933333 0.5100000
# L2P.L2L L2P.L2M L2R.L2DA L2R.L2DF L2R.L2G L2R.L2L L2R.L2M L2R.L2P L2R.L2V L2V.L2G L2V.L2L L2V.L2M L2V.L2P
# 0.3300000 0.4775000 0.5765714 0.4627778 0.5212121 0.5569565 0.4928571 0.4695652 0.5140625 0.5313793 0.5194118 0.5258333 0.4240000
Note that there are SIX L2DA.* values, suggesting that the first column of the matrix (inferring should be "L2DA" should span between 0.637 and 0.478 ... however, it is including the L2DF.L2G value as well:
vec2mat(Rs)
# [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
# [1,] 1.0000000 0.637 0.5533333 0.4180000 0.5550000 0.5678947 0.4781481 0.3675000
# [2,] 0.6370000 1.000 0.2440000 0.2900000 0.4840000 0.3500000 0.4750000 0.5700000
# [3,] 0.5533333 0.244 1.0000000 0.2933333 0.5100000 0.3300000 0.4775000 0.5765714
# [4,] 0.4180000 0.290 0.2933333 1.0000000 0.4627778 0.5212121 0.5569565 0.4928571
# [5,] 0.5550000 0.484 0.5100000 0.4627778 1.0000000 0.4695652 0.5140625 0.5313793
# [6,] 0.5678947 0.350 0.3300000 0.5212121 0.4695652 1.0000000 0.5194118 0.5258333
# [7,] 0.4781481 0.475 0.4775000 0.5569565 0.5140625 0.5194118 1.0000000 0.4240000
# [8,] 0.3675000 0.570 0.5765714 0.4928571 0.5313793 0.5258333 0.4240000 1.0000000
For whatever reason, not all combinations are present in your coefficients; at least L2DA.L2R is missing:
nms <- unique(unlist(strsplit(names(Rs), "[.]")))
nms
# [1] "L2DA" "L2DF" "L2G" "L2L" "L2M" "L2P" "L2V" "L2R"
grep("L2DA", names(Rs), value=TRUE)
# [1] "L2DA.L2DF" "L2DA.L2G" "L2DA.L2L" "L2DA.L2M" "L2DA.L2P" "L2DA.L2V" "L2R.L2DA"
setdiff(nms, sub(".*\\.", "", grep("L2DA", names(Rs), value=TRUE)))
# [1] "L2R"
... though L2R.L2DA is present ... it appears that the assumption of vec2mat is not shared with the data.
While we could likely brute-force to match, I think this inferential-step would make it less-clear that all coefficients have been assigned correctly to the rows/columns.
Instead of vec2mat, let's do it ourselves, preserving the names.
tmp <- data.frame(nm = names(Rs), R = Rs) |>
transform(row = sub("\\..*", "", nm), col = sub(".*\\.", "", nm))
head(tmp)
# nm R row col
# L2DA.L2DF L2DA.L2DF 0.6370000 L2DA L2DF
# L2DA.L2G L2DA.L2G 0.5533333 L2DA L2G
# L2DA.L2L L2DA.L2L 0.4180000 L2DA L2L
# L2DA.L2M L2DA.L2M 0.5550000 L2DA L2M
# L2DA.L2P L2DA.L2P 0.5678947 L2DA L2P
# L2DA.L2V L2DA.L2V 0.4781481 L2DA L2V
reshape2::dcast(tmp, row ~ col, value.var = "R")
# row L2DA L2DF L2G L2L L2M L2P L2V
# 1 L2DA NA 0.6370000 0.5533333 0.4180000 0.5550000 0.5678947 0.4781481
# 2 L2DF NA NA 0.3675000 0.2440000 0.2900000 0.4840000 0.3500000
# 3 L2G NA NA NA 0.4750000 0.5700000 0.2933333 NA
# 4 L2M NA NA NA 0.5100000 NA NA NA
# 5 L2P NA NA NA 0.3300000 0.4775000 NA NA
# 6 L2R 0.5765714 0.4627778 0.5212121 0.5569565 0.4928571 0.4695652 0.5140625
# 7 L2V NA NA 0.5313793 0.5194118 0.5258333 0.4240000 NA
This is not too surprising since we know we don't have both directions of pairs. Let's take tmp and swap the two, then row-bind, then dcast again.
tmp2 <- transform(tmp, row2 = row, row = col) |>
transform(col = row2, row2 = NULL)
head(tmp2)
# nm R row col
# L2DA.L2DF L2DA.L2DF 0.6370000 L2DF L2DA
# L2DA.L2G L2DA.L2G 0.5533333 L2G L2DA
# L2DA.L2L L2DA.L2L 0.4180000 L2L L2DA
# L2DA.L2M L2DA.L2M 0.5550000 L2M L2DA
# L2DA.L2P L2DA.L2P 0.5678947 L2P L2DA
# L2DA.L2V L2DA.L2V 0.4781481 L2V L2DA
out <- rbind(tmp, tmp2) |>
reshape2::dcast(row ~ col, value.var = "R")
out
# row L2DA L2DF L2G L2L L2M L2P L2R L2V
# 1 L2DA NA 0.6370000 0.5533333 0.4180000 0.5550000 0.5678947 0.5765714 0.4781481
# 2 L2DF 0.6370000 NA 0.3675000 0.2440000 0.2900000 0.4840000 0.4627778 0.3500000
# 3 L2G 0.5533333 0.3675000 NA 0.4750000 0.5700000 0.2933333 0.5212121 0.5313793
# 4 L2L 0.4180000 0.2440000 0.4750000 NA 0.5100000 0.3300000 0.5569565 0.5194118
# 5 L2M 0.5550000 0.2900000 0.5700000 0.5100000 NA 0.4775000 0.4928571 0.5258333
# 6 L2P 0.5678947 0.4840000 0.2933333 0.3300000 0.4775000 NA 0.4695652 0.4240000
# 7 L2R 0.5765714 0.4627778 0.5212121 0.5569565 0.4928571 0.4695652 NA 0.5140625
# 8 L2V 0.4781481 0.3500000 0.5313793 0.5194118 0.5258333 0.4240000 0.5140625 NA
If you need this as a simple matrix, then we can do this:
out2 <- as.matrix(out[,-1])
dimnames(out2) <- list(out$row, colnames(out)[-1])
out2
# L2DA L2DF L2G L2L L2M L2P L2R L2V
# L2DA NA 0.6370000 0.5533333 0.4180000 0.5550000 0.5678947 0.5765714 0.4781481
# L2DF 0.6370000 NA 0.3675000 0.2440000 0.2900000 0.4840000 0.4627778 0.3500000
# L2G 0.5533333 0.3675000 NA 0.4750000 0.5700000 0.2933333 0.5212121 0.5313793
# L2L 0.4180000 0.2440000 0.4750000 NA 0.5100000 0.3300000 0.5569565 0.5194118
# L2M 0.5550000 0.2900000 0.5700000 0.5100000 NA 0.4775000 0.4928571 0.5258333
# L2P 0.5678947 0.4840000 0.2933333 0.3300000 0.4775000 NA 0.4695652 0.4240000
# L2R 0.5765714 0.4627778 0.5212121 0.5569565 0.4928571 0.4695652 NA 0.5140625
# L2V 0.4781481 0.3500000 0.5313793 0.5194118 0.5258333 0.4240000 0.5140625 NA
diag(out2) <- 1
out2
# L2DA L2DF L2G L2L L2M L2P L2R L2V
# L2DA 1.0000000 0.6370000 0.5533333 0.4180000 0.5550000 0.5678947 0.5765714 0.4781481
# L2DF 0.6370000 1.0000000 0.3675000 0.2440000 0.2900000 0.4840000 0.4627778 0.3500000
# L2G 0.5533333 0.3675000 1.0000000 0.4750000 0.5700000 0.2933333 0.5212121 0.5313793
# L2L 0.4180000 0.2440000 0.4750000 1.0000000 0.5100000 0.3300000 0.5569565 0.5194118
# L2M 0.5550000 0.2900000 0.5700000 0.5100000 1.0000000 0.4775000 0.4928571 0.5258333
# L2P 0.5678947 0.4840000 0.2933333 0.3300000 0.4775000 1.0000000 0.4695652 0.4240000
# L2R 0.5765714 0.4627778 0.5212121 0.5569565 0.4928571 0.4695652 1.0000000 0.5140625
# L2V 0.4781481 0.3500000 0.5313793 0.5194118 0.5258333 0.4240000 0.5140625 1.0000000
As a function:
myfun <- function(Rs) {
tmp <- data.frame(nm = names(Rs), R = Rs) |>
transform(row = sub("\\..*", "", nm), col = sub(".*\\.", "", nm))
tmp2 <- transform(tmp, row2 = row, row = col) |>
transform(col = row2, row2 = NULL)
out <- rbind(tmp, tmp2) |>
tidyr::pivot_wider(id_cols = "row", names_from = "col", values_from = "R")
out <- out[order(match(out$row, colnames(out))),]
out2 <- as.matrix(out[,-1])
dimnames(out2) <- list(out$row, colnames(out)[-1])
diag(out2) <- 1
out2
}
Rs
# L2DA.L2DF L2DA.L2G L2DA.L2L L2DA.L2M L2DA.L2P L2DA.L2V L2DF.L2G L2DF.L2L L2DF.L2M L2DF.L2P L2DF.L2V L2G.L2L L2G.L2M L2G.L2P L2M.L2L
# 0.6370000 0.5533333 0.4180000 0.5550000 0.5678947 0.4781481 0.3675000 0.2440000 0.2900000 0.4840000 0.3500000 0.4750000 0.5700000 0.2933333 0.5100000
# L2P.L2L L2P.L2M L2R.L2DA L2R.L2DF L2R.L2G L2R.L2L L2R.L2M L2R.L2P L2R.L2V L2V.L2G L2V.L2L L2V.L2M L2V.L2P
# 0.3300000 0.4775000 0.5765714 0.4627778 0.5212121 0.5569565 0.4928571 0.4695652 0.5140625 0.5313793 0.5194118 0.5258333 0.4240000
myfun(Rs)
# L2DF L2G L2L L2M L2P L2V L2DA L2R
# L2DF 1.0000000 0.3675000 0.2440000 0.2900000 0.4840000 0.3500000 0.6370000 0.4627778
# L2G 0.3675000 1.0000000 0.4750000 0.5700000 0.2933333 0.5313793 0.5533333 0.5212121
# L2L 0.2440000 0.4750000 1.0000000 0.5100000 0.3300000 0.5194118 0.4180000 0.5569565
# L2M 0.2900000 0.5700000 0.5100000 1.0000000 0.4775000 0.5258333 0.5550000 0.4928571
# L2P 0.4840000 0.2933333 0.3300000 0.4775000 1.0000000 0.4240000 0.5678947 0.4695652
# L2V 0.3500000 0.5313793 0.5194118 0.5258333 0.4240000 1.0000000 0.4781481 0.5140625
# L2DA 0.6370000 0.5533333 0.4180000 0.5550000 0.5678947 0.4781481 1.0000000 0.5765714
# L2R 0.4627778 0.5212121 0.5569565 0.4928571 0.4695652 0.5140625 0.5765714 1.0000000