This is my dataset,
df1 <- "ID t res
1 1 -1.5
1 2 -1.5
1 3 0.5
1 4 0.5
2 1 -0.5
2 2 -0.5
2 3 -2.0
2 4 -1.5
2 5 1.5"
df1 <- read.table(text = df1, header = TRUE)
What I like to do is,
Covert this to a matrix.
Transform from long to wide
ID 1 2 3 4 5
1 -1.5 -1.5 0.5 0.5 NA
2 -0.5 -0.5 -2.0 -1.5 1.5
Finally Create a 5 x 5 covariance matrix like this.
1 2 3 4 5
0.5 0.5 -1.25 -1 0
0.5 0.5 -1.25 -1 0
-1.25 -1.25 3.125 2.5 0
-1 -1 2.5 2 0
0 0 0 0 0
I am able to do this with a dataframe , using functions pivot_wide or reshapeand cov which is manual and tedious. However i am not sure how to perform these steps when the data object is a matrix. Any suggestion is very helpful. Thanks.
Try this.
cov(reshape(as.data.frame(M), idvar='ID', timevar='t', direction='wide')[, -1])
# res.1 res.2 res.3 res.4 res.5
# res.1 0.50 0.50 -1.250 -1.0 NA
# res.2 0.50 0.50 -1.250 -1.0 NA
# res.3 -1.25 -1.25 3.125 2.5 NA
# res.4 -1.00 -1.00 2.500 2.0 NA
# res.5 NA NA NA NA NA
Data:
M <- structure(c(1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 3, 4, 1, 2, 3, 4,
5, -1.5, -1.5, 0.5, 0.5, -0.5, -0.5, -2, -1.5, 1.5), dim = c(9L,
3L), dimnames = list(NULL, c("ID", "t", "res")))
## or
M <- as.matrix(df1) ## from OP