Combines random vectors by columns (cbind.rv) or rows
(rbind.rv).
# S3 method for rv
cbind(..., deparse.level = 1)vectors or matrices, can be rv objects
(passed on to cbind)
Kerman, J. and Gelman, A. (2007). Manipulating and Summarizing Posterior Simulations Using Random Variable Objects. Statistics and Computing 17:3, 235-244.
See also vignette("rv").
x <- rvnorm(10)
y <- rvnorm(10)
cbind(x, y)
#> mean sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1,1] -0.0032 0.99 -2.3 -2.0 -0.64 -0.0013 0.65 1.9 2.3 4000
#> [2,1] 0.0120 1.00 -2.2 -1.9 -0.67 0.0367 0.69 2.0 2.3 4000
#> [3,1] -0.0411 0.99 -2.3 -2.0 -0.72 -0.0117 0.63 1.9 2.3 4000
#> [4,1] -0.0057 1.01 -2.3 -2.0 -0.68 -0.0185 0.69 1.9 2.4 4000
#> [5,1] 0.0209 1.00 -2.3 -2.0 -0.67 0.0288 0.71 2.0 2.3 4000
#> [6,1] -0.0210 1.00 -2.2 -1.9 -0.70 -0.0323 0.64 1.9 2.3 4000
#> [7,1] 0.0091 1.00 -2.3 -2.0 -0.66 -0.0024 0.66 2.0 2.3 4000
#> [8,1] -0.0024 1.00 -2.3 -1.9 -0.67 0.0125 0.67 1.9 2.4 4000
#> [9,1] -0.0123 1.00 -2.3 -2.0 -0.71 0.0225 0.67 2.0 2.3 4000
#> [10,1] 0.0117 0.99 -2.3 -1.9 -0.64 0.0290 0.63 2.0 2.4 4000
#> [1,2] 0.0253 1.00 -2.2 -1.9 -0.67 0.0248 0.70 2.0 2.3 4000
#> [2,2] -0.0021 1.01 -2.3 -2.0 -0.69 -0.0096 0.68 2.0 2.3 4000
#> [3,2] -0.0225 1.01 -2.3 -2.0 -0.73 -0.0308 0.66 2.0 2.4 4000
#> [4,2] 0.0090 1.00 -2.3 -1.9 -0.69 0.0233 0.69 2.0 2.3 4000
#> [5,2] -0.0198 1.00 -2.3 -1.9 -0.70 -0.0309 0.67 2.0 2.3 4000
#> [6,2] -0.0089 1.01 -2.3 -2.0 -0.70 -0.0194 0.70 1.9 2.3 4000
#> [7,2] 0.0356 1.00 -2.3 -1.9 -0.63 0.0449 0.70 2.0 2.3 4000
#> [8,2] 0.0091 0.98 -2.2 -1.9 -0.68 -0.0084 0.70 1.9 2.4 4000
#> [9,2] 0.0095 0.99 -2.2 -1.9 -0.65 0.0046 0.68 2.0 2.3 4000
#> [10,2] 0.0120 1.00 -2.3 -1.9 -0.68 0.0065 0.69 1.9 2.3 4000
rbind(x, y)
#> mean sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1,1] -0.0032 0.99 -2.3 -2.0 -0.64 -0.0013 0.65 1.9 2.3 4000
#> [2,1] 0.0253 1.00 -2.2 -1.9 -0.67 0.0248 0.70 2.0 2.3 4000
#> [1,2] 0.0120 1.00 -2.2 -1.9 -0.67 0.0367 0.69 2.0 2.3 4000
#> [2,2] -0.0021 1.01 -2.3 -2.0 -0.69 -0.0096 0.68 2.0 2.3 4000
#> [1,3] -0.0411 0.99 -2.3 -2.0 -0.72 -0.0117 0.63 1.9 2.3 4000
#> [2,3] -0.0225 1.01 -2.3 -2.0 -0.73 -0.0308 0.66 2.0 2.4 4000
#> [1,4] -0.0057 1.01 -2.3 -2.0 -0.68 -0.0185 0.69 1.9 2.4 4000
#> [2,4] 0.0090 1.00 -2.3 -1.9 -0.69 0.0233 0.69 2.0 2.3 4000
#> [1,5] 0.0209 1.00 -2.3 -2.0 -0.67 0.0288 0.71 2.0 2.3 4000
#> [2,5] -0.0198 1.00 -2.3 -1.9 -0.70 -0.0309 0.67 2.0 2.3 4000
#> [1,6] -0.0210 1.00 -2.2 -1.9 -0.70 -0.0323 0.64 1.9 2.3 4000
#> [2,6] -0.0089 1.01 -2.3 -2.0 -0.70 -0.0194 0.70 1.9 2.3 4000
#> [1,7] 0.0091 1.00 -2.3 -2.0 -0.66 -0.0024 0.66 2.0 2.3 4000
#> [2,7] 0.0356 1.00 -2.3 -1.9 -0.63 0.0449 0.70 2.0 2.3 4000
#> [1,8] -0.0024 1.00 -2.3 -1.9 -0.67 0.0125 0.67 1.9 2.4 4000
#> [2,8] 0.0091 0.98 -2.2 -1.9 -0.68 -0.0084 0.70 1.9 2.4 4000
#> [1,9] -0.0123 1.00 -2.3 -2.0 -0.71 0.0225 0.67 2.0 2.3 4000
#> [2,9] 0.0095 0.99 -2.2 -1.9 -0.65 0.0046 0.68 2.0 2.3 4000
#> [1,10] 0.0117 0.99 -2.3 -1.9 -0.64 0.0290 0.63 2.0 2.4 4000
#> [2,10] 0.0120 1.00 -2.3 -1.9 -0.68 0.0065 0.69 1.9 2.3 4000