Concatentates random vectors.
cc(..., recursive = FALSE)
# S3 method for rv
c(..., recursive = FALSE)objects to be concatenated. Can be a mixture of constants and rv objects.
logical. If recursive = TRUE, the function recursively descends through lists (and pairlists) combining all their elements into a vector.
NOTE: recursive has not yet been tested.
cc is a function that works for both non-rv and other vectors. To
make code compatible for both constant vectors and rv objects, one can use
cc instead of c.
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(2)
y <- rvbern(2, prob=0.5)
z <- c(x, y)
print(z)
#> mean sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1] -0.019 0.98 -2.2 -1.9 -0.68 -0.028 0.63 1.9 2.3 4000
#> [2] -0.014 1.00 -2.4 -1.9 -0.69 -0.012 0.66 1.9 2.3 4000
#> [3] 0.488 0.50 0.0 0.0 0.00 0.000 1.00 1.0 1.0 4000
#> [4] 0.516 0.50 0.0 0.0 0.00 1.000 1.00 1.0 1.0 4000
z1 <- cc(1, z)
z2 <- c(as.rv(1), z)
z3 <- c(as.rv(1), z)
print(z1)
#> mean sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1] 1.000 0.00 1.0 1.0 1.00 1.000 1.00 1.0 1.0 1
#> [2] -0.019 0.98 -2.2 -1.9 -0.68 -0.028 0.63 1.9 2.3 4000
#> [3] -0.014 1.00 -2.4 -1.9 -0.69 -0.012 0.66 1.9 2.3 4000
#> [4] 0.488 0.50 0.0 0.0 0.00 0.000 1.00 1.0 1.0 4000
#> [5] 0.516 0.50 0.0 0.0 0.00 1.000 1.00 1.0 1.0 4000
print(z2)
#> mean sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1] 1.000 0.00 1.0 1.0 1.00 1.000 1.00 1.0 1.0 1
#> [2] -0.019 0.98 -2.2 -1.9 -0.68 -0.028 0.63 1.9 2.3 4000
#> [3] -0.014 1.00 -2.4 -1.9 -0.69 -0.012 0.66 1.9 2.3 4000
#> [4] 0.488 0.50 0.0 0.0 0.00 0.000 1.00 1.0 1.0 4000
#> [5] 0.516 0.50 0.0 0.0 0.00 1.000 1.00 1.0 1.0 4000
print(z3)
#> mean sd 1% 2.5% 25% 50% 75% 97.5% 99% sims
#> [1] 1.000 0.00 1.0 1.0 1.00 1.000 1.00 1.0 1.0 1
#> [2] -0.019 0.98 -2.2 -1.9 -0.68 -0.028 0.63 1.9 2.3 4000
#> [3] -0.014 1.00 -2.4 -1.9 -0.69 -0.012 0.66 1.9 2.3 4000
#> [4] 0.488 0.50 0.0 0.0 0.00 0.000 1.00 1.0 1.0 4000
#> [5] 0.516 0.50 0.0 0.0 0.00 1.000 1.00 1.0 1.0 4000