Description Usage Arguments Value References See Also Examples
Conditional dependency detection between a level k_x (k_x > 1) categorical variable x
and a continuous variable y
via Bayes factor given a level k_z categorical variable z
. If k_z = 1, it is unconditional dependency detection method. It could be applied for non-parametric variable selecltion.
1 | bfslice_c(z, x, zdim, xdim, lambda, alpha)
|
z |
Vector: observations of given (preselected) categorical variable, 0,1,…,k_z-1 for level k_z categorical variable, should be ranked according to values of continuous variable |
x |
Vector: observations of categorical variable, 0,1,…,k_x-1 for level k_x categorical variable, should be ranked according to values of continuous variable |
zdim |
Level of |
xdim |
Level of |
lambda |
|
alpha |
|
Value of Bayes factor (nonnegative). Bayes factor could be treated as a statistic and one can take some threshold then calculates the corresponded Type I error rate. One can also take the value of Bayes factor for judgement.
Jiang, B., Ye, C. and Liu, J.S. Bayesian nonparametric tests via sliced inverse modeling. Bayesian Analysis, 12(1): 89-112, 2017.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | n <- 100
mu <- 0.5
## Unconditional test
y <- c(rnorm(n, -mu, 1), rnorm(n, mu, 1))
x <- c(rep(0, n), rep(1, n))
z <- rep(0, 2*n)
## Conditional test
y <- c(rnorm(n, -mu, 1), rnorm(n, mu, 1))
x <- c(rep(0, n/5), rep(1, n), rep(0, 4*n/5))
z <- c(rep(0, n), rep(1, n))
z <- z[order(y)]
x <- x[order(y)]
zdim <- max(z) + 1
xdim <- max(x) + 1
lambda <- 1.0
alpha <- 1.0
bfval <- bfslice_c(z, x, zdim, xdim, lambda, alpha)
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