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
with O(n^{1/2})resolution. The basic idea is almost the same as bfslice_c
. The only different is that bfslice_eqp_c
groups samples into approximate O(n^{1/2}) groups which contain approximate O(n^{1/2}) samples and treat the groups as a sample to calculate Bayes facor. If k_z = 1, it is unconditional dependency detection method. It could be applied for nonparametric variable selecltion.
1  bfslice_eqp_c(z, x, zdim, xdim, lambda, alpha)

z 
Vector: observations of given (preselected) categorical variable, 0,1,…,k_z1 for level k_z categorical variable, should be ranked according to values of continuous variable 
x 
Vector: observations of categorical variable, 0,1,…,k_x1 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): 89112, 2017.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  n < 1000
mu < 0.2
## 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_eqp_c(z, x, zdim, xdim, lambda, alpha)

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