Package 'truh'

Nearest neighbor computation for the TRUH statistic

Description

For a given $d$ dimensional vector $\mathbf{y}$, this function finds the nearest neighbor of $\mathbf{y}$ in a $n\times d$ matrix $\mathbf{U}$.

Usage

nearest(y, U, n, d)

Arguments

y	a $d$ dimensional vector.
U	a $n\times d$ matrix where $n$ represents the sample size and $d$ is the dimension of each sample.
n	the sample size.
d	dimension of each sample.

Value

1. d1 - nearest neighbor of $\mathbf{y}$ in $\mathbf{U}$

2. d2 - nearest neighbor of d1 in $\mathbf{U}$

See Also

truh

Examples

library(truh)
n = 100
d = 3
set.seed(1)
y = rnorm(3)
set.seed(2)
U = matrix(rnorm(n*d),nrow=n,ncol=d)
out = nearest(y,U,n,d)

---

TRUH test statistic

Description

TRUH test statistic for nonparametric two sample testing under heterogeneity.

Usage

truh(V, U, B, fc = 1, ncores = 2)

Arguments

V	$m\times d$ matrix where $m$ represents the sample size and $d$ is the dimension of each sample.
U	a $n\times d$ matrix where $n$ represents the sample size and $d$ is the dimension of each sample with $m\ll n$.
B	number of bootstrap samples.
fc	fold change constant. The default value is 1. See equation (2.8) of the referenced paper for more details.
ncores	the number of computing cores available. The default value is 2.

Value

1. teststat - TRUH test statistic.

2. k.hat - number of clusters detected in the uninfected sample.

3. pval - The maximum p-value across the detected clusters.

4. pval_all - p-value for each cluster.

5. dist.null_all - the approximate bootstrapped based null distribution.

References

Banerjee, Trambak, Bhaswar B. Bhattacharya, and Gourab Mukherjee. "A nearest-neighbor based nonparametric test for viral remodeling in heterogeneous single-cell proteomic data." The Annals of Applied Statistics 14, no. 4 (2020): 1777-1805.

See Also

nearest

Examples

library(truh)
n = 500
m = 10
d = 3
set.seed(1)
V = matrix(rnorm(m*d),nrow=m,ncol=d)
set.seed(2)
U = matrix(rnorm(n*d),nrow=n,ncol=d)
out = truh(V,U,100)

---