dICC.SE.bt: Bootstrap Standard Error of Distance-based Intra-Class...

In GUniFrac: Generalized UniFrac Distances, Distance-Based Multivariate Methods and Feature-Based Univariate Methods for Microbiome Data Analysis

Bootstrap Standard Error of Distance-based Intra-Class Correlation Coefficient

Description

Calculate the standard error of the distance-based intra-class correlation coefficient based on bootstrapping. Biological replicates are sampled by replacement. More conservative than the asymptotic approach.

Usage

 dICC.SE.bt(dist.mat, strata, B = 199)

Arguments

dist.mat	a symmetrical distance matrix between all the replicates (technical and biological replicates).
strata	a factor with each level corresponding to a biological replicate. Supports an unequal number of technical replicates for each biological replicate.
B	integer, the number of bootstrap iterations.

Value

Function dICC returns a list with the following component:

ICC	the distance-based ICC value.
SE	the standard error.

Author(s)

Jun Chen and Xianyang Zhang

References

Chen, J. & Zhang, X. 2022. dICC: Distance-based Intraclass Correlation Coefficient for Metagenomic Reproducibility Studies. submitted.

See Also

dICC, dICC.SE.asympt

Examples

# Generate the error-free measurements of 20 biological replicates, each with four dimensions
y <- matrix(rnorm(80), nrow = 20, ncol = 4)

# Generate two technical replicates for each biological replicate and add measurement errors
y1 <- y + matrix(rnorm(80, sd = 0.5), nrow = 20, ncol = 4)
y2 <- y + matrix(rnorm(80, sd = 0.5), nrow = 20, ncol = 4)
y12 <- rbind(y1, y2)

# Create the design vector
strata <- factor(rep(1 : 20, 2))

# Calculate the distance and distance-based ICC
dist.mat <- as.matrix(dist(y12))
dICC.SE.bt(dist.mat, strata)

GUniFrac documentation built on Sept. 14, 2023, 1:07 a.m.