# dICC.SE.asympt: Asymptotic 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

 dICC.SE.asympt R Documentation

## Asymptotic Standard Error of Distance-based Intra-Class Correlation Coefficient

### Description

Calculate the standard error of the distance-based intra-class correlation coefficient based on the asymptotic distribution.

### Usage

``` dICC.SE.asympt(dist.mat, strata)
```

### 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. Currently only supports the same number of technical replicates for each biological replicate.

### 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.

`dICC`, `dICC.SE.bt`

### 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.asympt(dist.mat, strata)

```

GUniFrac documentation built on May 18, 2022, 1:06 a.m.