# pairwise.bzinb: Pairwise underlying correlation based on bivariate... In bzinb: Bivariate Zero-Inflated Negative Binomial Model Estimator

## Description

For each pair of rows in the data, underlying corelation (ρ) is calculated based on bivariate zero-inflated negative binomial (BZINB) model.

## Usage

 ```1 2``` ```pairwise.bzinb(data, nonzero.prop = TRUE, fullParam = FALSE, showFlag = FALSE, nsample = NULL, ...) ```

## Arguments

 `data` a matrix with nonnegative integers. rows represent the feature (or gene), and columns represent the sample. If not integers, rounded to the nearest integers. `nonzero.prop` logical. If `TRUE`, proportion of nonzero for each of the pair is returned. `fullParam` logical. If `TRUE`, estimates of all parameters are returned. `showFlag` logical. If `TRUE`, for each pair, the estimates are printed out. `nsample` positive integer. If provided, `nsample` random pairs will only be considered for correlation. A non-integer will be rounded to the nearest integer. `...` Other arguments passed on to `bzinb` function.

## Value

a table of pairwise underlying correlation (ρ) and related statistics.

• `1` row number of the first vector of the pair

• `2` row number of the second vector of the pair

• `pair` row numbers of the pair

• `rho` underlying correlation estimate

• `se.rho` standard error of the underlying correlation estimate

• `nonzero.1, nonzero.2` non-zero proportion of the first and the second vector. Returned if `nonzero.prop` is `TRUE`.

• `nonzero.min` pairwise minimum of non-zero proportions Returned if `nonzero.prop` is `TRUE`.

• `a0, a1, ..., p4` parameter estimates

• `se.a0, se.a1, ..., se.p4` standard error of the parameter estimates

• `logLik` log-likelihood of the maximum likelihood estimates

## Author(s)

Hunyong Cho, Chuwen Liu, Jinyoung Park, and Di Wu

## References

Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation), "A bivariate zero-inflated negative binomial model for identifying underlying dependence"

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```# generating four random vectors set.seed(7) data1 <- rbzinb(n = 20, a0 = 0.5, a1 = 1, a2 = 1, b1 = 1, b2 = 1, p1 = 0.5, p2 = 0.2, p3 = 0.2, p4 = 0.1) set.seed(14) data2 <- rbzinb(n = 20, a0 = 0.5, a1 = 1, a2 = 1, b1 = 2, b2 = 2, p1 = 0.5, p2 = 0.2, p3 = 0.2, p4 = 0.1) data3 <- t(cbind(data1, data2)) # calculating all pairwise underlying correlations ## Not run: pairwise.bzinb(data3, showFlag = TRUE) ```

bzinb documentation built on Dec. 8, 2019, 9:12 a.m.