pairwise.bzinb | R Documentation |
For each pair of rows in the data, underlying corelation (\rho
) is calculated based on
bivariate zero-inflated negative binomial (BZINB) model.
pairwise.bzinb(
data,
nonzero.prop = TRUE,
fullParam = FALSE,
showFlag = FALSE,
nsample = NULL,
...
)
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 |
fullParam |
logical. If |
showFlag |
logical. If |
nsample |
positive integer. If provided, |
... |
Other arguments passed on to |
a table of pairwise underlying correlation (\rho
) 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
Hunyong Cho, Chuwen Liu, Jinyoung Park, and Di Wu
Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation), "A bivariate zero-inflated negative binomial model for identifying underlying dependence"
# 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.