| Title: | Negative Binomial Model-Based Clustering |
| Version: | 1.1.1 |
| Description: | Model-based clustering of high-dimensional non-negative data that follow Generalized Negative Binomial distribution. All functions in this package applies to either continuous or integer data. Correlation between variables are allowed, while samples are assumed to be independent. |
| Depends: | R (≥ 3.3.3) |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1 |
| Imports: | MASS, utils |
| NeedsCompilation: | no |
| Packaged: | 2017-06-03 01:07:56 UTC; 4466693 |
| Author: | Qian Li [aut, cre] |
| Maintainer: | Qian Li <qian.li10000@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2017-06-03 12:08:50 UTC |
NB.MClust Function
Description
This function performs model-based clustering on positive integer or continuous data that follow Generalized Negative Binomial distribution.
Usage
NB.MClust(Count, K, ini.shift.mu = 0.01, ini.shift.theta = 0.01,
tau0 = 10, rate = 0.9, bic = TRUE, iteration = 100)
Arguments
Count |
Data matrix of discrete counts.This function groups rows of the data matrix. |
K |
Number of clusters or components specified. It can be a positive integer or a vector of positive integer. |
ini.shift.mu |
Initial value in EM algorithm for the shift between clusters in mean. |
ini.shift.theta |
Initial value in EM algorithm for the shift between clusters in dispersion. |
tau0 |
Initial value of anealing rates in EM Algorithm. Default and suggested value is 10. |
rate |
Stochastic decreasing speed for anealing rate. Default and suggested value is 0.9 |
bic |
Whether Bayesian Information should be computed when K is an integer. BIC is forced to be TRUE when K is a vector. |
iteration |
Maximum number of iterations in EM Algorithm, default at 50. |
Value
parameters |
Estimated parameters |
$prior |
Prior probability that a sample belongs to each cluster |
$mu |
Mean of each cluster |
$theta |
Dispersion of each cluster |
$posterior |
Posterior probability that a sample belongs to each cluster |
cluster |
Estimated cluster assignment |
BIC |
Value of Bayesian Information |
K |
Optional or estimated number of clusters, if input K is a vector |
Examples
# Example:
data("Simulated_Count") # A 50x100 integer data frame.
m1=NB.MClust(Simulated_Count,K=2:5)
cluster=m1$cluster #Estimated cluster assignment
k_hat=m1$K #Estimated optimal K
Data set for illustration: Simulated_Count
Description
Data set for illustration: Simulated_Count
Usage
Simulated_Count
Format
A simulated data frame with 50 rows (i.e. samples) and 100 columns (i.e. variables ). It can be viewed as simulated RNA-Seq integer counts of 100 genes for 50 patients.
dnb, ldnb Functions
Description
These functions allow you to compute (log-)density of generalized Negative Binomial distribution.
Usage
ldnb(x, theta, mu)
dnb(x, theta, mu)
Arguments
x |
A positive numeric scalor or vector. Decimals and integers are both allowed. |
theta |
Value of dispersion. |
mu |
Value of mean. |
Value
dnb |
Density of generalized Negative Binomial |
ldnb |
Log-density of generalized Negative Binomial |
Examples
ldnb(x=10.4,theta=3.2,mu=5)
dnb(x=10.4,theta=3.2,mu=5)