| Title: | Negative Binomial Model-Based Clustering |
|---|---|
| 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. |
| Authors: | Qian Li [aut, cre] |
| Maintainer: | Qian Li <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.1.1 |
| Built: | 2024-08-21 03:14:16 UTC |
| Source: | https://github.com/cran/NB.MClust |
These functions allow you to compute (log-)density of generalized Negative Binomial distribution.
ldnb(x, theta, mu) dnb(x, theta, mu)ldnb(x, theta, mu) dnb(x, theta, mu)
x |
A positive numeric scalor or vector. Decimals and integers are both allowed. |
theta |
Value of dispersion. |
mu |
Value of mean. |
dnb |
Density of generalized Negative Binomial |
ldnb |
Log-density of generalized Negative Binomial |
ldnb(x=10.4,theta=3.2,mu=5) dnb(x=10.4,theta=3.2,mu=5)ldnb(x=10.4,theta=3.2,mu=5) dnb(x=10.4,theta=3.2,mu=5)
This function performs model-based clustering on positive integer or continuous data that follow Generalized Negative Binomial distribution.
NB.MClust(Count, K, ini.shift.mu = 0.01, ini.shift.theta = 0.01, tau0 = 10, rate = 0.9, bic = TRUE, iteration = 100)NB.MClust(Count, K, ini.shift.mu = 0.01, ini.shift.theta = 0.01, tau0 = 10, rate = 0.9, bic = TRUE, iteration = 100)
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. |
parameters |
Estimated parameters |
$prior |
Prior probability that a sample belongs to each cluster |
$mu |
Mean of each cluster |
$theta |
Dispersion of each cluster |
$posterior |
Posterior probability that a sample belongs to each cluster |
cluster |
Estimated cluster assignment |
BIC |
Value of Bayesian Information |
K |
Optional or estimated number of clusters, if input K is a vector |
# Example: data("Simulated_Count") # A 50x100 integer data frame. m1=NB.MClust(Simulated_Count,K=2:5) cluster=m1$cluster #Estimated cluster assignment k_hat=m1$K #Estimated optimal K# Example: data("Simulated_Count") # A 50x100 integer data frame. m1=NB.MClust(Simulated_Count,K=2:5) cluster=m1$cluster #Estimated cluster assignment k_hat=m1$K #Estimated optimal K
Data set for illustration: Simulated_Count
Simulated_CountSimulated_Count
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.