bivariate.mixalg: EM algorithm and classification for univariate data, for...

In CAMAN: Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN

EM algorithm and classification for univariate data, for bivariate data and for meta data

Description

Function

Usage

bivariate.mixalg(obs1, obs2, type, data = NULL, 
                 var1, var2, corr, lambda1, lambda2,
                 p,startk, numiter=5000, acc=1.e-7, class)

Arguments

obs1	the first column of the observations

obs2	the second column of the observations

type	kind of data

data	an optional data frame

var1	Variance of the first column of the observations(except meta-analysis)

var2	Variance of the second column of the observations (except meta-analysis)

corr	correlation coefficient

lambda1

Means of the first column of the observations

lambda2

Means of the second column of the observations

p	Probability

startk

starting/maximal number of components. This number will be used to compute the grid in the VEM. Default is 20.

numiter

parameter to control the maximal number of iterations in the VEM and EM loops. Default is 5000.

acc	convergence criterion. Default is 1.e-7

class

classification of studies

Examples

## Not run: 
#1.EM and classification for bivariate data
#Examples
data(rs12363681)
test <- bivariate.mixalg(obs1=x, obs2=y, type="bi", 
                         lambda1=0, lambda2=0, p=0, 
                         data=rs12363681, startk=20, class="TRUE")
#scatter plot with ellipse
plot(test)
#scatter plot without ellipse
plot(test, ellipse = FALSE)
#2.EM and classification for meta data
#Examples
data(CT)
bivariate.mixalg(obs1=logitTPR, obs2=logitTNR, 
                 var1=varlogitTPR, var2=varlogitTNR,
                 type="meta", lambda1=0, lambda2=0,
                 p=0,data=CT,startk=20,class="TRUE")

## End(Not run)

CAMAN documentation built on Sept. 19, 2023, 3:01 p.m.