CAMAN (version 0.78)

bivariate.EM: EM-algorithm for bivariate normally distributed data

Description

Function

Usage

bivariate.EM(obs1, obs2, type, data = NULL,
             var1, var2, 
             corr, lambda1, lambda2, p, 
             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. If not NULL, obs1, obs2, var1, var2 and corr will be looked for in data

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

Mixing weight

numiter

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

acc

convergence criterion. Default is 1.e-7

class

classification of studies?

Examples

if (FALSE) {
# 1.EM and classification for bivariate data with starting values
data(rs12363681)
lambda1<-c(1540.97, 837.12, 945.40, 1053.69)
lambda2<-c(906.66, 1371.81 ,1106.01,973.11)
p<-c(0.05,0.15,0.6,0.2)
test<-bivariate.EM(obs1=x, obs2=y, type="bi", lambda1=lambda1,lambda2=lambda2,
                   p=p,data=rs12363681,class="TRUE")
# scatter plot with ellipse
plot(test, ellipse=TRUE)
# scatter plot without ellipse
plot(test, ellipse=FALSE)
}
# 2. EM-algorithm for a diagnostic meta-analysis with bivariate 
#    normally distributed data and study specific fixed variances
data(CT)
p2<-c(0.4,0.6)
lamlog12<-c(2.93,3.22)
lamlog22<-c(2.5,1.5)
ct.m1 <- bivariate.EM(obs1=logitTPR, obs2=logitTNR, 
                      var1=varlogitTPR, var2=varlogitTNR,
                      type="meta", lambda1=lamlog12, lambda2=lamlog22,
                      p=p2,data=CT,class="TRUE")