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

View source: R/CAMAN.R

bivariate.EMR Documentation

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

## Not run: 
# 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)

## End(Not run)
# 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")

CAMAN documentation built on Sept. 22, 2023, 5:12 p.m.

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