bivariate.EM {CAMAN} | R Documentation |
Function
bivariate.EM(obs1, obs2, type, data = NULL, var1, var2, corr, lambda1, lambda2, p, numiter=5000,acc=1.e-7,class)
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 |
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? |
## 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")