bivariate.VEM {CAMAN} | R Documentation |
VEM algorithm for univariate data, for bivariate data and for meta data
bivariate.VEM(obs1, obs2, type, data = NULL, var1, var2, lambda1, lambda2, p, startk, numiter=5000, acc=1.e-7)
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 |
lambda1 |
Means of the first column of the observations |
lambda2 |
Means of the second column of the observations |
p |
Mixing weight |
var1 |
Variance of the first column of the observations(only for meta-analysis) |
var2 |
Variance of the second column of the observations (only for meta-analysis) |
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 |
## Not run: # 1. VEM-algorithm for bivariate normally distributed data #Examples data(rs12363681) bivariate.VEM(obs1=x,obs2=y,type="bi", data=rs12363681,startk=20) # 2.VEM for metadata data(CT) bivariate.VEM(obs1=logitTPR, obs2=logitTNR, var1=varlogitTPR, var2= varlogitTNR, type="meta", data=CT, startk=20) ## End(Not run)