bivariate.mixalg {CAMAN} | R Documentation |
Function
bivariate.mixalg(obs1, obs2, type, data = NULL, var1, var2, corr, lambda1, lambda2, p,startk, 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 |
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 |
## 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)