search for: s_ij

my major is bioinformatics, and i'm trying to cluster ( agglomerate the closest pari of observations ) in R. i have already got my own similarities metric, but do not know how to clust it based on similarities instead of dissimilarities. since the help document of hierclust mentions the parameter "sim", which seems good to me, but it doesn't appear in the code of hierclust()

...), > nrow = 2, byrow = TRUE) > } > rj<-positions(N)*spacing # all positions in the 2N x 2N array > rj<-rj[1:2,-((dim(rj)[2]-1)/2+1)] # remove rj=(0,0) > > mod<-function(x){sqrt(x[1]^2+x[2]^2)} # modulus > > sij <-function(rj){ > rij=mod(rj-ri) > cos_ij=rj[1]/rij > sin_ij=rj[2]/rij > > A<-(1-1i*k*rij)*(3*cos_ij^2-1)*exp(1i*k*rij)/(rij^3) > B<-k^2*sin_ij^2*exp(1i*k*rij)/rij > > sij<-A+B > } > > s_ij<-apply(rj,2,sij) > S<-sum(s_ij) > alpha_s/(1-alpha_s*S) > } > alpha_c() This function is to be...

...s the binary state. I would like to know in how many cases any two persons a. both have "1", b. the first has "0" - the second has "1", c. the first has "0" - the second has "0", d. both have "0". There will be choose(9,2) sums, denoted s_ij for 1<=i<j<=9, where i and j are running indices for an "id"-pair. Please help, this is way beyond my knowledge of R! Thank you, Serguei Kaniovski -- ___________________________________________________________________ Austrian Institute of Economic Research (WIFO) Name: Serg...

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and code the likelihood fun...