man/findSyncVarRegions.Rd

   1 % Generated by roxygen2 (4.0.2): do not edit by hand
   2 \name{findSyncVarRegions}
   3 \alias{findSyncVarRegions}
   4 \title{Direct clustering from a neighborhoods graph, or get regions from (Poisson)
   5 distribution parameters optimization, using convex relaxation.}
   6 \usage{
   7 findSyncVarRegions(method, M, k, alpha, gmode, K, dtype, cmeth, pcoef = 1,
   8   h = 0.001, eps = 0.001, maxit = 1000, showLL = TRUE, disp = TRUE)
   9 }
  10 \arguments{
  11 \item{method}{Global method: "direct" or "convex"}
  12
  13 \item{M}{Matrix of observations in rows, the two last columns
  14 corresponding to geographic coordinates;
  15 set to NULL to use our initial dataset (625 rows / 9 years)}
  16
  17 \item{k}{Number of neighbors}
  18
  19 \item{alpha}{Weight parameter for intra-neighborhoods distance computations;
  20 0 = take only geographic coordinates into account;
  21 1 = take only observations over the years into account;
  22 in-between : several levels of compromise;
  23 -1 or any negative value : use a heuristic to choose alpha.}
  24
  25 \item{gmode}{Neighborhood type. 0 = reduced [mutual] kNN; 1 = augmented kNN (symmetric);
  26 2 = normal kNN; 3 = one NN in each quadrant; (NON-symmetric).
  27 NOTE: gmode==3 automatically sets k==4 (at most!)}
  28
  29 \item{K}{Number of clusters}
  30
  31 \item{dtype}{Distance type, in {"simple","spath","ectd"}.
  32 NOTE: better avoid "simple" if gmode>=2}
  33
  34 \item{cmeth}{Clustering method, in {"KM","HC","spec"} for k-means (distances based)
  35 or hierarchical clustering, or spectral clustering (only if gmode>=2)}
  36
  37 \item{pcoef}{Penalty value for convex optimization [default: 1.0]}
  38
  39 \item{h}{Step in the min LL algorithm [default: 1e-3]}
  40
  41 \item{eps}{Threshold to stop min.LL iterations [default: 1e-3]}
  42
  43 \item{maxit}{Maximum number of iterations in the min LL algo [default: 1e3]}
  44
  45 \item{showLL}{Print trace of log-likelihood evolution [default: true]}
  46
  47 \item{disp}{True [default] for interactive display (otherwise nothing gets plotted)}
  48 }
  49 \value{
  50 list with the following entries. M: data matrix in input; NI: computed neighborhoods;
  51         dists: computed distances matrix; clusts: partition into K clusters, as an integer vector;
  52         cxpar: parameters obtained after convex optimization (if applicable)
  53 }
  54 \description{
  55 Direct clustering from a neighborhoods graph, or get regions from (Poisson)
  56 distribution parameters optimization, using convex relaxation.
  57 }
  58 \examples{
  59 cvr = findSyncVarRegions("convex",M=NULL,k=10,alpha=0.1,gmode=1,K=5,dtype="spath",cmeth="HC")
  60 drawMapWithSitez(cvr$M, cvr$clusters)
  61 drawNeighboroodGraph(cvr$M, cvr$NI)
  62 }
  63