add documentation (remove .gitignore under man/)

[synclust.git] / man / findSyncVarRegions.Rd

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+% Generated by roxygen2 (4.0.2): do not edit by hand
+\name{findSyncVarRegions}
+\alias{findSyncVarRegions}
+\title{Direct clustering from a neighborhoods graph, or get regions from (Poisson)
+distribution parameters optimization, using convex relaxation.}
+\usage{
+findSyncVarRegions(method, M, k, alpha, gmode, K, dtype, cmeth, pcoef = 1,
+  h = 0.001, eps = 0.001, maxit = 1000, showLL = TRUE, disp = TRUE)
+}
+\arguments{
+\item{method}{Global method: "direct" or "convex"}
+
+\item{M}{Matrix of observations in rows, the two last columns
+corresponding to geographic coordinates;
+set to NULL to use our initial dataset (625 rows / 9 years)}
+
+\item{k}{Number of neighbors}
+
+\item{alpha}{Weight parameter for intra-neighborhoods distance computations;
+0 = take only geographic coordinates into account;
+1 = take only observations over the years into account;
+in-between : several levels of compromise;
+-1 or any negative value : use a heuristic to choose alpha.}
+
+\item{gmode}{Neighborhood type. 0 = reduced [mutual] kNN; 1 = augmented kNN (symmetric);
+2 = normal kNN; 3 = one NN in each quadrant; (NON-symmetric).
+NOTE: gmode==3 automatically sets k==4 (at most!)}
+
+\item{K}{Number of clusters}
+
+\item{dtype}{Distance type, in {"simple","spath","ectd"}.
+NOTE: better avoid "simple" if gmode>=2}
+
+\item{cmeth}{Clustering method, in {"KM","HC","spec"} for k-means (distances based)
+or hierarchical clustering, or spectral clustering (only if gmode>=2)}
+
+\item{pcoef}{Penalty value for convex optimization [default: 1.0]}
+
+\item{h}{Step in the min LL algorithm [default: 1e-3]}
+
+\item{eps}{Threshold to stop min.LL iterations [default: 1e-3]}
+
+\item{maxit}{Maximum number of iterations in the min LL algo [default: 1e3]}
+
+\item{showLL}{Print trace of log-likelihood evolution [default: true]}
+
+\item{disp}{True [default] for interactive display (otherwise nothing gets plotted)}
+}
+\value{
+list with the following entries. M: data matrix in input; NI: computed neighborhoods;
+        dists: computed distances matrix; clusts: partition into K clusters, as an integer vector;
+        cxpar: parameters obtained after convex optimization (if applicable)
+}
+\description{
+Direct clustering from a neighborhoods graph, or get regions from (Poisson)
+distribution parameters optimization, using convex relaxation.
+}
+\examples{
+cvr = findSyncVarRegions("convex",M=NULL,k=10,alpha=0.1,gmode=1,K=5,dtype="spath",cmeth="HC")
+drawMapWithSitez(cvr$M, cvr$clusters)
+drawNeighboroodGraph(cvr$M, cvr$NI)
+}
+