-#draw (France or...) map with all sites of colors 'cols'
+#' Draw (France or...) map with all sites of colors 'cols'
+#'
+#' @param M Coordinates matrix (in columns)
+#' @param cols Vector of colors for each row of M [default: all black]
+#' @export
+#'
drawMapWithSites = function(M, cols=rep(1,nrow(M)))
{
xMin = range(M[,1])[1]
}
}
-#draw neighborhoods graph on top of a country map (or any other map)
+#' Draw neighborhoods graph on top of a country map (or any other map)
+#'
+#' @param M Coordinates matrix (in columns)
+#' @param NI Neighborhoods of M rows (list of integer vectors)
+#' @export
+#'
drawNeighborhoodGraph = function(M, NI)
{
for (i in 1:length(NI))
}
}
-#plot a matrix of curves (in rows)
+#' Plot a matrix of curves (in rows)
+#'
+#' @param M Coordinates matrix (in columns)
+#' @param cols Vector of colors for each row of M [default: all black]
+#' @export
+#'
plotCurves = function(M, cols=rep(1,nrow(M)))
{
n = nrow(M)
-#example of "not too bad" parameters
-#~ k=10
-#~ alpha=0.1
-#~ gmode=1
-#~ K = 5
-#~ dtype = "spath"
-#~ cmeth = "HC"
-#~ pcoef=??
-#~ h=??
-#~ eps=??
-#~ maxit=??
-
-#MAIN FUNCTION : direct clustering from a neighborhoods graph, or get regions
-#from (Poisson) distribution parameters optimization, using convex relaxation.
-findSyncVarRegions = function(
- method, #global method: "direct" or "convex"
- 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)
- k, #number of neighbors
- 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
- gmode, #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!)
- K, #number of clusters
- dtype, #distance type, in {"simple","spath","ectd"}.
- #NOTE: better avoid "simple" if gmode>=2
- cmeth, #clustering method, in {"KM","HC","spec"} for k-means (distances based)
- #or hierarchical clustering, or spectral clustering (only if gmode>=2)
- pcoef=1.0, #penalty value for convex optimization
- h=1e-3, #step in the min LL algorithm
- eps=1e-3, #threshold to stop min.LL iterations
- maxit=1e3, #maximum number of iterations in the min LL algo
- showLL=TRUE, #print trace of log-likelihood evolution
- disp=TRUE #true for interactive display (otherwise nothing gets plotted)
-) {
+#' Direct clustering from a neighborhoods graph, or get regions from (Poisson)
+#' distribution parameters optimization, using convex relaxation.
+#'
+#' @param method Global method: "direct" or "convex"
+#' @param 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)
+#' @param k Number of neighbors
+#' @param 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.
+#' @param 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!)
+#' @param K Number of clusters
+#' @param dtype Distance type, in {"simple","spath","ectd"}.
+#' NOTE: better avoid "simple" if gmode>=2
+#' @param cmeth Clustering method, in {"KM","HC","spec"} for k-means (distances based)
+#' or hierarchical clustering, or spectral clustering (only if gmode>=2)
+#' @param pcoef Penalty value for convex optimization [default: 1.0]
+#' @param h Step in the min LL algorithm [default: 1e-3]
+#' @param eps Threshold to stop min.LL iterations [default: 1e-3]
+#' @param maxit Maximum number of iterations in the min LL algo [default: 1e3]
+#' @param showLL Print trace of log-likelihood evolution [default: true]
+#' @param disp True [default] for interactive display (otherwise nothing gets plotted)
+#' @return 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)
+#' @export
+#' @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)
+#'
+findSyncVarRegions = function(method, M, k, alpha, gmode, K, dtype, cmeth,
+ pcoef=1.0, h=1e-3, eps=1e-3, maxit=1e3, showLL=TRUE, disp=TRUE)
+{
#get matrix M if not directly provided
if (is.null(M))
{
projects / synclust.git / commitdiff