moin: Modelling Interactions

#' Cultural distance matrix
#' 
#' Creates a cultural distance matrix by aggregating all features to the nodes by means of a Voronoi tesselation. xxxxxxx. The n dimensional distance matrix of the nodes and their assigned features will be calculated with euclidean distances. The bidirectional matrix will be returned and can be used as network weight.
#' 
#' @title cul_dist
#' 
#' @param nodes_x a vector containing metric x coordinates of nodes
#' @param nodes_y a vector containing metric y coordinates of nodes
#' @param nodes_id a vector containing ID for nodes
#' @param features a data.frame containing metric x and y coordinates of features, and feature type. Coordinates are expected to be the first two columns.
#' @param type_col a character string naming the columname containing feature types.
#' @param pre_size numeric, amount of letters, e.g. characters before typenumbers
#' @param method character string, the distance measure to be 
#'   used ("euclidean", "maximum", "manhattan", "canberra", 
#'   "binary" or "minkowski")
#' 
#' 
#' @return bidirectional cultural distance matrix 
#' 
#' @author Franziska Faupel <\email{franziska-faupel@@gmx.de}>
#'
#' @examples
#' 
#' set.seed(1234)
#' 
#' cul_dist(nodes_x=sample(3433806:3581396, 10, replace = TRUE),
#'                nodes_y=sample(5286004:5484972, 10, replace = TRUE), 
#'                nodes_id=c(1:10), 
#'              features=data.frame(x=sample(3433806:3581396, 100, replace = TRUE),
#'                    y=sample(5286004:5484972, 100, replace = TRUE),
#'                    type=paste0("B", c(rep(1, 5), rep(2,15), sample(11:19, 20, replace = TRUE), 
#'                    sample(111:119, 30, replace = TRUE), sample(1111:1115, 30, replace = TRUE)))
#'                    ), 
#'              type_col="type" , pre_size=1, method = "euclidean")
#'            
#'
#' @export

cul_dist <- function(nodes_x, nodes_y, nodes_id, features,type_col, pre_size=1, method){
    nodes <- data.frame(nodes_x, nodes_y, nodes_id)
    aggr_fea <- aggr_fea_voro(nodes,features, type_col)
    typelist <- create_type_generator(features, type_col, pre_size)
    type_spectra <- create_typespectra(aggr_fea, typelist)
    cul_dist <- dist_matr(type_spectra,method)
    return(cul_dist)
}