# Grow Families Call to the C++ function that grows the families. ```r grow(n, f, d, graph, scores, feat_mat, score_mat, tol, weight_mat, selected) ``` ## Arguments - `n`: Number of features to rank. - `f`: Number of families to grow. - `d`: Damping factor - `graph`: A matrix or data frame representation of a graph object. - `scores`: A numeric vector of empirical feature scores. - `feat_mat`: Matrix to store selected features. - `score_mat`: Matrix to store weighted scores of selected features. - `tol`: Tolerance - `weight_mat`: A matrix to store the cumulative weighted scores of selected futures across all families. - `selected`: Vector indicating whether a feature has been selected yet. ## Details This is the workhorse function for the Family Rank algorithm. ## Returns Returns a matrix with `1+2xn.families` columns and `n.rank` rows. The first column is the cumulative feature score for each of the ranked features `1:n.rank`. The row number corresponds to the re-indexed feature index. The next `n.families` columns contain the indices of selected features for each iteration of feature selection. The last `n.families` columns contain the weigthed scores of selected features for each iteration. ## References ADD REFERENCE ## Author(s) Michelle Saul ## Examples ```r # Toy Example scores <- c(.6, .2, .9) graph <- cbind(c(1,1), c(2,3), c(.4, .8)) # initialize matrices n <- n.families <- length(scores) feat.mat <- score.mat <- matrix(0, nrow = n, ncol = n.families) feat.mat[1,] <- order(scores, decreasing = TRUE) score.mat[1,] <- sort(scores, decreasing = TRUE) # Grow families mats <- grow(n = n, f = n.families, d = 0.5, graph = as.matrix(graph), scores = scores, feat_mat = feat.mat, score_mat = score.mat, tol = 0, weight_mat = as.matrix(scores), selected = rep(1, n)) # Selected Feature Matrix ## columns represent familes ## rows represent iterations ## values indicate indices of selected features feat.mat <- mats[, 2:(n.families+1)] feat.mat # Corresponding Score Matrix ## columns represent familes ## rows represent iterations ## values indicate max weighted score of selected features score.mat <- mats[, (n.families+2):(1+2*n.families)] score.mat ```

grow function