From 26779707dd69ac99fa1be6c7ad23275cccb22114 Mon Sep 17 00:00:00 2001 From: Lucy D'Agostino McGowan Date: Thu, 28 Sep 2023 15:48:46 -0400 Subject: [PATCH] update --- DESCRIPTION | 4 +- NEWS.md | 4 ++ R/pald_functions.R | 4 +- README.Rmd | 1 + README.md | 58 +++++++++++++-------------- man/community_clusters.Rd | 2 +- man/figures/README-comm-1.png | Bin 42831 -> 44216 bytes man/figures/README-fig-2-1.png | Bin 52540 -> 52505 bytes man/figures/README-k-mean-1.png | Bin 37677 -> 37751 bytes man/figures/README-lang-1.png | Bin 186896 -> 186138 bytes man/figures/README-pald-1.png | Bin 43399 -> 32216 bytes man/figures/README-pald-2.png | Bin 0 -> 20160 bytes man/figures/README-rand-1.png | Bin 64161 -> 59160 bytes tests/testthat/test-pald_functions.R | 2 +- 14 files changed, 40 insertions(+), 35 deletions(-) create mode 100644 man/figures/README-pald-2.png diff --git a/DESCRIPTION b/DESCRIPTION index e680e4d..fa59481 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: pald Title: Partitioned Local Depth for Community Structure in Data -Version: 0.0.2 +Version: 0.0.3 Authors@R: c(person("Katherine", "Moore", email = "kmoore@amherst.edu", role = c("aut"), comment = c(ORCID = "0000-0001-6943-2416")), @@ -20,7 +20,7 @@ Description: Implementation of the Partitioned Local Depth (PaLD) License: MIT + file LICENSE Encoding: UTF-8 Roxygen: list(markdown = TRUE) -RoxygenNote: 7.1.2 +RoxygenNote: 7.2.3 Imports: igraph, graphics, diff --git a/NEWS.md b/NEWS.md index b956450..67dbeba 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +# pald 0.0.3 + +* Change output in `community_clusters` to be a data frame with two columns: `point` and `community` + # pald 0.0.2 * Allow non-symmetric matrices to be input diff --git a/R/pald_functions.R b/R/pald_functions.R index 8a6b00a..85bf4ab 100644 --- a/R/pald_functions.R +++ b/R/pald_functions.R @@ -462,7 +462,7 @@ plot_community_graphs <- function(c, #' #' @return A data frame with two columns: #' * `point`: The points from cohesion matrix `c` -#' * `cluster`: The (community) cluster labels +#' * `community`: The community cluster labels #' #' @examples #' D <- dist(exdata2) @@ -475,7 +475,7 @@ community_clusters <- function(c) { cl <- igraph::clusters(c_graphs$G_strong)$membership data.frame( point = names(cl), - cluster = cl + community = cl ) } #' Partitioned Local Depth (PaLD) diff --git a/README.Rmd b/README.Rmd index ce41a67..c049ba7 100644 --- a/README.Rmd +++ b/README.Rmd @@ -110,6 +110,7 @@ Each time the function `pald()` is called, the matrix of cohesion values is re-c ## Cohesion Matrix + Cohesion reflects relationship strength from the perspective of relative position, see [@bmm22]. To begin PaLD analysis, we must first compute the matrix of cohesion values from the input distance matrix or `dist` object. Note that cohesion is not symmetric. The values, $C[x, w]$, in the cohesion matrix are interpretable probabilities which capture the strength of the alignment of $w$ to $x$. The sum of the cohesion matrix is always equal to $n/2$ (where $n$ is the number of data points). ```{r cohesion} diff --git a/README.md b/README.md index 9592814..2947b9e 100644 --- a/README.md +++ b/README.md @@ -55,15 +55,15 @@ nor optimization criteria are employed. The only information extracted from the distance matrix are within-triplet dissimilarity comparisons. As a result, outputs are unaffected by monotone transformations of the collection of distances -(e.g., log2). Further, one may transform any measure of -similarity, *s*(*x*,*y*), to a measure of dissimilarity, *d*(*x*,*y*), -via any order-reversing monotone transformation, for instance by taking -*d*(*x*,*y*) = 1/(1+*s*(*x*,*y*)). This provides the user some -flexibility in the choice of dissimilarity (e.g., triangle inequality is -not required) and care should be taken at this stage. +(e.g., $\log_2$). Further, one may transform any measure of similarity, +$s(x, y)$, to a measure of dissimilarity, $d(x,y)$, via any +order-reversing monotone transformation, for instance by taking +$d(x, y) = 1/(1 + s(x, y))$. This provides the user some flexibility in +the choice of dissimilarity (e.g., triangle inequality is not required) +and care should be taken at this stage. The function `dist()` from the `stats` package converts an input data -frame (with *n* rows) into an *n* × *n* distance matrix. In Euclidean +frame (with $n$ rows) into an $n \times n$ distance matrix. In Euclidean examples here, we will use the default Euclidean distance. ## A Small Example @@ -85,6 +85,11 @@ par(mfrow = c(1, 2), pty = "s") D <- dist(exdata1) pald_results <- pald(D, emph_strong = 1, vertex.label.cex = 3) +``` + + + +``` r ### @@ -104,7 +109,7 @@ text(exdata1 + .23, cex = .8) ``` - + The wrapper function `pald()` returns a list containing: the cohesion matrix, local depths, (community) clusters, the threshold for @@ -126,10 +131,10 @@ Cohesion reflects relationship strength from the perspective of relative position, see (Berenhaut, Moore, and Melvin 2022). To begin PaLD analysis, we must first compute the matrix of cohesion values from the input distance matrix or `dist` object. Note that cohesion is not -symmetric. The values, *C*\[*x*,*w*\], in the cohesion matrix are +symmetric. The values, $C[x, w]$, in the cohesion matrix are interpretable probabilities which capture the strength of the alignment -of *w* to *x*. The sum of the cohesion matrix is always equal to *n*/2 -(where *n* is the number of data points). +of $w$ to $x$. The sum of the cohesion matrix is always equal to $n/2$ +(where $n$ is the number of data points). ``` r D <- dist(exdata1) @@ -184,10 +189,9 @@ strong_threshold(C) ``` Pairs of points for which mutual cohesion (i.e., -min {*C**x*, *w*, *C**w*, *x*}) is greater than -the above threshold are considered to be \`\`strongly cohesive.” The -thresholded and symmetrized cohesion matrix can be obtained using the -function ‘cohesion_strong.’ +$\min\{C_{x, w}, C_{w, x}$}) is greater than the above threshold are +considered to be \`\`strongly cohesive.” The thresholded and symmetrized +cohesion matrix can be obtained using the function ‘cohesion_strong.’ ``` r round(cohesion_strong(C), 4) @@ -209,11 +213,10 @@ round(cohesion_strong(C), 4) The overall structure of the data can be observed via the networks obtained from cohesion (referred to here as “community graphs”). The community graph is a symmetric, weighted graph which is obtained from -symmetrizing the cohesion matrix (using -min {*C**x*, *w*, *C**w*, *x*}) and removing -self-loops. The “community cluster graph” is the subgraph consisting of -only the edges for which mutual cohesion greater than the above -threshold. +symmetrizing the cohesion matrix (using $\min\{C_{x, w}, C_{w, x}\}$) +and removing self-loops. The “community cluster graph” is the subgraph +consisting of only the edges for which mutual cohesion greater than the +above threshold. The connected components of the community cluster graph, `G_strong`, are referred to the (community) clusters of the data. Note that no @@ -374,7 +377,7 @@ plot_community_graphs( One could alternatively use the wrapper function: -`pald(cognate_dist, emph_strong = 3, edge_width_factor = 30, vertex.label = lang_lab_subset, vertex.label.cex = .65, vertex.size = 3)`. +$\texttt{pald(cognate_dist, emph_strong = 3, edge_width_factor = 30, vertex.label = lang_lab_subset, vertex.label.cex = .65, vertex.size = 3)}$. It will return a list containing: the cohesion matrix, local depths, (community) clusters, the threshold for identifying strong ties, the thresholded and symmetrized cohesion matrix, the community graph whose @@ -390,7 +393,7 @@ cohesion) and can be found directly from the cohesion matrix. library(igraph) G_strong_lang <- community_graphs(C_lang)$G_strong neighbors(G_strong_lang, "French") -#> + 8/87 vertices, named, from c8a0516: +#> + 8/87 vertices, named, from 8cc26e0: #> [1] Italian Ladin Provencal Walloon #> [5] French_Creole_C French_Creole_D Spanish Catalan @@ -409,7 +412,7 @@ density, see discussion in (Berenhaut, Moore, and Melvin 2022). Note that PaLD was able to detect the eight natural groups within the data without the use of any additional inputs (e.g., number of clusters) nor optimization criteria. Despite providing the “correct” number of -clusters (i.e., *k* = 8) both *k*-means and hierarchical clustering did +clusters (i.e., $k = 8$) both *k*-means and hierarchical clustering did not give the desired result. ``` r @@ -432,11 +435,6 @@ plot_community_graphs( edge_width_factor = 2, vertex.size = 5 ) -``` - - - -``` r ### The cluster vector is provided by `pald' and also may be computed via: library(igraph) cluster_graph <- community_graphs(C3)$G_strong @@ -447,8 +445,10 @@ table(clusters(cluster_graph)$membership) #> 40 40 60 20 20 20 20 20 ``` + + Here are the results for the data obtained from *k*-means and -hierarchical clustering when *k* = 8. +hierarchical clustering when $k = 8$. ``` r par(mfrow = c(1, 2), pty = "s") diff --git a/man/community_clusters.Rd b/man/community_clusters.Rd index 8244442..01b3ff4 100644 --- a/man/community_clusters.Rd +++ b/man/community_clusters.Rd @@ -14,7 +14,7 @@ community_clusters(c) A data frame with two columns: \itemize{ \item 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