Package RCA. R topics documented: February 29, 2016
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1 Type Package Title Relational Class Analysis Version 2.0 Date Author Amir Goldberg, Sarah K. Stein Package RCA February 29, 2016 Maintainer Amir Goldberg Depends igraph, gplots Description Relational Class Analysis (RCA) is a method for detecting heterogeneity in attitudinal data (as described in Goldberg A., 2011, Am. J. Soc, 116(5)). License GPL (>= 2) NeedsCompilation no Repository CRAN Date/Publication :24:27 R topics documented: RCA-package Index 5 RCA-package Relational Class Analysis Description Relational Class Analysis (RCA) is a method for detecting heterogeneity in attitudinal data (as described in Goldberg A., 2011, Am. J. Soc, 116(5)). The RCA function produces an object of class RCA. This object includes: (1) a vector of class membership $membership; (2) a list of modules $modules; and (3) the relationality matrix $R. 1
2 2 RCA-package Usage RCA(matrix, max = NULL, min = NULL, num = 1000, alpha = 0.05) ## S3 method for class RCA print(x,...) ## S3 method for class RCA plot(x, module = NULL, colorblind = FALSE, heatmap = TRUE, heat_labels = FALSE, drop_neg_ties = TRUE, layout = layout.kamada.kawai, edge_color = "gray", vertex_color = "white", vertex_frame_color = "black", vertex_size = 20, vertex_label_color = "black", vertex_label_cex = 0.8, margin = 0,...) Arguments matrix max min num alpha x module colorblind heatmap heat_labels drop_neg_ties layout edge_color a matrix of size m x n. Rows correspond to observations and columns correspond to variables. either a single integer or a vector of length n. If an integer is specified, its value is used as the maximum for all variables in the matrix. If a vector is specified, then each element is used as the maximum value for each variable. The default value is NULL, in which case max is assumed to be the maximum of each variable in the input matrix. either a single integer or a vector of length n. If an integer is specified, its value is used as the minimum for all variables in the matrix. If a vector is specified, then each element is used as the minimum value for each variable. The default value is NULL, in which case min is assumed to be the minimum of each variable in the input matrix. the number of bootstrap samples to be used in testing for significant relationalities. The default is a value between 0 and 1 specifying the significance level to be used in testing for significant relationalities. The default is an object of class RCA. the module number to graph. change the graph color schemes to be colorblind friendly. Default is FALSE. choose which type of graph to produce. Default is TRUE for heatmap. FALSE produces an igraph. add correlation labels to the heatmap. Default is FALSE. drop negative graph edges. Default is TRUE. choose a layout for plotting graphs. Default is layout.kamada.kawai. Additional layouts can be chosen from layout{igraph}. color of the graph edges. Default is gray.
3 RCA-package 3 Details vertex_color color of the vertices. Default is white. vertex_frame_color color of the vertex frames. Default is black. vertex_size vertex frame size. Default is 20. vertex_label_color color of the vertex labels. Default is black. vertex_label_cex size of the vertex labels. Default is 0.8. margin adjust the margins of the graph. Default is 0. Negative values reduce the margins.... additional arguments RCA computes the relationality between all pairs of observations and partitions the sample into subgroups of schematically overlapping respondents such that those who subscribe to the same logic are clustered together. It accomplishes this by performing the following sequence: 1.) RCA computes a measure of relationality for each pair of observations in the data set, resulting in a complete, undirected, and weighted graph. 2.) Graph edges whose weights are statistically insignificant are removed. Remaining edges are transformed by their absolute value. 3.) RCA partitions the graph into subgroups of schematically similar observations using a graphpartitioning algorithm. Author(s) Amir Goldberg, Sarah K. Stein Maintainer: Amir Goldberg <amirgo@stanford.edu> References Goldberg, Amir. "Mapping shared understandings using relational class analysis: the case of the cultural omnivore reexamined." American Journal of Sociology (2011): Examples # Test matrix set.seed <- 2 matrix <- round(matrix(runif(100, min = 0, max = 6), ncol = 5)) rca <- RCA(matrix) rca <- RCA(matrix, max = rep(6, 5), min = rep(0, 5)) rca <- RCA(matrix, max = 6, min = 0) rca <- RCA(matrix, num = 10000, alpha = 0.1) summary(rca) print(rca)
4 4 RCA-package rca$membership rca$modules[[1]]$matrix rca$r # Heatmap plot(rca, module = 1) plot(rca, module = 1, colorblind = TRUE, heat_labels = TRUE) # IGraph plot(rca, module = 1, heatmap = FALSE) plot(rca, module = 1, heatmap = FALSE, layout = layout.circle, edge_color = "red", vertex_color = "gray", vertex_frame_color = "red", vertex_size = 30, vertex_label_color= "red", vertex_label_cex = 1, margin = 0.2)
5 Index Topic package RCA-package, 1 plot.rca (RCA-package), 1 print.rca (RCA-package), 1 RCA (RCA-package), 1 RCA-package, 1 5
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