R语言Corrplot包

## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set( fig.align = 'center', fig.path = 'webimg/', fig.width = 7, fig.height = 7, out.width = '600px', dev = 'png') get_os = function() { sysinf = Sys.info() if (!is.null(sysinf)) { os = sysinf['sysname'] if (os == 'Darwin') os = 'osx' } else { ## mystery machine os = .Platform$OS.type if (grepl('^darwin', R.version$os)) os = 'osx' if (grepl('linux-gnu', R.version$os)) os = 'linux' } tolower(os) } if(get_os() =='windows' & capabilities('cairo') | all(capabilities(c('cairo', 'X11')))) { knitr::opts_chunk$set(dev.args = list(type='cairo')) } ## ----intro-------------------------------------------------------------------- library(corrplot) M = cor(mtcars) corrplot(M, method = 'number') # colorful number corrplot(M, method = 'color', order = 'alphabet') corrplot(M) # by default, method = 'circle' corrplot(M, order = 'AOE') # after 'AOE' reorder corrplot(M, method = 'shade', order = 'AOE', diag = FALSE) corrplot(M, method = 'square', order = 'FPC', type = 'lower', diag = FALSE) corrplot(M, method = 'ellipse', order = 'AOE', type = 'upper') corrplot.mixed(M, order = 'AOE') corrplot.mixed(M, lower = 'shade', upper = 'pie', order = 'hclust') ## ----hclust------------------------------------------------------------------- corrplot(M, order = 'hclust', addrect = 2) corrplot(M, method = 'square', diag = FALSE, order = 'hclust', addrect = 3, rect.col = 'blue', rect.lwd = 3, tl.pos = 'd') ## ----seriation---------------------------------------------------------------- library(seriation) list_seriation_methods('matrix') list_seriation_methods('dist') data(Zoo) Z = cor(Zoo[, -c(15, 17)]) dist2order = function(corr, method, ...) { d_corr = as.dist(1 - corr) s = seriate(d_corr, method = method, ...) i = get_order(s) return(i) } ## ----seriation-plot----------------------------------------------------------- # Fast Optimal Leaf Ordering for Hierarchical Clustering i = dist2order(Z, 'OLO') corrplot(Z[i, i], cl.pos = 'n') # Quadratic Assignment Problem i = dist2order(Z, 'QAP_2SUM') corrplot(Z[i, i], cl.pos = 'n') # Multidimensional Scaling i = dist2order(Z, 'MDS_nonmetric') corrplot(Z[i, i], cl.pos = 'n') # Simulated annealing i = dist2order(Z, 'ARSA') corrplot(Z[i, i], cl.pos = 'n') # TSP solver i = dist2order(Z, 'TSP') corrplot(Z[i, i], cl.pos = 'n') # Spectral seriation i = dist2order(Z, 'Spectral') corrplot(Z[i, i], cl.pos = 'n') ## ----rectangles--------------------------------------------------------------- library(magrittr) # Rank-two ellipse seriation, use index parameter i = dist2order(Z, 'R2E') corrplot(Z[i, i], cl.pos = 'n') %>% corrRect(c(1, 9, 15)) # use name parameter # Since R 4.1.0, the following one line code works: # corrplot(M, order = 'AOE') |> corrRect(name = c('gear', 'wt', 'carb')) corrplot(Z, order = 'AOE') %>% corrRect(name = c('tail', 'airborne', 'venomous', 'predator')) # use namesMat parameter r = rbind(c('eggs', 'catsize', 'airborne', 'milk'), c('catsize', 'eggs', 'milk', 'airborne')) corrplot(Z, order = 'hclust') %>% corrRect(namesMat = r) ## ----echo=FALSE, fig.width = 8, fig.height = 6, out.width = '700px'---------- ## diverging colors plot.new() par(mar = c(0, 0, 0, 0) + 0.1) plot.window(xlim = c(-0.2, 1.1), ylim = c(0, 1), xaxs = 'i', yaxs = 'i') col = c('RdBu', 'BrBG', 'PiYG', 'PRGn', 'PuOr', 'RdYlBu') for(i in 1:length(col)) { colorlegend(COL2(col[i]), -10:10/10, align = 'l', cex = 0.8, xlim = c(0, 1), ylim = c(i/length(col)-0.1, i/length(col)), vertical = FALSE) text(-0.01, i/length(col)-0.02, col[i], adj = 0.5, pos = 2, cex = 0.8) } ## ----echo=FALSE, fig.width = 8, fig.height = 6, out.width = '700px'---------- ## sequential colors plot.new() par(mar = c(0, 0, 0, 0) + 0.1) plot.window(xlim = c(-0.2, 1.1), ylim = c(0, 1), xaxs = 'i', yaxs = 'i') col = c('Oranges', 'Purples', 'Reds', 'Blues', 'Greens', 'Greys', 'OrRd', 'YlOrRd', 'YlOrBr', 'YlGn') for(i in 1:length(col)) { colorlegend(COL1(col[i]), 0:10, align = 'l', cex = 0.8, xlim = c(0, 1), ylim = c(i/length(col)-0.1, i/length(col)), vertical = FALSE) text(-0.01, i/length(col)-0.02, col[i], adj = 0.5, pos = 2) } ## ----eval=FALSE--------------------------------------------------------------- # COL1(sequential = c("Oranges", "Purples", "Reds", "Blues", "Greens", # "Greys", "OrRd", "YlOrRd", "YlOrBr", "YlGn"), n = 200) # # COL2(diverging = c("RdBu", "BrBG", "PiYG", "PRGn", "PuOr", "RdYlBu"), n = 200) ## ----color-------------------------------------------------------------------- corrplot(M, order = 'AOE', col = COL2('RdBu', 10)) corrplot(M, order = 'AOE', addCoef.col = 'black', tl.pos = 'd', cl.pos = 'n', col = COL2('PiYG')) corrplot(M, method = 'square', order = 'AOE', addCoef.col = 'black', tl.pos = 'd', cl.pos = 'n', col = COL2('BrBG')) ## bottom color legend, diagonal text legend, rotate text label corrplot(M, order = 'AOE', cl.pos = 'b', tl.pos = 'd', col = COL2('PRGn'), diag = FALSE) ## text labels rotated 45 degrees and wider color legend with numbers right aligned corrplot(M, type = 'lower', order = 'hclust', tl.col = 'black', cl.ratio = 0.2, tl.srt = 45, col = COL2('PuOr', 10)) ## remove color legend, text legend and principal diagonal glyph corrplot(M, order = 'AOE', cl.pos = 'n', tl.pos = 'n', col = c('white', 'black'), bg = 'gold2') ## ----non-corr----------------------------------------------------------------- ## matrix in [20, 26], grid.col N1 = matrix(runif(80, 20, 26), 8) corrplot(N1, is.corr = FALSE, col.lim = c(20, 30), method = 'color', tl.pos = 'n', col = COL1('YlGn'), cl.pos = 'b', addgrid.col = 'white', addCoef.col = 'grey50') ## matrix in [-15, 10] N2 = matrix(runif(80, -15, 10), 8) corrplot(N2, is.corr = FALSE, method = 'color', col.lim = c(-15, 10), tl.pos = 'n', col = COL2('PiYG'), cl.pos = 'b', addCoef.col = 'grey50') ## ----col-lim------------------------------------------------------------------ # when is.corr=TRUE, col.lim only affect the color legend display corrplot(M/2) corrplot(M/2, col.lim=c(-0.5, 0.5)) ## ----NA-math------------------------------------------------------------------ M2 = M diag(M2) = NA colnames(M2) = rep(c('$alpha+beta', '$alpha[0]', '$alpha[beta]'), c(4, 4, 3)) rownames(M2) = rep(c('$Sigma[i]^n', '$sigma', '$alpha[0]^100', '$alpha[beta]'), c(2, 4, 2, 3)) corrplot(10*abs(M2), is.corr = FALSE, col.lim = c(0, 10), tl.cex = 1.5) ## ----test--------------------------------------------------------------------- testRes = cor.mtest(mtcars, conf.level = 0.95) ## specialized the insignificant value according to the significant level corrplot(M, p.mat = testRes$p, sig.level = 0.10, order = 'hclust', addrect = 2) ## leave blank on non-significant coefficient ## add significant correlation coefficients corrplot(M, p.mat = testRes$p, method = 'circle', type = 'lower', insig='blank', addCoef.col ='black', number.cex = 0.8, order = 'AOE', diag=FALSE) ## ----special------------------------------------------------------------------ ## leave blank on non-significant coefficient ## add all correlation coefficients corrplot(M, p.mat = testRes$p, method = 'circle', type = 'lower', insig='blank', order = 'AOE', diag = FALSE)$corrPos -> p1 text(p1$x, p1$y, round(p1$corr, 2)) ## ----p-values----------------------------------------------------------------- ## add p-values on no significant coefficients corrplot(M, p.mat = testRes$p, insig = 'p-value') ## add all p-values corrplot(M, p.mat = testRes$p, insig = 'p-value', sig.level = -1) ## add significant level stars corrplot(M, p.mat = testRes$p, method = 'color', diag = FALSE, type = 'upper', sig.level = c(0.001, 0.01, 0.05), pch.cex = 0.9, insig = 'label_sig'