3 <- PrettyBarPlot2(signed.ctrJ, threshold = 1 / NROW(signed.ctrJ), font.size = 3, color4bar =, signifOnly = FALSE, horizontal = TRUE, main = 'Variable Contributions (Signed)', ylab = paste0( 'Contributions Barplot', laDim), ylim = c( 1.2 * min(signed.ctrJ), 1.2 * max(signed.ctrJ)) ) + ggtitle( "", subtitle = paste0( 'Component ', laDim)) gridExtra :: grid.arrange( as.grob(ctrJ. 2 <- PrettyBarPlot2(signed.ctrJ, threshold = 1 / NROW(signed.ctrJ), font.size = 3, color4bar =, signifOnly = FALSE, horizontal = TRUE, main = 'Variable Contributions (Signed)', ylab = paste0( 'Contributions Barplot', laDim), ylim = c( 1.2 * min(signed.ctrJ), 1.2 * max(signed.ctrJ)) ) + ggtitle( "", subtitle = paste0( 'Component ', laDim)) laDim = 3 ctrJ. 1 <- PrettyBarPlot2(signed.ctrJ, threshold = 1 / NROW(signed.ctrJ), font.size = 3, signifOnly = FALSE, horizontal = TRUE, color4bar =, main = 'Variable Contributions (Signed)', ylab = paste0( 'Contributions Barplot',laDim), ylim = c( 1.2 * min(signed.ctrJ), 1.2 * max(signed.ctrJ)) ) + ggtitle( "Contribution", subtitle = paste0( 'Component ', laDim)) # plot contributions for component 2 laDim = 2 ctrJ. Signed.ctrJ <- cj * sign(F) laDim = 1 ctrJ. Vocabulary # 5.5.2 CA-like Barycentric (same Inertia as products) F4Voc <- DistatisR :: projectVoc(, F) set.seed( 44) Voc.clusters <- kmeans(F4Voc $Fvoca.bary, 3) Voc.color <- Voc.clusters $cluster Voc.color <- prettyGraphsColorSelection( lor = sample( 1 : 170, 1)) Voc.color <- prettyGraphsColorSelection( lor = sample( 1 : 170, 1)) Voc.color <- prettyGraphsColorSelection( lor = sample( 1 : 170, 1)) gg.voc.bary <- createFactorMap(F4Voc $Fvoca.bary, title = 'Vocabulary', col.points = Voc.color, col.labels = Voc.color, display.points = FALSE, constraints = gg. $constraints) # gg. <- gg.voc.bary $zeMap + label4S #print(e1.gg.) gg. <- gg. $zeMap_background + gg. $zeMap_dots + #gg.$zeMap_text + gg.voc.bary $zeMap_text + label4S gg. # table.wines,, # have the design sign <- as.matrix(table.wines $X) rownames(sign) <- table.wines $X for(i in 1 : nrow(sign)) rownames(table.wines) <- table.wines $X <- table.wines rownames() <- $X <- colnames() <- <- sign <- index <- index # creating distance cube DistanceCube <- DistanceFromSort() # run distance cube res.Distatis <- distatis(DistanceCube) # get the factors from the Cmat analysis G <- res.Distatis $res4Cmat $G C <- res.Distatis $res4Cmat $C eigs <- res.Distatis $res4Cmat $eigValues tau <- res.Distatis $res4Cmat $tau cj <- res.Distatis $res4Splus $ctr promise <- res.Distatis $res4Splus $eigValues # get partial factor array BootF <- BootFactorScores(res.Distatis $res4Splus $PartialF) # Bootstrap On Factor Scores. 8.8 Contribution and Bootstrap Ratio Barplots. 7.8 Contribution and Bootstrap Ratio Barplot.6.7 Contribution and Bootstrap Ratio Barplots import datathief as dt filename 'dufig1aannotated.png' xlim -10, 20 ylim 0, 15 data dt.datathief(filename, xlimxlim, ylimylim) On this input (NB, you might need to zoom in to see the individual pixels): Extracts the data for this plot: See the examples folder for more information.5.7 Contribution and Bootstrap Ratio Barplot.4.7 Contriution and Bootstrap Ratio Barplots.3.1 Main data set: Collective Action Data Set.
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