tag:blogger.com,1999:blog-86833917033524645832018-09-17T12:10:22.917+08:00Taiyun WeiAbout my work and thoughts.Cloud Weinoreply@blogger.comBlogger7125tag:blogger.com,1999:blog-8683391703352464583.post-69449881570546162462011-12-08T19:17:00.004+08:002011-12-08T21:44:22.127+08:00Some example graphs in corrplot 0.60
corrplot 0.60 is on CRAN now, here are some example graphs:
Cloud Weihttps://plus.google.com/108984902637450086329noreply@blogger.com2tag:blogger.com,1999:blog-8683391703352464583.post-57975499869378542002009-04-22T17:33:00.007+08:002010-05-11T02:19:40.864+08:00Visulization of correlation matrix 2The plot.corr() function was updated, now it can1. Add colorkey and text labels more flexible.2. Reorder the variables using PCA or hierarchical clustering methods.3. Excellent in details.4. Other.What's more, I found a new method to display correlation matricex, using squares with different areas and colors .See also here . Get corrplot package here.Cloud Weihttps://plus.google.com/108984902637450086329noreply@blogger.com2tag:blogger.com,1999:blog-8683391703352464583.post-75742947505368019522009-03-24T20:16:00.004+08:002010-05-11T02:19:13.189+08:00Comparison of different circle graphsSee in my Picasa here and get corrplot package here. Thanks Bob O'Hara's advice:)I found people's tastes differ, so input parameter col (fill color) and bg (background color) was added in new edition. What is more, now you can order your variables using PCA (order=TRUE) to get a better impression.Cloud Weihttps://plus.google.com/108984902637450086329noreply@blogger.com1tag:blogger.com,1999:blog-8683391703352464583.post-47283505622044481592009-03-22T18:40:00.000+08:002009-03-22T18:47:23.637+08:00Play Sliding Puzzles on RThe code was shared on my google docs. See it here. Cloud Weihttps://plus.google.com/108984902637450086329noreply@blogger.com2tag:blogger.com,1999:blog-8683391703352464583.post-20641215367710133832009-03-13T11:22:00.001+08:002009-04-07T12:36:50.999+08:00Visulization of correlation matrixColor Imagedata(mtcars)fit = lm(mpg ~ ., mtcars)cor = summary(fit, correlation = TRUE)$correlationcor2 = t(cor[11:1, ])colors = c("#A50F15", "#DE2D26", "#FB6A4A", "#FCAE91", "#FEE5D9","white", "#EFF3FF", "#BDD7E7", "#6BAED6", "#3182BD", "#08519C")image(1:11, 1:11, cor2, axes = FALSE, ann = F, col = colors)text(rep(1:11, 11), rep(1:11, each = 11), round(100 * cor2))Ellipseslibrary(ellipse)col = Cloud Weihttps://plus.google.com/108984902637450086329noreply@blogger.com17tag:blogger.com,1999:blog-8683391703352464583.post-54484117734386044852009-03-11T19:53:00.000+08:002009-03-22T12:43:02.465+08:00Andrews' Curve And Parallel Coordinate GraphUnison graph and parallel coordinate graph share similar thought in visualising the difference of multidimensional data, thought the former is much more complicated. Based on iris data, we can see their performance.Parallel coordinate graphAndrews' CurveWe can see that unison graph seems more vivid and powerful.#----------------------------------------------------------------------#code of unisonCloud Weihttps://plus.google.com/108984902637450086329noreply@blogger.com4tag:blogger.com,1999:blog-8683391703352464583.post-4536455260088791432009-03-11T19:05:00.000+08:002009-03-11T19:51:28.286+08:00ScatterplotsThere are many types of scatterplots in R, here are some examples based on the famous Iris data.pairs() and coplot() in package graphics.gpairs() in package YaleToolkit.scatterplot.matrix() or spm() in package car.splom() in package lattice.Cloud Weihttps://plus.google.com/108984902637450086329noreply@blogger.com3