From c3fc76e66f15c806458f3af8dc09cc777e35f171 Mon Sep 17 00:00:00 2001 From: pajaskowiak Date: Mon, 14 Nov 2022 16:26:45 +0100 Subject: [PATCH] version 0.0.1 --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 33cbf4d..f14c851 100644 --- a/README.md +++ b/README.md @@ -145,7 +145,7 @@ print((((dPlot[[1]] | dPlot[[2]] | dPlot[[3]])/ #### Example 4 -Maybe you have a list of partitions (even with more than one partition per value of k). No worries. You can evaluate all of them and even get a nice plot on the go. In the middle plot we depict all evaluations from AUCC (note that now we have more than one partition per k). We then fit a line [loess](https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/loess) just to have an idea on the trend of the values. The res object has both plost from the right and all AUCC values within it. +Maybe you have a list of partitions (even with more than one partition per value of k). No worries. You can evaluate all of them and even get a nice plot on the go. In the middle plot we depict all evaluations from AUCC (note that now we have more than one partition per k). We then fit a curve ([loess](https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/loess)) just to have an idea on the trend of the values. The res object has both plots from the right and all AUCC values within it. ```{r} library(clusterConfusion)