I got some figures after I did decision tree model using part library.
This figures shows fundamental function of part library.
In these figures, I understand all excepts fourth kind of figure.
This figure don't have any powful feature. It does not show any information. How can I understand this figure?
You are right. The fourth panel shows the visualization of a tree without any splits. Thus, in this case none of the available split variables improved the cost-complexity criterion of rpart and hence only the root node remains.
The visualization using the partykit package employs stacked bar plots for every terminal node in the trees (the default visualization for binary classification in the package). Thus, there is only a single stacked bar in case of a root node only.
Related
before k-means clustering for consumer segmentation, I want to identify and delete outliers of my sample. I tried hierarchical clustering with single linkage algorithm. The problem is, I have a sample with more than 800 cases, and in my plot (single linkage dendrogram) the numbers are written across each other and therefore not readable, so it is impossible for me to clearly identify the outliers by just looking at the graph :-/
Here they say, you can create boxplots based on the branch distance to identify outliers in a more objective way. I thought that would be also a great way to just make the row numbers of the outliers in my dataset readable, however I am struggling with creating the boxplots..
https://link.springer.com/article/10.1186/s12859-017-1645-5/figures/3
Does anyone know, how to write the code to get the boxplots based on the height of the branches?
This is the code I use for clustering and attached you can see the plot
dr_dist<-dist(dr_ma_cluster[,c(148:154)])
hc_dr<-hclust(dr_dist,method = "single") #single linkage
plot(hc_dr,labels=(row.names(dr_ma_cluster)))
This is my failed trial to do the boxplot, as I don't know how to address the branch height
> boxplot(hc_dr)
Error in x[floor(d)] + x[ceiling(d)] :
non-numeric argument for binary operator
> boxplot(hc_dr[,c(148:154)])
Error in hc_dr[, c(148:154)] : Incorrect number of dimensions
And here another way to do the graph (and some automated outlier detection approach), but it makes the readability even worse with large datasets..
Another code to plot the tree, even less readable for large datasets:
Delete outliers automatically of a calculated agglomerative hierarchical clustering data
Thanks for any help!!
boxplot(hc_dr$height) as suggested by StupidWolf was the simple thing I was looking for.
Unfortunately I did not manage to label the outlier dots with the rownames from the original dataframe. Rownames from the branch height table were useless as they were assigned in ascending order.
hang = 0.0001 gave a better look to the dendrogram, but labels were still unreadable as still over eachother.
If anyone has a similar problem check R Shiny, zoomable dendrogram program
the code given there in the answer was super easy to adapt, resulting in a zoomable dendrogram, which makes it easy to identify the relevant cases (->outliers). for details search dendextendas proposed by csgroen.
Both together, the boxplot and this nice tool served to identify the rownames of the outliers after single linkage clustering in order to delete them before km means clustering
I am trying to find a way to plot a disease transmission tree that allows me to:
plot the tree over a timeline (a timeline spanning 2 months)
specify the shape and colour of the nodes in the tree (so that you can easily identify which nodes belong to the same household for example)
format the link between the nodes (dashed lines, two way arrows, solid lines...etc.)
plot "stray branches" that aren't linked to the root/parent node.
The dataset I am working with is relatively small (22 nodes) so I don't mind working with a package that is a bit fiddly!
I have thought about using phylogeny trees, but I'm uncertain whether they will allow me to plot stray nodes. Which package would be most suitable for this task?
Thanks!
Try DiagrammeR. I don't have much experience, but it did what I needed to do and I know I barely scratched the surface of what it can do.
I am starting a new project in python (to be used through jupyter-notebooks), where I'll need to visualise some hierarchically clustered graphs.
I have looked for existing packages, but so far I am not convinced by what I have seen.
I am not interested in the clustering process in itself, because this will be another part of the project and I know (roughly) how the graphs will be built up progressivelly.
What I am looking for are:
an appropriate data structure for storing hierarchically clustered graphs,
visualisation tools that would allow to represent the graph on a map (based on X and Y coordinates of the nodes) and either represent the subparts of the clusters, or simplify the clusters depending on their type or depth in the graph structure,
ideally, bring some interactivity, for example the ability to zoom-in or-out, or click on some clustered nodes to expand the nodes that were hidden in the cluster.
It looks pretty specific and despite some cool packages I have seen I am not sure which one would help without having too much to reimplement. So far, NetworkX looks like a cool starting point, especially with some D3.js (as shown here), but it is still far from what I have in mind.
Any advice about where to start digging?
Thanks a lot.
Gautier
For Python, Seaborn's clustermaps are nice. Seaborn is mainly meant to be used with Pandas dataframes; however, the documentation for clustermap says it can be rectangular data, and so I think it means other arrays will wor.
See also:
Dendrogram with heat map
SciPy Hierarchical Clustering and Dendrogram Tutorial
Hierarchical Clustering in Python
I am visualizing three different decision trees
Normal tree
Tree with balanced data (using SMOTE)
Tree with weighted data
For all trees I am using the fancyrpartplot() and I have no error when plotting.
When I plot my first tree, everything is fine. Then I run my code for the SMOTE function and I plot my second desicion tree with the balanced data. This second tree is plotted only in 2/3 of the total plotting area...
When plotting my normal tree again, the normal tree is plotted underneath the SMOTE tree....
Here is how it looks like:
I do not know why this happens and would appreciate some help with this!
I'm working with the ciplot graphing module for Stata and am encountering a problem with the alignment of bars when I use the by() option. Here's a trivial example demonstrating the issue:
webuse citytemp, clear
ciplot heatdd cooldd, by(region) horizontal recast(conn)
So, the graph shows means and confidence intervals for two variables across categories of the region variable. The bars for the different variables do not align horizontally, though. For each region, the point and bar for heatdd is one line above, and the point and bar for cooldd is one line below, the category label. I would like these to be on the same line, but I can't figure out how to achieve it.
I'm open to solutions that do not involve ciplot, but I have found it to be useful for the specific task I'm working on.
This is my program (in Stata terms, downloadable via ssc install ciplot) so I can speak confidently. (On Statalist, it's expected that you explain the exact provenance of user-written programs; that would be good practice here too.)
It's not a bug; it's a feature (supposedly).
The offsets are entirely deliberate, to avoid messes when two or more intervals would just overlap and occlude each other, which is entirely likely when groups or comparable variables have similar values, which in turn is common when you do this. Even in your example, intervals for heating and cooling degree-days for the South would overlap otherwise, so the graph makes the point for me.
I can see that it's not what you want, but
There is no option in ciplot to remove the offset. I can see a case for one, but
My advice is now to use statsby to get a reduced dataset containing the confidence interval information, and then the graphics are typically a couple of command lines and you get to choose what you want. This approach is documented in a paper easily accessible from the Stata Journal.
You are always welcome to clone the program and modify the code using a different program name, with notional mention of the original.