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  • Kruskal-Wallis test with details on pairwise comparisons

    - by dalloliogm
    The standard stats::kruskal.test module allows to calculate the kruskal-wallis test on a dataset: >>> data(diamonds) >>> kruskal.test.test(price~carat, data=diamonds) Kruskal-Wallis rank sum test data: price by carat by color Kruskal-Wallis chi-squared = 50570.15, df = 272, p-value < 2.2e-16 this is correct, it is giving me a probability that all the groups in the data have the same mean. However, I would like to have the details for each pair comparison, like if diamonds of colors D and E have the same mean price, as some other softwares do (SPSS) when you ask for a Kruskal test. I have found kruskalmc from the package pgirmess which allows me to do what I want to do: > kruskalmc(diamonds$price, diamonds$color) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference D-E 571.7459 747.4962 FALSE D-F 2237.4309 751.5684 TRUE D-G 2643.1778 726.9854 TRUE D-H 4539.4392 774.4809 TRUE D-I 6002.6286 862.0150 TRUE D-J 8077.2871 1061.7451 TRUE E-F 2809.1767 680.4144 TRUE E-G 3214.9237 653.1587 TRUE E-H 5111.1851 705.6410 TRUE E-I 6574.3744 800.7362 TRUE E-J 8649.0330 1012.6260 TRUE F-G 405.7470 657.8152 FALSE F-H 2302.0083 709.9533 TRUE F-I 3765.1977 804.5390 TRUE F-J 5839.8562 1015.6357 TRUE G-H 1896.2614 683.8760 TRUE G-I 3359.4507 781.6237 TRUE G-J 5434.1093 997.5813 TRUE H-I 1463.1894 825.9834 TRUE H-J 3537.8479 1032.7058 TRUE I-J 2074.6585 1099.8776 TRUE However, this package only allows for one categoric variable (e.g. I can't study the prices clustered by color and by carat, as I can do with kruskal.test), and I don't know anything about the pgirmess package, whether it is maintained or not, or if it is tested. Can you recommend me a package to execute the Kruskal-Wallis test which returns details for every comparison? How would you handle the problem?

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  • a package for kruskal-wallis that shows pairwise comparison details

    - by dalloliogm
    The standard stats::kruskal.test module allows to calculate the kruskal-wallis test on a dataset: >>> data(diamonds) >>> kruskal.test.test(price~carat, data=diamonds) Kruskal-Wallis rank sum test data: price by carat by color Kruskal-Wallis chi-squared = 50570.15, df = 272, p-value < 2.2e-16 this is fine, it is giving me the probability that all the groups in the data have the same mean. However, I would like to have the details per each pair comparison, like if diamonds of colors D and E have the same mean price, as some other softwares (SPSS) do when you ask for a Kruskal test. I have found kruskalmc from the package pgirmess which allows me to do what I want to do: > kruskalmc(diamonds$price, diamonds$color) Multiple comparison test after Kruskal-Wallis p.value: 0.05 Comparisons obs.dif critical.dif difference D-E 571.7459 747.4962 FALSE D-F 2237.4309 751.5684 TRUE D-G 2643.1778 726.9854 TRUE D-H 4539.4392 774.4809 TRUE D-I 6002.6286 862.0150 TRUE D-J 8077.2871 1061.7451 TRUE E-F 2809.1767 680.4144 TRUE E-G 3214.9237 653.1587 TRUE E-H 5111.1851 705.6410 TRUE E-I 6574.3744 800.7362 TRUE E-J 8649.0330 1012.6260 TRUE F-G 405.7470 657.8152 FALSE F-H 2302.0083 709.9533 TRUE F-I 3765.1977 804.5390 TRUE F-J 5839.8562 1015.6357 TRUE G-H 1896.2614 683.8760 TRUE G-I 3359.4507 781.6237 TRUE G-J 5434.1093 997.5813 TRUE H-I 1463.1894 825.9834 TRUE H-J 3537.8479 1032.7058 TRUE I-J 2074.6585 1099.8776 TRUE However, this package only allows for one categoric variable (e.g. I can't study the prices clustered by color and by carat, as I can do with kruskal.test), and I don't know anything about the pgirmess package, whether it is maintained or not, or if it is tested.

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  • Weakly connected tree

    - by wow_22
    hello I have an algorithmic problem using a weakly connected tree T where w(T)=sum(w(e)) for each edge e,by w i declare weight and i have to prove that we can use prim and Kruskal algorithm while w(T)=max{w(e)} maximum between any edge e belongs at T (I proved that) but i have also to prove the same for w(T)=?(w(e)) while ? states product of all edges belongs at T i tried a lot to prove it but i did not came up with a result that proving or disapproving the use of prim ,kruskal any help will be more than appreciated thanks

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  • Calling gwt static method from parent of iframe

    - by Richard Wallis
    I'd like to know how to call a GWT static method from the parent of the iframe in which the gwt module is loaded. As a simple example suppose I have the following gwt class: public class Simple { public static void showWindow() { Window.alert("Hello from the iframe"); } } I create an html host page called "iFrameHost.html" that can run the function above. Then in an unrelated GWT module on a different page I call: Frame iFrame = new Frame("iFrameHost.html"); RootPanel.get().add(iFrame); How do I now call the showWindow() method from the parent page?

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  • Clustering [assessment] algorithm with distance matrix as an input

    - by Max
    Can anyone suggest some clustering algorithm which can work with distance matrix as an input? Or the algorithm which can assess the "goodness" of the clustering also based on the distance matrix? At this moment I'm using a modification of Kruskal's algorithm (http://en.wikipedia.org/wiki/Kruskal%27s_algorithm) to split data into two clusters. It has a problem though. When the data has no distinct clusters the algorithm will still create two clusters with one cluster containing one element and the other containing all the rest. In this case I would rather have one cluster containing all the elements and another one which is empty. Are there any algorithms which are capable of doing this type of clustering? Are there any algorithms which can estimate how well the clustering was done or even better how many clusters are there in the data? The algorithms should work only with distance(similarity) matrices as an input.

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  • What are algorithmic paradigms?

    - by Vaibhav Agarwal
    We generally talk about paradigms of programming as functional, procedural, object oriented, imperative etc but what should I reply when I am asked the paradigms of algorithms? For example are Travelling Salesman Problem, Dijkstra Shortest Path Algorithm, Euclid GCD Algorithm, Binary search, Kruskal's Minimum Spanning Tree, Tower of Hanoi paradigms of algorithms? Should I answer the data structures I would use to design these algorithms?

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  • Good Java graph algorithm library?

    - by Nick Fortescue
    Has anyone had good experiences with any Java libraries for Graph algorithms. I've tried JGraph and found it ok, and there are a lot of different ones in google. Are there any that people are actually using successfully in production code or would recommend? To clarify, I'm not looking for a library that produces graphs/charts, I'm looking for one that helps with Graph algorithms, eg minimum spanning tree, Kruskal's algorithm Nodes, Edges, etc. Ideally one with some good algorithms/data structures in a nice Java OO API.

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  • I am trying to build a list of limitations of all graph algorithms

    - by Jack
    Single Source shortest Path Dijkstra's - directed and undirected - works only for positive edge weights - cycles ?? Bellman Ford - directed - no cycles should exist All source shortest path Floyd Warshall - no info Minimum Spanning Tree ( no info about edge weights or nature of graph or cycles) Kruskal's Prim's - undirected Baruvka's

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  • Boost graph libraries: setting edge weight values

    - by AndyUK
    I am investigating the use of the boost graph libraries in order to apply them to various network problems I have in mind. In the examples I have been looking at the graph edge values ("weights") are always initialized as integers, such as in these Bellman-Ford and Kruskal algorithms eg: int weights[] = { 1, 1, 2, 7, 3, 1, 1, 1 }; My problem is if I try and change the weights to double, I get a heap of warning messages about conversions etc, which so far I have not been able to figure out how to overcome. Does anyone see a way around this?

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  • Prim's MST algorithm implementation with Java

    - by user1290164
    I'm trying to write a program that'll find the MST of a given undirected weighted graph with Kruskal's and Prim's algorithms. I've successfully implemented Kruskal's algorithm in the program, but I'm having trouble with Prim's. To be more precise, I can't figure out how to actually build the Prim function so that it'll iterate through all the vertices in the graph. I'm getting some IndexOutOfBoundsException errors during program execution. I'm not sure how much information is needed for others to get the idea of what I have done so far, but hopefully there won't be too much useless information. This is what I have so far: I have a Graph, Edge and a Vertex class. Vertex class mostly just an information storage that contains the name (number) of the vertex. Edge class can create a new Edge that has gets parameters (Vertex start, Vertex end, int edgeWeight). The class has methods to return the usual info like start vertex, end vertex and the weight. Graph class reads data from a text file and adds new Edges to an ArrayList. The text file also tells us how many vertecis the graph has, and that gets stored too. In the Graph class, I have a Prim() -method that's supposed to calculate the MST: public ArrayList<Edge> Prim(Graph G) { ArrayList<Edge> edges = G.graph; // Copies the ArrayList with all edges in it. ArrayList<Edge> MST = new ArrayList<Edge>(); Random rnd = new Random(); Vertex startingVertex = edges.get(rnd.nextInt(G.returnVertexCount())).returnStartingVertex(); // This is just to randomize the starting vertex. // This is supposed to be the main loop to find the MST, but this is probably horribly wrong.. while (MST.size() < returnVertexCount()) { Edge e = findClosestNeighbour(startingVertex); MST.add(e); visited.add(e.returnStartingVertex()); visited.add(e.returnEndingVertex()); edges.remove(e); } return MST; } The method findClosesNeighbour() looks like this: public Edge findClosestNeighbour(Vertex v) { ArrayList<Edge> neighbours = new ArrayList<Edge>(); ArrayList<Edge> edges = graph; for (int i = 0; i < edges.size() -1; ++i) { if (edges.get(i).endPoint() == s.returnVertexID() && !visited(edges.get(i).returnEndingVertex())) { neighbours.add(edges.get(i)); } } return neighbours.get(0); // This is the minimum weight edge in the list. } ArrayList<Vertex> visited and ArrayList<Edges> graph get constructed when creating a new graph. Visited() -method is simply a boolean check to see if ArrayList visited contains the Vertex we're thinking about moving to. I tested the findClosestNeighbour() independantly and it seemed to be working but if someone finds something wrong with it then that feedback is welcome also. Mainly though as I mentioned my problem is with actually building the main loop in the Prim() -method, and if there's any additional info needed I'm happy to provide it. Thank you. Edit: To clarify what my train of thought with the Prim() method is. What I want to do is first randomize the starting point in the graph. After that, I will find the closest neighbor to that starting point. Then we'll add the edge connecting those two points to the MST, and also add the vertices to the visited list for checking later, so that we won't form any loops in the graph. Here's the error that gets thrown: Exception in thread "main" java.lang.IndexOutOfBoundsException: Index: 0, Size: 0 at java.util.ArrayList.rangeCheck(Unknown Source) at java.util.ArrayList.get(Unknown Source) at Graph.findClosestNeighbour(graph.java:203) at Graph.Prim(graph.java:179) at MST.main(MST.java:49) Line 203: return neighbour.get(0); in findClosestNeighbour() Line 179: Edge e = findClosestNeighbour(startingVertex); in Prim()

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  • All minimum spanning trees implementation

    - by russtbarnacle
    I've been looking for an implementation (I'm using networkx library.) that will find all the minimum spanning trees (MST) of an undirected weighted graph. I can only find implementations for Kruskal's Algorithm and Prim's Algorithm both of which will only return a single MST. I've seen papers that address this problem (such as http://fano.ics.uci.edu/cites/Publication/Epp-TR-95-50.html) but my head tends to explode someway through trying to think how to translate it to code. In fact i've not been able to find an implementation in any language!

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  • Small questions on data structure

    - by John Graveston
    Hi, I'm trying to search the parent of a node with Kruskal's algorithm. My program works just fine, but I think I have heard of a method to improve the speed of the algorithm by reconstructing the tree while searching for the parent node and connecting it to the parent node. I'm pretty sure that I've heard of this somewhere, maybe in a lecture. Can anyone refresh my memory? And also, given a number of arrays, when searching for the minimum and the maximum value from a certain section of an array, what is the name of the tree that can calculate the minimum/maximum value from the array by making a binary tree that has the minimum/maximum value of each array in O(log N)?

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