R: Visualise a distance matrix using a heatmap.
A square adjacency matrix. From igraph version 0.5.1 this can be a sparse matrix created with the Matrix package. mode: Character scalar, specifies how igraph should interpret the supplied matrix. See also the weighted argument, the interpretation depends on that too. Possible values are: directed, undirected, upper, lower, max, min, plus. See.
If you’re interested in following a course, consider checking out our Introduction to Machine Learning with R or DataCamp’s Unsupervised Learning in R course!. Using R For k-Nearest Neighbors (KNN). The KNN or k-nearest neighbors algorithm is one of the simplest machine learning algorithms and is an example of instance-based learning, where new data are classified based on stored, labeled.
Fixing Axes and Labels in R Plot Using Basic Options Riaz Khan, South Dakota State University August 8, 2017. Ofter we suffer from a common problem while making graphs in R. Often we think of customized axes and labels in R plot, may be even inserting text. This is an effort to aggregate some of the things we look for every now and then. A default plot. Here some random numbers were generated.
Make the Confusion Matrix Less Confusing. A confusion matrix is a technique for summarizing the performance of a classification algorithm. Classification accuracy alone can be misleading if you have an unequal number of observations in each class or if you have more than two classes in your dataset. Calculating a confusion matrix can give you a better idea of what your classification model.
Cluster Analysis in R. This page covers the R functions to perform cluster analysis. Some of these methods will use functions in the vegan package, which you should load and install (see here if you haven’t loaded packages before). Cluster analysis in R requires two steps: first, making the distance matrix; and second, applying the agglomerative clustering algorithm.
Defining Neighbors, Creating Weight Matrices. This entry outlines a few procedures that come with the spdep package. All commands have options, but most of these are not mentioned here. Neighbors. Neighbors will typically be created from a spatial polygon file. Neighbors can be based on contiguity, distance, or the k nearest neighbors may be defined. Finally, second or third order neighbors.
Input Data and Formats. Input file formats for different kinds of data are discussed in this chapter. In addition, the use of in-memory data editing options is explained. Note that there is no limit on the amount of molecular sequence or distance matrix data that can be analyzed in MEGA; the size of data set is constrained only by the computer memory available. 2.1 MEGA Format. Either sequence.