jaccard_run_emd | R Documentation |
Simulate amount of random data removal from time series data list and determine Jaccard index via Earth Mover's Distance approach for multiple random data removal steps for a specific cluster of interest.
jaccard_run_emd( plist, parameter, n_simu, method, clust_num, n_clust, range, maxIter, normalize )
plist |
Object of type list storing patient time series data (also see function: patient_list) |
parameter |
Parameter of interest in time series data list |
n_simu |
Number of simulations |
method |
Clustering method (also see function: clust_matrix) |
clust_num |
Cluster of interest |
n_clust |
Number of clusters |
range |
Range to simulate random data removal (e.g. c(0.1,0.2,0.5,0.7,0.8)) |
maxIter |
Maximum iterations to determine Earth Mover's Distances (also see function: emd_matrix); default is 5,000 for this function |
normalize |
Indicates if parameter indicated needs to be normalized or not (TRUE by default) |
See sim_jaccard_emd for more detailed approach on Jaccard index determination. The difference in this function is that now only one cluster is observed für multiple amounts of random data removal where for each data removal step defined the resulting Jaccard indices are stored in a list object. Furthermore, a boxplot visualization is generated, in the style of recent publications.
Object of type list storing Jaccard indices for each indicated random data removal step and visualized results in a boxplot
Yossi Rubner, Carlo Tomasi, and Leonidas J Guibas. A metric for distributions with applications to image databases. In Sixth International Conference on Computer Vision (IEEE Cat. No. 98CH36271), pages 59–66. IEEE, 1998.
list <- patient_list( "https://raw.githubusercontent.com/MrMaximumMax/FBCanalysis/master/demo/phys/data.csv", GitHub = TRUE) #Sampling frequency is supposed to be daily output <- jaccard_run_emd(list,"PEF",10,"hierarchical",1,3,c(0.005,0.01,0.05,0.1,0.2))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.