jaccard_run_global: Simulate random data removal range via Global Cognate Cluster...

In MrMaximumMax/FBCanalysis: Develop and evaluate time series data models based on fluctuation based clustering

Simulate random data removal range via Global Cognate Cluster cluster assignment approach

Description

Simulate amount of random data removal from time series data list and determine Jaccard index via Global Cognate Cluster approach for multiple random data removal steps for a specific cluster of interest.

Usage

jaccard_run_global(
  plist,
  parameter,
  n_simu,
  method,
  clust_num,
  n_clust,
  range,
  maxIter,
  normalize
)

Arguments

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)

Details

See sim_jaccard_global 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.

Value

Object of type list storing Jaccard indices for each indicated random data removal step and visualized results in a boxplot

References

Anja Jochmann, Luca Artusio, Hoda Sharifian, Angela Jamalzadeh, Louise J Fleming, Andrew Bush, Urs Frey, and Edgar Delgado-Eckert. Fluctuation-based clustering reveals phenotypes of patients with different asthma severity. ERJ open research, 6(2), 2020.

Edgar Delgado-Eckert, Oliver Fuchs, Nitin Kumar, Juha Pekkanen, Jean-Charles Dalphin, Josef Riedler, Roger Lauener, Michael Kabesch, Maciej Kupczyk, Sven-Erik Dahlen, et al. Functional phenotypes determined by fluctuation-based clustering of lung function measurements in healthy and asthmatic cohort participants. Thorax, 73(2):107–115, 2018.

Examples

list <- patient_list(
"https://raw.githubusercontent.com/MrMaximumMax/FBCanalysis/master/demo/phys/data.csv",
GitHub = TRUE)
output <- jaccard_run_global(list,"PEF",10,"hierarchical",1,3,c(0.005,0.01,0.05,0.1,0.2))

MrMaximumMax/FBCanalysis documentation built on June 23, 2022, 8:21 p.m.