mt.ttdetect: Detect different trajectory types

In jmbh/mt.analysis: Some function to process 2D motor-tracing data from 2-alternative choice tasks

Description

Uses k-means clustering on a specified vector of variables to identify different types of trajectories

Usage

mt.ttdetect(data, kclust, varclust)

Arguments

data	The dataset containing the mouse-tracing data and the variables used for clustering
kclust	Number of clusters
varclust	Vector of variable names used for clustering

Examples

head(dataprocessed) #example dataset

data.g <- mt.ttdetect(data = dataprocessed, 
                      kclust = 2, #classify in two clusters
                      varclust = c("MAD", "AAD") #use variables "MAD" and "AAD"
                      ) 
                      
head(data.g)
table(data.g$clusters)

jmbh/mt.analysis documentation built on May 19, 2019, 1:51 p.m.