mt.preprocess: Peprocess motor-tracing data

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

Description

Time-normalizes the trajectories and computes a set of derivatives of the trajectories

Usage

mt.preprocess(data, box.cor, i.id, i.measure, tsteps = 101)

Arguments

data	n x m - matrix with n measurements and m variables
box.cor	List containting the position of the center of the two response boxes and the starting position of the cursor
i.id	Vector of column names indicating the id-variables identifying unique trajectries
i.measure	Vector of column names indicating the columns for the measures of x, y, time and the selected box (left=0 or right=1)
tsteps	The number of time-steps the data is normalized to. Default = 101.

References

Spivey, M. J., Grosjean, M., & Knoblich, G. (2005). Continuous attraction toward phonological competitors. Proceedings of the National Academy of Sciences of the United States of America, 102(29), 10393-10398.

Examples

head(dataraw) #example dataset

box.cor <- list("start"=c(960,230), "left"=c(130,905), "right"=c(1830,905)) #coordinates from the example dataset
i.id <- c("trial")
i.measure <- c("x", "y","t", "b")
data.norm <- mt.preprocess(data=dataraw,
                      box.cor = box.cor, 
                      i.id, 
                      i.measure, 
                      tsteps=101)
head(data.norm[,1:9])

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