prepr: Preprocessing data from 2D mousetracking data in dual choice...

In jmbh/mta: Processing of 2d movement data from 2-choice mousetracking experiments

Description

Function normalizes trajectories with respect to time or space.

Usage

prepr(data, i.xyt, i.id, type, steps = 101, start2zero = TRUE, stretch = NA, takeAllvar = FALSE)

Arguments

data	A data frame containing the x, y, t variables and indicator variables which uniquely identify single trajectories
i.xyt	Vector containing the column names of the x, y, t variables in that order.
i.id	Vector containing the column names of the indicator variables that jointly uniquely identify single trajectories (e.g. c('participant_id', 'trial_id')).
type

type='time' normalizes trajectory with respect to time (all trajectories have the same number of steps of time steps). This is the standard data preprocessing procedure for mousetracking. type='spatial' normalizes trajectories with respect to space, i.e. we place a specified number of equally spaced (with respect to space) points on the trajectory. This is useful for comparing trajectories using a distance measure.

steps	The number of steps that should intrapolated (with respect to time or space, depending on type )
start2zero	If TRUE, the startpoint of each trajectory is set to (0,0)
stretch	Each trajectory is 'streched' into a specified norm-display. The norm display is specified as a list with the coordinates of the start point and the two possible end points of the trajectories. See the example below. This is useful for comparing data from various studies with different layout.
takeAllvar	Default FALSE. If TRUE, the returned dataframe contains all columns of the input dataframe. Otherwise only the columns x,y,t and the id-variables are returned.

Value

The function returns a list:

call	Returns all function input except the data.
data	Returns a data frame with normalized trajectories.

Author(s)

Jonas Haslbeck <[e-mail]>

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

## Not run: 

# THIS EXAMPLE DOES NOT WORK ANYMORE !!!


# we use a part of the following example dataset

head(data_sp2015)

# we use prepr() to time normalize all trajectories to 101 time-steps and strech them to a norm display

layout_stretch <- list("start"=c(0,0), "left"=c(-1,1.5), "right"=c(1,1.5))

output <- prepr(data = data_sp2015[1:1000,], 
                i.xyt = c('x', 'y', 't'), 
                i.id =  c('id.ptp', 'id.trial'), 
                type = "time", 
                steps = 101, 
                start2zero = TRUE, 
                stretch = layout_stretch)

head(output$data)


## End(Not run)

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