On 05/28/2015 12:28 PM, Chris English wrote: > Hi, > > I am wondering about the role of endTime in STIDF objects. I am examining > eye tracking data (previously cleaned of blinks) in relation to > presented stimuli that is for some subjects an optical illusion and for > others not. I want to examine where they were looking and when. > > My process is to make a STIDF from the eye tracking data case and a STSDF of > the stimuli that was presented where and for how long, > convert the STIDF to a Track then do some 'over' analysis. > > If I build my endTime for the STIDF using the delta() function on N samples, > I think I get something like N-1 endTimes, or every sample > is an endTime so N = N. > > If instead I am thinking of endTime(s) as an interval during which there is a > cross hair and some tangential stimulus on the screen and > endTime is when a subject responds in some manner I can't build an STDIF due > to the following test: > >> eye_5v1_stidf <- STIDF(eye_5v1_sp, eye_5v1_time, eye_5v1_data, > + eye_5v1_endTime) > Error: nrow(object@time) == length(object@endTime) is not TRUE >> nrow(eye_5v1_time) > [1] 4724 >> length(eye_5v1_endTime) > [1] 63 >>
endTime is meant to give the end time of the time interval an observation refers to, and so the number of endTime s has to be identical to the number of time instances (number of observations). I guess you figure that out. > > Indeed, it is not true. But what information do I have in endTime other than > my sensor sampling rate adjusted for blinks? What I hoped to > achieve was to compare the spacetime aspects of the Track data through time > periods consistent with the time periods in the STSDF. > Perhaps 'over' takes care of this for me and I don't have to attend if I just > accept that endTime in the case of the STIDF is the end of each > sample. > > The eye tracking data I am examining is fairly simple: x, y, cumulative sum > of samples in ms, duration between samples; from which > an STI can be constructed. Not much more data than where the eyes were when. > It would seem that there would be a lot of simple sensor > data of this sort so I wonder if Track can relax its requirement of STIDF to > allow STI. Good point - I wonder that too. For now, you could feed it a data.frame with zero columns, e.g. data.frame(matrix(nrow=n, ncol=0)) -- Edzer Pebesma Institute for Geoinformatics (ifgi), University of Münster, Heisenbergstraße 2, 48149 Münster, Germany; +49 251 83 33081 Journal of Statistical Software: http://www.jstatsoft.org/ Computers & Geosciences: http://elsevier.com/locate/cageo/ Spatial Statistics Society http://www.spatialstatistics.info
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