[R] Coding question for behavioral data analysis

[jim holtman]

You might try using "outer" to create a matrix that will help out:

> Time <- c(1000, 1050, 1100, 1500, 2500, 5000, 6500, 6600, 7000)
> Time
[1] 1000 1050 1100 1500 2500 5000 6500 6600 7000
> ?outer
starting httpd help server ... done
> x <- outer(Time, Time, FUN = function(a, b){d <- b-a; (d>=0) & (d <= 1000)})
> x
       [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9]
 [1,]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [2,] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [3,] FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
 [4,] FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE
 [5,] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
 [6,] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
 [7,] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE
 [8,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE
 [9,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
>

This says, reading down the columns, that event 4 occurs after 1, 2 &
3 within the window; event 9 occurs after 7 & 8 within the window;
etc.

On Thu, Aug 18, 2011 at 1:29 PM, jabroesch <james.broesch at gmail.com> wrote:
> Hello all,
> I have a question which I have been struggling with for several weeks
> now, that I think might be easy for more proficient coders than
> myself. I have a large behavioral dataset, with behaviors and the
> times (milliseconds) that they occurred. Each subject has a separate
> file, and a sample subject file can be generated using the following
> syntax:
>
> Time <- c(1000, 1050, 1100, 1500, 2500, 5000, 6500, 6600, 7000)
> Behavior <- c("g", "a", "s", "5", " z", "g", "z", "g", "a")
> mydata <- data.frame(Time,Behavior)
>
> My basic goal is to be able to extract some details about what
> behaviors follow another specific behavior within a time window
> (say1000 milliseconds). I figured out how to determine if one specific
> behavior follows another specific behavior within that window with the
> following syntax.
>
> TimeG=mydata$Time[mydata$Behavior == "g"]
> TimeA=mydata$Time[mydata$Behavior == "a"]
> out=rep(NA, length(TimeG))
>
> for (i in 1:length(TimeG)){tmp = TimeA-TimeG[i]
> out[i]=(sum(0 < tmp & tmp <=1000 )>0 ) }
>
> number_of_behaviors<-length(TimeG)
> number_of_affectmirroring<-sum(out)
>
> This generates 2 values: the number of times that the target behavior
> "g" occurred, and the number of times that it was followed by the
> behavior "a" within 1000 milliseconds.
>
> Question:
> What I can't seem to figure out is a to generate a count of the number
> of times that multiple different types of behaviors immediately follow
> a specific behavior within 1000 milliseconds. So say the behavior of
> interest is “g” as it is in the example above. I want to determine
> 1)what was the next behavior (from a specified list of possible
> behaviors bellow) that followed it within 1000 milliseconds.
>
> Ideally the output would 1 row with be 13 columns. The first column
> would be the number of times that the target behavior, "g" in this
> example occurs. The next 12 columns would be the number of times that
> one of the specific behaviors was the next behavior that followed
> within 1000 milliseconds.  So one column for each of these behaviors :
> a s d z x c v q w e r t.
>
> The two complicating factors are: 1)there might be multiple behaviors
> that followed within 1000 milliseconds, and I only want to count the
> first one; and 2)there are additional behaviors that I would like to
> ignore (like the "5" in the example above).
>
> Any help or suggestions are appreciated.
>
> Thank you,
> James Broesch
>
>
> --
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>
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>



-- 
Jim Holtman
Data Munger Guru

What is the problem that you are trying to solve?