Euclidean Distance in 3 Dimensions

This function unfortunately does not work in 3d space.

Thoughts?


On Wed, Aug 20, 2014 at 4:57 PM, Don McKenzie <dmck at u.washington.edu<mailto:dmck at u.washington.edu>> wrote:
?dist

from the help

dist {stats}    R Documentation
Distance Matrix Computation

Description

This function computes and returns the distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.

Is this what you want?  Computing on a matrix whose rows are your x, y, and z values?


On Aug 20, 2014, at 1:12 PM, Patzelt, Edward <patzelt at g.harvard.edu<mailto:patzelt at g.harvard.edu>> wrote:

R Community -

I am attempting to write a function that will calculate the distance
between points in 3 dimensional space for unique regions (e.g. localized
brain regions such as the frontal lobe).

For example I'm looking to compare each point in region 45 to every other
region in 45 to establish if they are a distance of 8 or more apart. I can
do this linearly comparing each distance to the previous but this is not
comparing all points.

structure(list(Cluster.Index = c(46L, 46L, 46L, 46L, 46L, 45L,
45L, 45L, 45L, 45L, 44L, 44L, 44L, 44L, 44L, 43L, 43L, 43L, 43L,
43L), Value = c(8.21, 7.96, 7.85, 7.83, 7.8, 5.38, 4.56, 4.5,
4, 3.99, 5.42, 4.82, 4.21, 4.18, 3.91, 4.79, 4.27, 3.24, 3.06,
3.04), x = c(33L, 38L, 37L, 36L, 38L, 47L, 42L, 43L, 44L, 42L,
50L, 41L, 39L, 41L, 44L, 46L, 45L, 45L, 41L, 46L), y = c(15L,
12L, 12L, 13L, 13L, 91L, 84L, 84L, 95L, 96L, 69L, 70L, 65L, 65L,
59L, 41L, 40L, 46L, 44L, 47L), z = c(41L, 38L, 41L, 39L, 33L,
39L, 40L, 42L, 44L, 45L, 34L, 36L, 30L, 35L, 39L, 53L, 47L, 61L,
52L, 57L), X = c(NA, 6.557438524302, 3.16227766016838, 2.44948974278318,
6.32455532033676, 78.7464284904401, 8.66025403784439, 2.23606797749979,
11.2249721603218, 2.44948974278318, 30.2324329156619, 9.2736184954957,
8.06225774829855, 5.3851648071345, 7.81024967590665, 22.8910462845192,
6.16441400296898, 15.2315462117278, 10.0498756211209, 7.68114574786861
)), .Names = c("Cluster.Index", "Value", "x", "y", "z", "X"), row.names =
c(NA,
20L), class = "data.frame")

mainDat <- data.frame()
for(i in 2:nrow(dat)){
tempDist <- (sqrt((dat$x[i] - dat$x[i-1])^2 + (dat$y[i] - dat$y[i-1])^2 +
(dat$z[i] - dat$z[i-1])^2))
dat$X[i] <- c(tempDist)
if(dat$Cluster.Index[i] != dat$Cluster.Index[i-1]){
mainDat <- rbind(mainDat, dat[i,])
}
if((dat$Cluster.Index[i] == dat$Cluster.Index[i-1])) {
if(tempDist > 8){
mainDat <- rbind(mainDat, dat[i,])
}
}
}




--

*Edward H Patzelt | Clinical Science PhD StudentPsychology | Harvard
University *

      [[alternative HTML version deleted]]

Don McKenzie
Research Ecologist
Pacific Wildland Fire Sciences Lab
US Forest Service

Affiliate Professor
School of Environmental and Forest Sciences
University of Washington
dmck at uw.edu<mailto:dmck at uw.edu>







--
Edward H Patzelt | Clinical Science PhD Student
Psychology | Harvard University