mydata_x <- rep(seq(200,600,by=200),times=1,each=4)
mydata_y <- rep(seq(600,1200,by=200),times=4,each=1)
mydata_z <- c(0,0,0,0,0,0,0,529,0,0,0,0)
mydata <- data.frame(x=mydata_x,y=mydata_y,z=mydata_z)
I've taken samples from a virtual field and now want to predict neighboring values. It's been done by the two methods described below... both predicting a constant value for each point. I don't understand why.
Keep in mind that I'm a new R user and love it. I've been trying to follow an example I found online to perform my task. Using the example dataset, I have no problem achieving their outcome. However, I'm unable to apply it to my own data. See the example here: http://www.stat.ucla.edu/~nchristo/statistics_c173_c273/c173c273_lec11_w11.pdf
require(geoR)
#create x,y coordinates for locations to predict
kx <- rep(seq(250,700,by=100),times=1,each=length(seq(650,1250,by=100)))
ky <- rep(seq(650,1250,by=100),times=length(seq(250,700,by=100)),each=1)
k_df <- data.frame(x=kx,y=ky) #coordinates as data.frame
k_matrix <- as.matrix(cbind(kx,ky)) #coordinates as.matrix
#convert sample data (top of post) as.geodata
b <- as.geodata(mydata)
#predict values
prediction <- ksline(b,cov.model = "gaussian",cov.pars = c(10,3.33),locations=k_df)
prediction$predict
All 35 outcomes return 44.0833 #I just don't get it!!!
I don't understand how I am returning a single value across the field when there are so many zeros. I would expect to see a single region of decreasing values.
Try this:
prediction <- ksline(b,cov.pars = c(10,200),locations=k_df)
cbind(k_matrix,prediction$predict)[12:20,]
# kx ky
# [1,] 350 1050 93.6977130
# [2,] 350 1150 292.2674563
# [3,] 350 1250 329.6293038
# [4,] 450 650 0.5934677
# [5,] 450 750 -0.1541056
# [6,] 450 850 -2.9329553
# [7,] 450 950 1.8124209
# [8,] 450 1050 93.6977130
# [9,] 450 1150 292.2674563
The kriging algorithm calculates an estimate for a prediction point based on values at "nearby" data points (discounted for distance from the prediction point). The "range" element (second element) of cov.pars=... controls how far to look for "nearby points". You had it set to 3.33, whereas the nearest point was ~ 50 units away. So basically the algorithm wasn't using any nearby points, and the estimate for all points is then simply the mean of all the data values.