Find nearest neighbor names from KKNN package - r

Question

I have been trying to build this program or find out how to access what KKNN does to produce its results. I am using the KKNN function and package to help predict future baseball stats. It takes in 11 predictor variables (previous 3 year stats, PA and level, along with age and another predictor). The predictions work great but what I am hoping to do is when I am predicting only one player (as this would be ridiculous while predicting 100s of players), I would like to see maybe the 3 closest neighbors to the player in question and their previous stats with what they produced the next year. I am most concerned with the name of the nearest neighbors as knowing which players are closest will give context to the prediction that it makes.
I am fine with trying to edit the actual code to the function if that is the only way to get at these. Even finding the indices would be helpful as I can backsolve from there to get the names. Thank you so much for all of your help!
Here is some sample code that should help:
name=c("McGwire,Mark","Bonds,Barry","Helton,Todd","Walker,Larry","Pujols,Albert","Pedroia,Dustin")
z
lag1=c(100,90,75,89,95,70)
lag2=c(120,80,95,79,92,90)
Runs=c(65,120,105,99,65,100)
full=cbind(name,lag1,lag2,Runs)
full=data.frame(full)
learn=full
learn
learn$lag1=as.numeric(as.character(learn$lag1))
learn$lag2=as.numeric(as.character(learn$lag2))
learn$Runs=as.numeric(as.character(learn$Runs))
valid=learn[5,]
learn=learn[-5,]
valid
k=kknn(Runs~lag1+lag2,learn,valid,k=2,distance=1)
summary(k)
fit=fitted(k)
fit
Here is the function that I am actually calling if that helps you tailor your answers for workarounds!
kknn(RVPA~(lag1*lag1LVL*lag1PA)+(lag2*lag2LVL*lag2PA)+(lag3*lag3LVL*lag3PA)+Age1‌​+PAsize, RV.learn, RV.valid,k=86, distance = 1,kernel = "optimal")

Answer 1

Here's a slightly modified version of your example:
full= data.frame(
name=c("McGwire,Mark","Bonds,Barry","Helton,Todd","Walker,Larry","Pujols,Albert","Pedroia,Dustin"),
lag1=c(100,90,75,89,95,70),
lag2=c(120,80,95,79,92,90),
Runs=c(65,120,105,99,65,100)
)
library(kknn)
train=full[full$name!="Bonds,Barry",]
test=full[full$name=="Bonds,Barry",]
k=kknn(Runs~lag1+lag2,train=train, test=test,k=2,distance=1)
This predicts Bonds to have 80.2 runs. The Runs variable acts like a class label and if you call k$CL you'll get back 65 and 99 (the number of runs corresponding to the two nearest neighbors). There are two players (McGwire, Pujols) with 65 runs and one with 99, so you can't tell directly who the neighbors are. It appears that the output for kknn does not include a list of the nearest neighbors to the test set (though you could probably back it out from the various outputs).
The FNN package, however, will let you do a query against your training data in the way you want:
library(FNN)
get.knnx(data=train[,c("lag1","lag2")], query=test[,c("lag1","lag2")],k=2)
$nn.index
[,1] [,2]
[1,] 3 4
$nn.dist
[,1] [,2]
[1,] 1.414214 13
train[c(3,4),"name"]
[1] Walker,Larry Pujols,Albert
So nearest neighbors to Bonds are Pujols and Walker.