Random Forest run

Question

I am struggling to understand the different between the inputs of RF codes on Package 'randomForest'. This reference suggested to use

  ## S3 method for class 'formula'
randomForest(formula, data=NULL, ..., subset, na.action=na.fail)
## Default S3 method:
randomForest(**x**,  **y** =NULL, xtest=NULL, ytest=NULL, ntree=500,

,,,,,,

As I understand, x is the data frame with predictors and y is the response variabel. I see, however, the example of producing this code from the same paper is using the response variable first then the data,

 iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE,
proximity=TRUE)

So, I have wrote my code having both options but I am not sure which one is correct for classification and why?

Here is my code: 

I am basically comparing the two codes for rf.




## create data frame 
 n   <- 199
  z   <- seq(-10, 10, length=n)
x<-sin(x)/x       
  y <-  rnorm(n, 0, 0.1)
  xy <- data.frame(x,y)


## create classes
 xy$Y<-sample(1:2,  n, replace = T)
   XY<-xy
   n <- nrow(XY)
   p <- ncol(XY)-1
   colnames(XY)[p+1]<-'Y'




## create trining and test sets
s     <- sample(sample(n)) 
    ntr   <- round(ptr*n) 
    id.tr <- s[1:ntr]
    id.te <- s[(ntr+1):n]
    XY.tr <- XY[id.tr, ]
    XY.te <- XY[id.te, ]
    y.te  <- XY[id.te, p+1]

    XY.tr$Y<-as.factor(XY.tr$Y)

##run Random forest
rf1 <- randomForest(XY.tr, data=XY.tr$Y, proximity=TRUE,importance=T) 
rf2<-randomForest(formula = XY.tr$Y ~ .,  data=XY.tr, proximity = TRUE, importance = T) 

Thank you very much for any insight

Answer 1

Both will give you the same answer:

data(iris)                                                    #load data

In first approach you explicitly provide the response vector y (but correct your code accordingly):

set.seed(131)
rf1 <- randomForest(y= iris$Species, x=iris[1:4], proximity=TRUE, importance=T)  

In second approach you implicitly inform about the response vector y through formula and provide the whole data matrix.

set.seed(131)
rf2<-randomForest(formula = Species ~ ., data=iris, importance=TRUE, proximity=TRUE)

See this R documentation for randomForest :

Argument: x, formula:
a data frame or a matrix of predictors, or a formula describing the model
to be fitted (for the print method, an randomForest object).