I am trying to move from 2 sets of data to 3 sets as explained in the above question. Following is the script I used:
set.seed(125)
d <- sample(x = nrow(db), size = nrow(db) * 0.60, )
train60 <-db[d, ]
valid40 <-db[-d, ]
Is there a way to modify the above script? I have tried to create another line:
valid40 <- db[-d] * 0.2
which did not work.
Current dataset has several factor variables.
I have tried using Frank's solution here on the cut
function, but somehow I manage to get
Error in cut.default(seq(nrow(df)), nrow(df) * cumsum(c(0, spec)), labels = names(spec)) : lengths of 'breaks' and 'labels' differ
which I don't understand even after searching for help online.
If I understood you correctly then you want a bifurcation of 60%, 20% and 20% of sample without repeat. I have taken iris data for an example which contains 150 rows and 5 columns.
samp <- sample(1:nrow(iris),.6*nrow(iris)) ##60 and 40 bifurcation
train60 <- iris[samp,] ## This is the 60% chunk
remain40 <- iris[-samp,] ## This is used for further bifurcation
samp2 <- sample(1:nrow(remain40),.5*nrow(remain40))
first20 <- remain40[samp2,] ## First chunk of 20%
secnd20 <- remain40[-samp2,] ## Second Chunk of 20%
Reduce("intersect",list(train60,first20,secnd20)) ##Check to find if there is any intersect , 0 rows means everything is fine and sample are not repetitive.
db <- data.frame(x=1:10, y=11:20)
set.seed(125)
d <- sample(x=nrow(db),size=nrow(db)*0.60,)
train60 <-db[d,]
valid40 <-db[-d,]
Now just take half of valid40 in each new dataframe:
e <- sample(x=nrow(valid40),size=nrow(valid40)*0.50,)
train20 <-valid40[e,]
valid20 <- valid40[-e,]
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