Hi,
I would like to break a dataset in n.classes quantiles.
Till now, I used the following code:
Classify.Quantile <- function (dataset, nclasses = 10)
{
n.probs <- seq(0,1,length=nclasses+1)
n.labels = paste("C", 1:nclasses-1, sep="")
n.rows <- nrow(dataset)
n.cols <- ncol(dataset)
n.motif <- dataset
for (j in 2:n.cols)
{
cat(j, " ");
discr =
n.labels[unclass(cut(dataset[,j],quantile(dataset[,j],n.probs),include.lowest=T))]
n.motif[,j] = discr
}
res <- list(motif=n.motif, labels=n.labels, n.classes=nclasses)
return(res)
}
but if you try to call this with a dataset with a lot of same value, you got a
Error in cut.default(dataset[, j], quantile(dataset[, j], n.probs),
include.lowest = T) :
cut: breaks are not unique
I perfectly understand why but I would like to know how to avoid this behaviour.
for e.g., use this code to raise the error:
x=matrix(0,1000,1)
x[100]=1
Classify.Quantile(x, 10)
of course this dataset is a bit extreme but it happens to get data
with very small variance.
Thanks for any help you could provide
Eric Rodriguez wrote:> Hi, > > I would like to break a dataset in n.classes quantiles. > Till now, I used the following code: > Classify.Quantile <- function (dataset, nclasses = 10) > { > n.probs <- seq(0,1,length=nclasses+1) > n.labels = paste("C", 1:nclasses-1, sep="") > n.rows <- nrow(dataset) > n.cols <- ncol(dataset) > n.motif <- dataset > > for (j in 2:n.cols) > { > cat(j, " "); > discr = n.labels[unclass(cut(dataset[,j],quantile(dataset[,j],n.probs),include.lowest=T))] > n.motif[,j] = discr > } > > res <- list(motif=n.motif, labels=n.labels, n.classes=nclasses) > return(res) > } > > > but if you try to call this with a dataset with a lot of same value, you got a > Error in cut.default(dataset[, j], quantile(dataset[, j], n.probs), > include.lowest = T) : > cut: breaks are not unique > > I perfectly understand why but I would like to know how to avoid this behaviour. > > for e.g., use this code to raise the error: > x=matrix(0,1000,1) > x[100]=1 > Classify.Quantile(x, 10) > > of course this dataset is a bit extreme but it happens to get data > with very small variance. > > > Thanks for any help you could provideThe cut2 function in the Hmisc package may help. -FH -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University