Displaying 4 results from an estimated 4 matches for "filtereddata".
2009 Feb 11
3
Filter a big matrix
Hi,
I have a big matrix X with M rows and N columns that I want to filter
it into smaller ones with m (<M) rows and N columns.
The filter rule is based on the values of each columns, i.e.
X looks like this:
column name: a, b, c, d, ... etc
a b c d ...
1 2 3 4 ...
5 6 7 8 ...
9 8 7 6 ...
... ... ... ...
The filter rule with the result that I want is:
2009 Mar 02
2
R-code help for filtering with for loop
...nknown")
filter<-datax[,1:6]
filtered<-vector()
for(i in 1:(dim(filter)[1]))
{
for(j in 1:(dim(filter)[2]))
{
x=(filter[i,j])>=64
filtered[i]<-x
}
}
# summing the result of the above
sum(filtered)
which(filtered)
z<-which(filtered)
filereddata<-filter[z,]
write.table(filtereddata,file ="filterdgenes.txt",quote = TRUE, sep = "\t ",
dec = ".",row.names=T,col.names = NA, qmethod = c (escape",
"double"))
---------------------------
There is something is missing in my coding therefore the filteration is done
according t...
2009 Dec 22
1
Slow survfit -- is there a faster alternative?
...s in a data frame
with 20+ variables.
I'd like to be able to quickly generate estimate and plot survival
curves. However the survfit and cph() functions are extremely slow.
As an example: I tried
results.cox<-coxph(Surv(duration, success) ~ start_time + factor1+
factor2+ variable3, data=filteredData) #(took a few seconds)
plot(results.cox)
#(never finished in an hour)
I also tried the cph() function, with similar results.
Is there some easier quick-and-dirty way of producing and plotting
survival curves for large data sets? I've seen some references on this
list that suggest that the u...
2009 Dec 22
0
slow survfit -- is there a better replacement?
...s in a data frame
with 20+ variables.
I'd like to be able to quickly generate estimate and plot survival
curves. However the survfit and cph() functions are extremely slow.
As an example: I tried
results.cox<-coxph(Surv(duration, success) ~ start_time + factor1+
factor2+ variable3, data=filteredData)
#(took a few seconds)
plot(results.cox)
#(never finished in an hour)
I also tried the cph() function, with similar results.
Is there some easier quick-and-dirty way of producing and plotting
survival curves for large data sets? I've seen some references on this
list that suggest that the...