Warning: This is not a helpful answer. Actually, it's a question: Why
do you want to do this? Replacing missing values with row or column
averages and then analyzing the data as if the missing values were not
there is a dangerous thing to do it can produce biased estimates and
understate the true e
Hi Daniel,
If your data is stored in a matrix, the following should work (and be
fairly efficient):
#
dat <- matrix(rnorm(100), nrow = 10)
dat[sample(1:10, 3), sample(1:10, 3)] <- NA
## create an index of missing values
index <- which(is.na(dat), arr.ind = TRUE)
## calculate the row m
Hello,
I have some dataset, which i read it from external file using the (data <-
read.csv("my file location")) and read as a dataframe
> is(data)
[1] "data.frame" "list" "oldClass" "vector"
but i have also converted this into a matrix and tried to apply my code but
didnt work.
Anywa
3 matches
Mail list logo
