Hi all，
Thank you very much for reading this email.
After trying the bigmemory package, I could not figure it out. So I turn to try
ff, hopefully someone here have some experience about it?
I have a data set (data.frame) with 16459 rows and 457 columns. I am
trying to do a multiple imputation for the missing values in the data
set. Usually, my ram (4G) is not enough for the imputation with such a big
data, so I figured maybe I could try
ff. However, I failed again, the ram was still not enough. Here is my code, is
there anything wrong with it?
x=ff("acc3",vmode="double",dim=c(16459,457)) # acc3 is the object
name of the data set
Warning messages:
1: In as.vmode.default(initdata[1], vmode) : NAs introduced by coercion
2: In as.vmode.default(value, vmode) : NAs introduced by coercion
x=acc3
fix(x)
library(mice) # MICE package for multiple imputation for missing value.
Loading required package: MASS
Loading required package: nnet
Loading required package: lattice
mice 2.12 2012-03-25
acc3imp=mice(x,m=50,seed=1234,print=F) # m=50 means data set was
imputed for 50 times, thus, another 50 new
# data sets without missing value were generated.
Any advice are appreciated. Thank you very much.
Best regards,
YA
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
