On 18/10/11 10:35, Michael Parent wrote:
Hi, all,
I'm running a monte carlo simulation with missing data. The data are arranged
such that there are k columns and n rows over a set number of simulations (set
to 10 right now so it runs fast while I set everything up). The data are
integers, numbers 1-7 only (normal distribution).
For CRYING OUT LOUD. This sort of blithering nonsense makes me
want to SCREAM!!! The normal distribution is a continuous
distribution.
It does not take on (exclusively) integer values.
The simulations are set up and run without a hitch, including imposing NA
missing values at a specified prevalence semi-randomly (there are not allowed
to be any completely empty rows).
I'd like to replace the missing values ("NA") with the mean for the non-missing
items items *on that row*. I want to go through all the monte carlo simulation runs that
I already did (so that I'm using the same data) and replace NA with the mean (e.g., if
k=5 and a row has values of 3 3 NA 5 5, I want to put a 4 in for NA). I also want the
imputed mean values to be rounded to the nearest integer.
Does anyone have an idea for how I'd set that up? I feel like there's a fairly
easy way to set up searching out those NAs and replacing the the row mean that
is not coming to me.
Let your matrix of values be "m".
rv <- round(apply(m,1,mean,na.rm=TRUE))
ij <- which(is.na(m),arr.ind=TRUE)
m[ij] <- rv[ij[,1]]
cheers,
Rolf Turner
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