Ted et.That solution is for (in "missing data language") MCAR
(Missing Completely At Random), i.e. the probability
of being missing does not depend on any of the variables
in the data.
For MAR (Missing At Random), the probability of being
missing may depend on the values of covariates but must
not
On 31-Jan-11 04:17:45, David Winsemius wrote:
>
> On Jan 30, 2011, at 10:01 PM, assaedi76 assaedi76 wrote:
>
>> R users:
>> Thanks in advance
>> How to generate missing at random (MAR)?
[DW]:
missidx <- sample(1:nrow(dfrm), nrow(dfrm)*frac)
is.na(dfrm$measure) <- 1:nrow(dfrm) %in% missidx
On Jan 30, 2011, at 10:01 PM, assaedi76 assaedi76 wrote:
R users:
Thanks in advance
How to generate missing at random (MAR)?
> missidx <- sample(1:nrow(dfrm), nrow(dfrm)*frac)
> is.na(dfrm$measure) <- 1:nrow(dfrm) %in% missidx
assaed...@yahoo.com
Thanks
[[alternative HTML
R users:
Thanks in advance
How to generate missing at random (MAR)?
assaed...@yahoo.com
Thanks
[[alternative HTML version deleted]]
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