Comment inline
On 19/12/2014 11:17, aoife doherty wrote:
Many thanks, I appreciate the response.
When I convert the missing values to NA and run the cox model as described
in previous post, the cox model seems to remove all of the rows with a
missing value (as the number of rows "n" in the cox
Yes, your basic reasoning is correct. In general, the observed variables
carry information about the variables with missing values, so (in some
way) the missing values can be replaced with estimates ("imputations")
and the standard regression method will then work as though the
replacements were th
Many thanks, I appreciate the response.
When I convert the missing values to NA and run the cox model as described
in previous post, the cox model seems to remove all of the rows with a
missing value (as the number of rows "n" in the cox output after I
completely remove any row with missing data
Hi Aoife,
I think that if you simply replace each "*" in the data file
with "NA", then it should work ("NA" is usually interpreted
as "missing" for those functions for which missingness is
relevant). How you subsequently deal with records which have
missing values is another question (or many quest
First recode the * in NA: death.dat$v3[death.dat$v1==*] <- NA
Include this in your model: na.rm=TRUE
Or you could create a new dataset: newdata <- na.omit(death.dat)
Shouro
On Fri, Dec 19, 2014 at 11:12 AM, aoife doherty
wrote:
>
> Hi all,
>
> I have a data set like this:
>
> Test.cox file
Hi all,
I have a data set like this:
Test.cox file:
V1V2 V3 Survival Event
ann 13 WTHomo 41
ben 20 *51
tom 40 Variant 61
where "*" in
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