Hi
Do you want something like this?
> x <- c(1,2,NA, 3, 4, 5, NA, 6,7,8, NA, NA, 9,10)
> y <- c(1,2,NA, NA, 3, 4, 5, 6, NA, 7,8, NA, NA, 9,10)
> identical(x[which(!is.na(x))], y[which(!is.na(y))])
[1] TRUE
If I expect NA and want to extract or compare something, I tend to use which to
select onl
You are asking the wrong question. The right question is, "why are so many
values missing?" Is it because they were censored, not reported for some
reason, due to instrument failure,...? Until you answer that question, any
analysis you do is garbage.
I strongly recommend you consult a competent
Hi Bowie,
As David suggested, you can substitute the R missing value (NA) for
999 (probably an SPSS missing value). If you don't want to change it,
you could probably just subset your data like this:
V<-create_infotables(data=Test[Test[n] != 999,-1],y="class",bins=10)
where "n" is the column num
On 3/10/19 5:29 PM, wong bowie wrote:
> You are right. Actually this variable represents the number of day
> passed after contacting a client, 999 means the client has never been
> contacted.
>
> But I am not supposed to change the value, am I?
I certainly would. SAS allows one to specify a v
I searched the manual for package biomed2 and found 6 references to "AIC".
Perhaps that is your answer.
David L. Carlson
Department of Anthropology
Texas A&M University
-Original Message-
From: R-sig-ecology [mailto:r-sig-ecology-boun...@r-project.org] On Behalf Of
Lara Silva
Sent: Satu
You are right. Actually this variable represents the number of day passed
after contacting a client, 999 means the client has never been contacted.
But I am not supposed to change the value, am I?
David Winsemius 於 2019年3月10日 週日 下午10:48寫道：
> Seems rather likely that 999 is not really a measured
Seems rather likely that 999 is not really a measured value but rather
is a missing value indicator.
--
David.
On 3/10/19 1:54 PM, wong bowie wrote:
I wish to calculate the weight of evidence of a variable x, which is
positively skewed, with over 6000 of the observations are 999 but only 200
I wish to calculate the weight of evidence of a variable x, which is
positively skewed, with over 6000 of the observations are 999 but only 200
range from 1-27. I used the code,
“IV<-create_infotables(data=Test[,-1],y="class",bins=10)”
However, no matter what number I used in bins parameter, I ca
I suspect it isn't actually hung, it is just very, very slow. If I
change the line
x <- (-100:100)/5
to
x <- (-20:20)/5
then it works almost instantly. At
x <- (-50:50)/5
it is slow (16 seconds); replacing the 50 with 60, 70 or 80 yields 29,
46, or 120 seconds respectively. So far I have
I've found some other reports that resemble this, but none seem to have a clear
indication of the source of the hangup. This post raises a non-critical issue,
but
it would be nice to understand the problem and possibly fix it, or else have an
error message generated. As Duncan has posted in one of
On 10/03/2019 1:15 a.m., David Goldsmith wrote:
Thanks, all. I had read about recycling, but I guess I didn't fully
appreciate all the "weirdness" it might produce. :/
With this explained, I'm going to ask a follow-up, which is only
contextually related: the impetus for this discovery was check
Thanks, all. I had read about recycling, but I guess I didn't fully
appreciate all the "weirdness" it might produce. :/
With this explained, I'm going to ask a follow-up, which is only
contextually related: the impetus for this discovery was checking "corner
cases" to determine if all(x[!is.na(x)
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