Hi Ashta,
This does not seem too difficult:
DF$flag<-"n"
for(thisname in unique(DF$Name)) {
if(any(DF$year[DF$Name == thisname] %in% c(2014,2015) &
DF$tag[DF$Name == thisname]))
DF$flag[DF$Name == thisname]<-"y"
}
Jim
On Sun, Feb 28, 2016 at 1:23 PM, Ashta wrote:
> Hi all,
>
> I have a d
(on list, since others might not have gotten it either).
OK, I get it now. It was I who misunderstood.
But isn't the bug in the **misuse** of match() in ecdf() (by failing
to specify the nomatch argument). Jeff says comparisons with NaN
should return an unordered result, which NaN is afaics:
> N
Hi all,
I have a data set represented by the following sample.
I want flag records of an individual as "N", if if the tag column of
an individual is equal to zero for the last two years. So in the
following example, Alex1 records are flagged as "y", On the other
hand Carla's records are fla
That is one valid point, but according to IEEE754 "a comparison with NaN always
returns an unordered result" which it doesn't do unless the incomparables
argument to match is specified. Ick.
--
Sent from my phone. Please excuse my brevity.
On February 27, 2016 3:34:34 PM PST, Bert Gunter wrote
If I understand you correctly, the "bug" is that you do not understand
match(). See inline comment below and note carefully the "Value"
section of ?match.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Be
this happens with both SQLite and MonetDBLite, so i assume it is not an
RSQLite bug.
notice the gc() in the no-crash version..
thanks
# initiate R with "C:\Program Files\R\R-3.2.3\bin\x64\Rterm.exe"
--max-mem-size=35M
library(RSQLite)
db <- dbConnect( SQLite() )
for( i in 1:1000
For some reason `match()` treats `NaN`'s as comparables by default:
> x <- c(1,2,3,NaN,4,5)
> match(x,x)
[1] 1 2 3 4 5 6
which I can override when using `match()` directly:
> match(x,x,incomparables=NaN)
[1] 1 2 3 NA 5 6
but not necessarily when calling a function that uses `match()` inter
Got it. thanks.
On Sat, Feb 27, 2016 at 2:39 PM, peter dalgaard wrote:
> Yeah, well, not much harm done, but once compilers are involved, r-devel
> is usually preferred over r-help.
>
> -pd
>
> > On 27 Feb 2016, at 21:30 , Erin Hodgess wrote:
> >
> > Sorry...thought it was ok since it uses RI
Yeah, well, not much harm done, but once compilers are involved, r-devel is
usually preferred over r-help.
-pd
> On 27 Feb 2016, at 21:30 , Erin Hodgess wrote:
>
> Sorry...thought it was ok since it uses RInside and Rcpp.
>
>
> On Sat, Feb 27, 2016 at 2:15 PM, Jeff Newmiller
> wrote:
>
>>
Sorry...thought it was ok since it uses RInside and Rcpp.
On Sat, Feb 27, 2016 at 2:15 PM, Jeff Newmiller
wrote:
> This is off topic here... wrong audience. Read the Posting Guide.
> --
> Sent from my phone. Please excuse my brevity.
>
> On February 27, 2016 12:00:23 PM PST, Erin Hodgess <
> er
This is off topic here... wrong audience. Read the Posting Guide.
--
Sent from my phone. Please excuse my brevity.
On February 27, 2016 12:00:23 PM PST, Erin Hodgess
wrote:
>Hello again.
>
>This time, I would like to add MPI to my Fortran program. Here are the
>Fortran and C++ codes:
>
>prog
Hello again.
This time, I would like to add MPI to my Fortran program. Here are the
Fortran and C++ codes:
program buzzy
use iso_c_binding
implicit none
include '/opt/openmpi/include/mpif.h'
integer :: rank,size,ierror,tag,status(MPI_STATUS_SIZE), i,np
integer :: argc = 100
On 2/27/2016 1:34 PM, Michael Friendly wrote:
You might also find that an HE plot (library (heplots))
is illuminating.
Follow-up: Try the following with your example
library(heplots)
hs.mod <- lm(cbind(y1, y2) ~ x1 + x2, data=hs.r)
heplot(hs.mod, fill=TRUE)
uv.mod <- lm(cbind(u1, u2) ~ v1 +
Hi Alex,
Thanks for the detailed explanation and the reproducible example.
But it is still not clear exactly what you wish to accomplish.
You know how to calculate the scores on the canonical variates.
These could be considered 'predicted scores', but in canonical
space. What's wrong with that?
I think the advice about the file format is an track, but you imply modifying
the file as a solution but that is probably not the best approach. Using a
decent text editor that shows you what invisible characters are in the file can
guide you in adjusting the arguments to read.table. for example
> On Feb 27, 2016, at 6:04 AM,
> wrote:
>
> Hi,
>
> I read data from file as follows
>
> Data<-read.table("file.txt",header=T,sep="\t")
>
> mode(Data)
> list
>
> I want to convert data to data frame,
It is already a dataframe. That is the class of object that read.table returns.
> I tr
To known the format of your object, please use
class(Data)
str(Data)
Be sure to have regular space between strings in your file.txt.
Karim
On Sat, Feb 27, 2016 at 3:56 PM, Ivan Calandra
wrote:
> Hi,
>
> I have seen this question a few days/weeks ago...
>
> Data.frames are special list, so it's
Hi,
I have seen this question a few days/weeks ago...
Data.frames are special list, so it's normal.
Read the help for read.table(), especially the "value" section (where
the output of the function is described). And read also some
introductory material, where the different data types are expla
Hi,
I read data from file as follows
Data<-read.table("file.txt",header=T,sep="\t")
mode(Data)
list
I want to convert data to data frame, I tried the following:
as.data.frame(Data)
data.frame(Data)
But the Data did not change
When I tried
as.data.frame(unlist(Data))
The Data converted to a
Carolina Arias Muñoz gmail.com> writes:
>
> Hello
>
> I am trying to use "readRAST" in GRASS, but I am keep getting the same
> error:
>
> *Error: 'checkCRSArgs' is not an exported object from 'namespace:rgdal'*
>
> Probably a problem of the rgdal library?
Speculation of this kind is never se
I have a considerable interest in trying to improve the predictions from
10+ highly correlated predictors to two (2) highly correlated responses.
OLS does not allow me to take into account the correlation between the
responses. Multivariate can take into account the correlations between
the pr
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