Dear All,
Independent censoring is one of the fundamental assumptions in the survival
analysis. However, I cannot find any test for it or any paper which discusses
how real that assumption is.
I would be grateful if anybody could point me to some useful references. I have
found the following
Doesn't look right to me... you are likely to need to change something to
handle the multiple directories thing somehow, but I don't know why you made
the changes you did make to my suggestion.
--
Sent from my phone. Please excuse my brevity.
On November 28, 2016 5:56:04 PM PST, Val wrote:
>Hi
Hi Jeff and John,
Thank you for your response.
In each folder, I am expecting a single file name (either dat or
dat.csv).v so will this work?
Is the following correct?
fns <- list.files(mydir)
if (is.element(pattern="dat(\\.[^.]+)$",fns ))
Thank you again.
On Mon, Nov 28, 2016 at 7:20 P
No, and yes, depending what you mean.
No, because you have to supply the file name to open it... you cannot directly
use wildcards to open files.
Yes, because the list.files function can be used to match all file names
fitting a regex pattern, and you can use those filenames to open the files
On Mon, Nov 28, 2016 at 4:23 PM, Ashta wrote:
> Hi all,
>
> I have a script that reads a file (dat.csv) from several folders.
> However, in some folders the file name is (dat) with out csv and in
> other folders it is dat.csv. The format of data is the same(only the
> file name differs with
I'm using R 3.3.1
when installing/ updating a library module, for example "Hmisc" I get an error
message about "unable to move..."
cutting/pasting
survival’ successfully unpacked and MD5 sums checkedWarning: unable to move
temporary installation
‘C:\Users\Chris\Documents\R\win-library\3.3\file
Hi all,
I have a script that reads a file (dat.csv) from several folders.
However, in some folders the file name is (dat) with out csv and in
other folders it is dat.csv. The format of data is the same(only the
file name differs with and without "csv".
Is it possible to read these files dep
To help with the clarification, I renamed 'col1' to 'year' and 'col2' to
'origDat'. With that said...
The reason the second 'block' of 1's (four consecutive 1's appearing in
DF$origDat[11:14]) is preserved is because they are only separated by a
total of 1 year (1998 in DF$year) from a larger gro
On 11/28/2016 1:06 AM, jean-philippe wrote:
dear all,
I have a dataframe of 500 rows and 4004 columns that I would like to
reshape to a dataframe of 500500 rows and 4 columns. That is from this
dataframe:
V1 V2 V3 V4 ... V4001 V4002 V4003 V4004
1 2 3 4 ... 4001 4002 4003 4004
1 2 3 4 ... 4001
> On Nov 28, 2016, at 9:38 AM, Morway, Eric wrote:
>
> The example below is a pared-down version of a much larger dataset. My
> goal is to use the binary data contained in DF$col2 to guide manipulation
> of the binary data itself, subject to the following:
>
> - Groups of '1' that are separa
There may be a simpler way of getting there, but this works:
> rows <- 500
> cols <- 4004
> dat <- as.data.frame(t(replicate(rows, 1:cols)))
> dat[c(1:3, 500), c(1:4, 4001:4004)]
V1 V2 V3 V4 V4001 V4002 V4003 V4004
11 2 3 4 4001 4002 4003 4004
21 2 3 4 4001 4002 4003 4004
dear all,
I have a dataframe of 500 rows and 4004 columns that I would like to
reshape to a dataframe of 500500 rows and 4 columns. That is from this
dataframe:
V1 V2 V3 V4 ... V4001 V4002 V4003 V4004
1 2 3 4 ... 4001 4002 4003 4004
1 2 3 4 ... 4001 4002 4003 4004
1 2 3 4 ... 4001 4002 400
The example below is a pared-down version of a much larger dataset. My
goal is to use the binary data contained in DF$col2 to guide manipulation
of the binary data itself, subject to the following:
- Groups of '1' that are separated from other, larger groups of "1's" in
'col2' by 2 or more
You probably need to upgrade your R software to the current version. Many CRAN
mirrors don't keep binary package repositories for old versions of R online,
and the Posting Guide warns that old versions of R are effectively off-topic on
the mailing lists.
You could also try downloading the zip f
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