I believe mapshaper has functionality for removing small 'islands'.
There is a webinterface for mapshaper, but I see there is also an
R-package (see
https://search.r-project.org/CRAN/refmans/rmapshaper/html/ms_filter_islands.html
for island removal).
If you want to manually select which
exchanging things with my
Spanish speaking
colleagues). Jan or anyone: any simple reassurance or pointers to
resources I should
best use for homework about these issues?
TIA (again!)
Chris
On 06/10/2023 12:55, Jan van der Laan wrote:
You are right, sorry.
Another possible solution then: use
t; = "grey",
"C" = "green"),
labels = c("Deteriorated",
"No change",
"Improved")) +
scale_fill_manual(name = "Change",
Does adding
, show.legend = c("color"=TRUE, "fill"=FALSE)
to the geom_point do what you want?
Best,
Jan
On 06-10-2023 11:09, Chris Evans via R-help wrote:
library(tidyverse)
tibble(x = 2:9, y = 2:9, c = c(rep("A", 5), rep("B", 3))) -> tmpTibPoints
tibble(x = c(1, 5, 5, 1), y = c(1, 1, 5,
Shorter/simpler alternative for adding a alpha channel
adjustcolor("lightblue", alpha = 0.5)
So I would use something like:
# Open new plot; make sure limits are ok; but don't plot
plot(0, 0, xlim=c(1,20),
ylim = range(c(mean1+sd1, mean2+sd2, mean1-sd1, mean2-sd2)),
type="n", las=1,
The dredge function has a `extra` argument to get other statistics:
optional additional statistics to be included in the result, provided as
functions, function names or a list of such (preferably named or
quoted). As with the rank argument, each function must accept as an
argument a fitted
Perhaps the ragg package? That has an `agg_capture` device "that lets
you access the device buffer directly from your R session."
https://github.com/r-lib/ragg
HTH,
Jan
On 28-05-2023 13:46, Duncan Murdoch wrote:
Is there a way to open a graphics device that plots entirely to an array
Hi Avi, list,
Below an alternative suggestion:
func <- function(a, b, c) {
list(a, b, c)
}
1:3 |> list(x = _) |> with(func(a, x, b))
Not sure if this is more readable than some of the other solutions, e.g.
your solution, but you could make a variant of with more specific for
this use
You could try to see what stattransfer can make of it. They have a free
version that imports only part of the data. You could use that to see if
stattransfer would help and perhaps discover what format it is in.
HTH
Jsn
On 16-01-2023 23:22, Joseph Voelkel wrote:
Dear foreign maintainers and
You're sure the extra column is indeed an extra column? According to the
documentation
(https://artefacts.ceda.ac.uk/badc_datadocs/ukmo-midas/RH_Table.html)
there should be 15 columns.
Could it, for example, be that one of the columns contains records with
commas?
Jan
On 29-09-2022
For visualising hierarchical data a treemap can also work well. For
example, using the treemap package:
n <- 1000
library(data.table)
library(treemap)
dta <- data.table(
level1 = sample(LETTERS[1:5], n, replace = TRUE),
level2 = sample(letters[1:5], n, replace = TRUE),
level3 =
Have a look at the base functions tapply and aggregate.
For example see:
-
https://cran.r-project.org/doc/manuals/r-release/R-intro.html#The-function-tapply_0028_0029-and-ragged-arrays
,
- https://online.stat.psu.edu/stat484/lesson/9/9.2,
- or ?tapply and ?aggregate.
Also your current code
mptyenv())
Hope this helps!
On Tue, Nov 2, 2021, 06:49 Yonghua Peng wrote:
But for data.frame the colnames can be duplicated. Am I right?
Regards.
On Tue, Nov 2, 2021 at 6:29 PM Jan van der Laan
wrote:
True, but in a lot of cases where a python user might use a dict
an R user will prob
ate a data.frame with
multiple columns with the same name. But as Duncan Murdoch mentions you
can usually control for that.
Best,
Jan
On 02-11-2021 11:32, Yonghua Peng wrote:
But for data.frame the colnames can be duplicated. Am I right?
Regards.
On Tue, Nov 2, 2021 at 6:29 PM Jan van der L
True, but in a lot of cases where a python user might use a dict an R
user will probably use a list; or when we are talking about arrays of
dicts in python, the R solution will probably be a data.frame (with each
dict field in a separate column).
Jan
On 02-11-2021 11:18, Eric Berger
What you could also try is check if the self coded functions use the
random generator when defining them:
starting_seed <- .Random.seed
Step 1. Self-coded functions (these functions generate random numbers as
well)
# check if functions have modified the seed:
all.equal(starting_seed,
read_fst is from the package fst. The fileformat fst uses is a binary
format designed to be fast readable. It is a column oriented format and
compressed. So, to be able to work fst needs access to the file itself
and wont accept a file connection as functions like read.table an
variants
This is an optimisation problem that you are trying to solve using a
grid search. There are numerous methods for optimisation, see
https://cran.r-project.org/web/views/Optimization.html for and overview
for R. It really depends on the exact problem what method is appropriate.
As Petr said
But even if you could have a generator that is superefficient and
perform an calculation that is superfast the number of elements is
ridiculously large.
If we take 1 nanosec per element; the computation would still take:
> (100^10)*1E-9/3600
[1] 2778
hours, or
>
I would actually go a step in the other direction: per project
libraries. For example by adding a .Rprofile file to your project
directory. This ensures that everybody working on a project uses the
same version of the packages (even on different machines e.g. on shared
folders).
This can
Alejandra,
If it was initially working ok, I would first check with the IT
department if there has been a change to the configuration of the
firewall, virus scanners, file system etc. as these can affect the
performance of R-studio. R-studio uses a client-server setup on your
machine, so a
Are you sure you didn't do saveRDS("rawData", file = "rawData.rds")
instead of saveRDS(rawData, file = "rawData.rds") ? This would explain
the result you have under linux.
In principle saveRDS and readRDS can be used to copy objects between
R-sessions without loosing information.
What
Below a similar example, using sf and leaflet; plotting the trajectory
on a background map.
library(leaflet)
library(sf)
library(dplyr)
# Generate example data
gen_data <- function(id, n) {
data.frame(
id = id,
date = 1:n,
lat = runif(10, min = -90, max = 90),
lon =
or the first 5 rows.
but it did not work. Do you have any suggestion about it?
On Fri, 26 Oct 2018 at 21:31, Jan van der Laan <mailto:rh...@eoos.dds.nl>> wrote:
Using another implementation of the gower distance:
library(gower)
gower_dist(iris[1,], iris)
HTH,
Using another implementation of the gower distance:
library(gower)
gower_dist(iris[1,], iris)
HTH,
Jan
On 26-10-18 15:07, Aerenbkts bkts wrote:
I have a data-frame with 30k rows and 10 features. I would like to
calculate distance matrix like below;
gower_dist <- daisy(data-frame,
Or
testdf <- testdf[FALSE, ]
or
testdf <- testdf[numeric(0), ]
which seems to be slightly faster.
Best,
Jan
Op 27-9-2018 om 10:32 schreef PIKAL Petr:
Hm
I would use
testdf<-data.frame(A=c(1,2),B=c(2,3),C=c(3,4))
str(testdf)
'data.frame': 2 obs. of 3 variables:
$ A: num 1 2
$
You can also inadvertently transmit data to the internet using a package
without being obviously 'stupid', e.g. by using a package that uses an
external service for data processing. For example, some javascript
visualisation libs can do that (not sure if those wrapped in R-packages
do), or,
With lrm.fit you are fitting a completely different model. One of the
things lrm does, is preparing the input for lrm.fit which in this case
means that dummy variables are generated for categorical variables such
as 'KILLIP'.
The error message means that model did not converge after the
First, the file does contain 302 columns; the variable layout
(http://www.cdc.gov/brfss/annual_data/2006/varlayout_table_06.htm)
contains 302 columns. So, reading the SASS file probably works correctly.
Second, the read.asc function you use is for reading geographic raster
files, not fixed
Could it be that your r-script is saved in a different encoding than
the one used by R (which will probably be UTF8 since you're working on
linux)?
--
Jan
gerald.j...@dgag.ca schreef:
Hello,
I am using R, 2.15.2, on a 64-bit Linux box. I run R through Emacs' ESS.
R runs in a
The following seems to work:
data = read.csv.ffdf(x=NULL,file=data.csv,nrows=1001,first.rows = 500,
next.rows = 1005,sep=,,colClasses = c(integer,factor,logical))
'character' doesn't work because ff does not support character
vectors. Character vector need to be stored as factors. The
-
Von: Jan van der Laan [mailto:rh...@eoos.dds.nl]
Gesendet: Freitag, 9. August 2013 10:01
An: Kamenik Christian ASTRA
Betreff: Re: AW: AW: [R] laf_open_fwf
Christian,
It seems some of the lines in your file have additional characters at the end
causing the line lengths to vary. The only way I
89
Fax +41 31 323 43 21
christian.kame...@astra.admin.ch
www.astra.admin.ch
-Ursprüngliche Nachricht-
Von: Jan van der Laan [mailto:rh...@eoos.dds.nl]
Gesendet: Mittwoch, 7. August 2013 20:57
An: r-help@r-project.org
Cc: Kamenik Christian ASTRA
Betreff: Re: [R] laf_open_fwf
Dear Christian
I don't know how many files you are planning to open, but what you
also might run into is the maximum number of connections namely 125.
See ?file.
Jan
mohan.radhakrish...@polarisft.com schreef:
Hi,
I thought that 'R' like java will allow me to store file names
(keys) and file
Dear Christian,
Well... it shouldn't normally do that. The only way I can currently
think of that might cause this problem is that the file has \r\n\r\n,
which would mean that every line is followed by an empty line.
Another cause might be (although I would not really expect the results
you
What probably is the problem is that read.table.ffdf uses the nrows
argument to read the file in chunks. However, read.fwf doesn't use a
nrow argument but a n argument.
One (non tested) solution is to write a wrapper around read.fwf and pass
this wrapper to read.table.ffwf. Something like:
Characters in R are zero terminated (although I couldn't find that in
the R extensions manual). So, you could use:
void dealWithCharacter(char **chaine, int *size){
Rprintf(The string is '%s'\n, chaine[0]);
}
Jan
On 05/10/2013 03:51 PM, cgenolin wrote:
Hi the list,
I include some C
Some colleagues ran into similar problems after migrating to windows 7.
They could no longer install packages in certain network locations
because the read only bit was set (which could be unset after which
windows set it again). Perhaps the following helps:
I believe it was already mentioned, but I can recommend the LaF package
(not completely impartial being the maintainer of LaF ;-)
However, the speed differences between packages will not be very large.
Eventually all packages will have to read in 6 GB of data and convert
the text data to
Have a look at the system command:
?system
HTH,
Jan
On 03/16/2013 10:09 PM, Sedat Sen wrote:
Dear list,
I want to run a statistical program (using its .exe file) from R by
writing a script. I know there are some packages that call WinBUGS, Mplus
etc. form R. I just want to call the .exe
apply((t(as.matrix(b)) * a), 2, sum)
should do what you want.
Why this works; see,
http://cran.r-project.org/doc/manuals/r-release/R-intro.html#The-recycling-rule and the paragraph before
that.
Jan
Tammy Ma metal_lical...@live.com schreef:
HI,
I have the following question:
Vector
You could use the fact that scan reads the data rowwise, and the fact
that arrays are stored columnwise:
# generate a small example dataset
exampl - array(letters[1:25], dim=c(5,5))
write.table(exampl, file=example.dat, row.names=FALSE. col.names=FALSE,
sep=\t, quote=FALSE)
# and
I suspect it should be
my.data.copy - dbReadTable(con, week42)
(with con instead of tbs as first argument)
Jan
Tammy Ma metal_lical...@live.com schreef:
tbs-dbListTables(con)
tbs
[1] lowend time week30 week33 week39 week42
my.data.copy - dbReadTable(tbs, week42)
Error in function
I have a r-script (rook.R) that starts a Rook server. To present users
from having to start R and type in source(rook.R), I want to create
a bash script and bat file that starts R and sources the script.
However, to keep the Rook server running R should not close after
running the script
Or
ti - aggregate(dataframename[paste0(y, 1:3)],
by=dataframename[aggregationvar],
sum,na.rm=TRUE)
which gives you all results in one data.frame.
Jan
MacQueen, Don macque...@llnl.gov schreef:
Many ways. Here is one:
### supposing you have y1, y2, and y3 in your data
Did not see a simple way to make it faster. However, this is a piece of
code which can be made to run much faster in C. See below.
I don't know if you are familiar with running c-code from R. If not, the
official documentation is in the R Extensions manual. However, this is
not the most
It doesn't seem possible to index an ff-vector using a logical
ff-vector. You can use subset (also in ffbase) or first convert 'a' to
a normal logical vector:
library(ff)
library(ffbase)
data1 - as.ffdf(data.frame(a = letters[1:10], b=1:10))
data2 - as.ffdf(data.frame(a =
I come up with:
runs - function(numbers) {
tmp - diff(c(0, which(diff(numbers) = 0), length(numbers)))
split(numbers, rep(seq_along(tmp), tmp))
}
Can't say it's elegant, but it seems to work
runs(c(1:3, 1:4))
$`1`
[1] 1 2 3
$`2`
[1] 1 2 3 4
runs(c(1,1,1))
$`1`
[1] 1
$`2`
[1] 1
Having had a quick look at the source code for read.table.ffdf, I
suspect that using 'NULL' in the colClasses argument is not allowed.
Could you try to see if you can use read.table.ffdf with specifying
the colClasses for all columns (thereby reading in all columns in the
file)? If that
Looking at the source code for read.table.ffdf what seems to happen is
that when reading the first block of data by read.table (standard 1000
lines) the specified colClasses are used. In subsequent calls the
types of the columns of the ffdf object are used as colClasses. In
your case the
You probably have a character (which is converted to factor) or factor
column with a large number of distinct values. All the levels of a
factor are stored in memory in ff.
Jan
threshold r.kozar...@gmail.com schreef:
*..plus I get the following message after reading the whole set (all
system elapsed
0.700 0.000 0.702
Jan
Johan Henriksson maho...@areta.org schreef:
On Thu, Jul 19, 2012 at 5:02 PM, Jan van der Laan rh...@eoos.dds.nl wrote:
Johan,
Your 'list' and 'array doubling' code can be written much more efficient.
The following function is faster than your g
Johan,
Your 'list' and 'array doubling' code can be written much more efficient.
The following function is faster than your g and easier to read:
g2 - function(dotot) {
v - list()
for (i in seq_len(dotot)) {
v[[i]] - FALSE
}
}
In the following line in you array doubling function
On 07/19/2012 05:50 PM, Hadley Wickham wrote:
On Thu, Jul 19, 2012 at 8:02 AM, Jan van der Laan rh...@eoos.dds.nl wrote:
The following function is faster than your g and easier to read:
g2 - function(dotot) {
v - list()
for (i in seq_len(dotot)) {
v[[i]] - FALSE
}
}
Except
, 2012 at 8:02 AM, Jan van der Laan rh...@eoos.dds.nl wrote:
Johan,
Your 'list' and 'array doubling' code can be written much more efficient.
The following function is faster than your g and easier to read:
g2 - function(dotot) {
v - list()
for (i in seq_len(dotot)) {
v[[i]] - FALSE
to do it anyway.
v - as.list(rep(FALSE,dotot))
is way faster.
-- Bert
On Thu, Jul 19, 2012 at 8:50 AM, Hadley Wickham had...@rice.edu wrote:
On Thu, Jul 19, 2012 at 8:02 AM, Jan van der Laan rh...@eoos.dds.nl wrote:
Johan,
Your 'list' and 'array doubling' code can be written much more efficient
I don't know if any R-packages exist that can do this, but you could
install imagemagick (http://www.imagemagick.org), which provides
command line tools for image manipulation and conversion, and call
these from R using system. Something like:
system(convert yourimage.ppm yourimage.bmp)
using reshape:
library(reshape)
m - melt(my.df, id.var=pathway, na.rm=T)
cast(m, pathway~variable, sum, fill=NA)
Jan
On 05/07/2012 12:30 PM, Karl Brand wrote:
Dimitris, Petra,
Thank you! aggregate() is my lesson for today, not melt() | cast()
Really appreciate the super fast help,
Karl
Perhaps you could contact the persons that supplied/created the file and
ask them what the format of the file exactly is. That is probably the
safest thing to do.
If you are sure that the lines containing only whitespace are
meaningless, then you could alter the previous code to make a copy
read.table imports the company name GREAT FALLS GAS CO as four
separate columns. I think that needs to be one column. I can imagine
that further one in your file you will have another company name that
does not consist of four words which would cause the error you
observed. From your
read.delim calls read.table so any differences between the two are
caused by differences in the default values of some of the parameters.
Take a look at the help file ?read.table
read.table uses white space as separator; read.delim tabs
read.table uses and ' as quotes; read.delim just
or
identical(vec1, vec2) identical(vec2, vec3)
Jan
Petr Savicky savi...@cs.cas.cz schreef:
On Fri, May 04, 2012 at 12:53:12AM -0700, aaurouss wrote:
Hello,
I'm writing a piece of code where I need to compare multiple same length
vectors.
I've gone through the basic functions like
OK, not all, but most lines have the same length. Perhaps you could
write the lines with a different line size to a separate file to have
a closer look at those lines. Modifying the previous code (again not
tested):
con - file(dataset.txt, rt)
out - file(strangelines.txt, wt)
# skip
What you could try to do is skip the first 5 lines. After that the file
seems to be 'normal'. With read.table.ffdf you could try something like
# open a connection to the file
con - file('yourfile', 'rt')
# skip first 5 lines
tmp - readLines(con, n=5)
# read the remainder using
Your question is not completely clear. read.csv.ffdf automatically
reads in the data in chunks. You don´t have to do anything for that. You
can specify the size of the chunks using the next.rows option.
Jan
On 03/24/2012 09:29 PM, Mav wrote:
Hello!
A question about reading large CSV
The 'normal' way of doing that with ff is to first convert your csv
file completely to a
ffdf object (which stores its data on disk so shouldn't give any
memory problems). You
can then use the chunk routine (see ?chunk) to divide your data in the
required chunks.
Untested so may contain
Marianna,
You can use merge for that (or match). Using merge:
MyData - data.frame(
V1=c(red-j, red-j, red-j, red-j, red-j, red-j),
V4=c(10.5032, 9.3749, 10.2167, 10.8200, 9.2831, 8.2838),
redNew=c(appearance blood-n, appearance ground-n, appearance
sea-n, appearance sky-n, area
You could also have a look at the LaF package which is written to
handle large text files:
http://cran.r-project.org/web/packages/LaF/index.html
Under the vignettes you'll find a manual.
Note: LaF does not help you to fit 9GB of data in 4GB of memory, but
it could help you reading your
Step by step:
1. Create a new document in Open/LibreOffice
2. Copy/paste the following text into the document (as an example)
helloworld=
cat(Hello, world)
@
2. Save the file (e.g. hello.odt)
3. Start R (if not already) shouldn't matter if its plain R/RStudio
4. Change working directory to
The suggestion below gives you week numbers with week 1 being the week
containing the first monday of the year and weeks going from monday to
sunday. There are other conventions. The ISO convention is that week 1
is the first week containing at least 4 days in the new year (week 1
of
I don't know if this completely solves your problem, but here are some
arguments to read.table/read.delim you might try:
row.names=FALSE
fill=TRUE
The details section also suggests using the colClasses argument as the
number of columns is determined from the first 5 rows which may not be
Devarayalu,
This is FAQ 7.22:
http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-do-lattice_002ftrellis-graphics-not-work_003f
use print(qplot())
Regards,
Jan
Sri krishna Devarayalu Balanagu balanagudevaray...@gvkbio.com schreef:
Hi All,
Can you please help me, why this code in not
[i == Orange1$REFID, ]
pdf('PGA.pdf')
print(qplot(TIME1, BASCHGA, data=Orange2, geom= c(line), colour= ACTTRT))
dev.off()
}
Regards
Devarayalu
-Original Message-
From: Jan van der Laan [mailto:rh...@eoos.dds.nl]
Sent: Thursday, January 19, 2012 4:25 PM
To: Sri krishna Devarayalu
Devarayalu
-Original Message-
From: Jan van der Laan [mailto:rh...@eoos.dds.nl]
Sent: Thursday, January 19, 2012 4:25 PM
To: Sri krishna Devarayalu Balanagu
Cc: r-help@r-project.org
Subject: Re: [R] Not generating line chart
Devarayalu,
This is FAQ 7.22:
http://cran.r-project.org/doc
Emma,
If, as you say, each unit is the same you can just repeat the units to
obtain the required number of units. For example,
unit_size - 10
n_units - 10
unit_id - rep(1:n_units, each=unit_size)
pid - rep(1:unit_size, n_units)
senior - ifelse(pid = 2, 1, 0)
pop -
will be useful in the future.
Thanks again!
Emma
- Original Message -
From: Jan van der Laan rh...@eoos.dds.nl
To: r-help@r-project.org r-help@r-project.org
Cc: Emma Thomas thomas...@yahoo.com
Sent: Wednesday, December 14, 2011 6:18 AM
Subject: Re: [R] Generating input population
What I did in the past (not with R scripts) is to start my jobs using
at (start the job at a specified time e.g. now) or batch (start the
job when the cpu drops below ?%)
at now R CMD BATCH yourscript.R
or
batch R CMD BATCH yourscript.R
something like that, you'll have to look at the man
Raphael,
This looks like fixed width format which you can read with read.fwf.
In fixed width format the columns are not separated by white space (or
other characters), but are identified by the positition in the file.
So in your file, for example the first field looks to contained in the
I assume you use a command window to build your packages. One possible
solution might be to leave out the path variables set by Rtools from
your global path and to create a separate shortcut to cmd for building
r-packages where you set your path as needed by R CMD build/check
Something like
Yet another solution. This time using the LaF package:
library(LaF)
d-c(1,4,7,8)
P1 - laf_open_csv(M1.csv, column_types=rep(double, 10), skip=1)
P2 - laf_open_csv(M2.csv, column_types=rep(double, 10), skip=1)
for (i in d) {
M-data.frame(P1[, i],P2[, i])
}
(The skip=1 is needed as laf_open_csv
The LaF package provides methods for fast access to large ASCII files.
Currently the following file formats are supported:
* comma separated format (csv) and other separated formats and
* fixed width format.
It is assumed that the files are too large to fit into memory, although
the package
George,
Perhaps the site of the RISQ project (Representativity indicators for
Survey Quality) might be of use: http://www.risq-project.eu/ . They
also provide R-code to calculate their indicators.
HTH,
Jan
Quoting ghe...@mathnmaps.com:
An organization has asked me to comment on the
plyr isn't necessary in this case. You can use the following:
cols - sapply(df, is.numeric)
df[, cols] - pct(df[,cols])
round (and therefore pct) accepts a data.frame and returns a
data.frame with the same dimensions. If that hadn't been the case
colwise might have been of help:
An obvious reason might be that your second argument should be a
pointer to int.
As others have mentioned, you might want to have a look at Rccp and/or
inline. The documentation is good and I find it much easier to work
with.
For example, your example could be written as:
library(Rcpp)
Quoting Uwe Ligges lig...@statistik.tu-dortmund.de:
I don't agree that it's overkill -- you get to sidestep the whole `R
CMD SHLIB ...` and `dyn.load` dance this way while you experiment with
C(++) code 'live using the inline package.
You need two additional packages now where you have to
You can with the routines in the memisc library. You can open a file
using spss.system.file and then import a subset using subset. Look in
the help pages of spss.system.file for examples.
HTH
Jan
On 09/25/2011 11:56 PM, sassorauk wrote:
Is it possible to import only certain variables from
it in xlswrite in matlab, where you can
even specify the cell number from where you want to write.
-Ashish
*From:*R. Michael Weylandt [mailto:michael.weyla...@gmail.com]
*Sent:* Thursday, September 22, 2011 12:03 AM
*To:* Jan van der Laan
*Cc:* r-help@r-project.org; ashish.ku...@esteeadvisors.com
Michael,
You example doesn't seem to work. Append isn't passed on to the
write.table call. You will need to add a
Call$append- append
to the function. And even then there will be a problem with the
headers that are repeated when appending.
An easier solution is to use write.table
Michael,
You example doesn't seem to work. Append isn't passed on to the
write.table call. You
will need to add a
Call$append- append
to the function. And even then there will be a problem with the
headers that are repeated
when appending.
An easier solution is to use write.table
Michael,
You example doesn't seem to work. Append isn't passed on to the
write.table call. You will need to add a
Call$append- append
to the function. And even then there will be a problem with the
headers that are repeated when appending.
An easier solution is to use write.table
Laura,
Perhaps the following example helps:
nbstr - 100
result - numeric(nbstr)
for (i in seq_len(nbstr)) {
# set the default value for when the current bootstrap fails
result[i] - NA
try({
# estimate your cox model here
if (runif(1) 0.1) stop(ERROR)
result[i] - i
},
can tell). Could you perhaps just tell me how I
should combine the output of multiple odf* calls inside a function?
Thanks again.
Jan
Quoting Max Kuhn mxk...@gmail.com:
formatting.odf, page 7. The results are in formattingOut.odt
On Thu, Sep 15, 2011 at 2:44 PM, Jan van der Laan rh
What is the correct way to combine multiple calls to odfCat,
odfItemize, odfTable etc. inside a function?
As an example lets say I have a function that needs to write two
paragraphs of text and a list to the resulting odf-document (the real
function has much more complex logic, but I
Max,
Thank you for your answer. I have had another look at the examples (I
already had before mailing the list), but could find the example you
mention. Could you perhaps tell me which example I should have a look at?
Regards,
Jan
On 09/15/2011 04:47 PM, Max Kuhn wrote:
There are
Alexandra,
Have a look at add1 and drop1.
Regards,
Jan
On 06/23/2011 07:32 PM, Alexandra Thorn wrote:
Here's a more general question following up on the specific question I
asked earlier:
Can anybody recommend an R command other than mle.aic() (from the wle
package) that will give back a
The memisc package also offers functionality for documenting data.
Jan
On 06/22/2011 04:57 PM, Robert Lundqvist wrote:
Every now and then I realize that my attempts to document what all dataframes
consist of are unsufficient. So far, I have been writing notes in an external
file. Are there
Santosh, Ivan,
This is also what I was looking for. Thanks. Looking at the source of
dataFrame.default is seems that it uses the same approach as I did:
first create a list then a data.frame from that list. I think I'll stick
with the code I already had as I don't want another dependency
Jan van der Laan rh...@eoos.dds.nl:
I use the following code to create two data.frames d1 and d2 from a list:
types - c(integer, character, double)
nlines - 10
d1 - as.data.frame(lapply(types, do.call, list(nlines)),
stringsAsFactor=FALSE)
l2 - lapply(types, do.call, list(nlines))
d2
I use the following code to create two data.frames d1 and d2 from a list:
types - c(integer, character, double)
nlines - 10
d1 - as.data.frame(lapply(types, do.call, list(nlines)),
stringsAsFactor=FALSE)
l2 - lapply(types, do.call, list(nlines))
d2 - as.data.frame(l2,
Thanks. I also noticed myself minutes after sending my message to the
list. My 'please ignore my question it was just a stupid typo' message
was sent with the wrong account and is now awaiting moderation.
However, my other question still stands: what is the
preferred/fastest/simplest way to
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