Yes this is the solution to my problem.
I think there is a small typo in old.index, instead of:
k=rep(1:nks, rep(njs*nks, nks)))
it should be:
k=rep(1:nks, rep(nis*njs, nks)))
That is, the k index should be replicated by nis*njs,, not nks*njs.
Many thanks
Asher
On Fri, Dec
Respected Sir
I tried the strucchange
My data is attached. However I tried the attached commands (last
save.txt) to perform Bai Perron 2003... I t worked well but in the end
it is giving warning that overlapping confidence interval... I am not
sure how to proceed... Please Help Me
Thanking You
Hi all!
I was wondering if anyone could help me.
I'd like to calculate prevalence and confidence interval for a clustered
sample (design effect estimated at 2).
In the old version of epiR the epi.conf function allowed for the design
effect argument, but in the new version this doesn't seem
Assume that we collect below data : -
subjects = 20 males + 20 females, every single individual is independence,
and difference
events = 1, 2, 3... n
covariates = 4 blood types A, B, AB, O
http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg
λm = hazards rates for male
λn = hazards
# plyr
plyr is a set of tools for a common set of problems: you need to
__split__ up a big data structure into homogeneous pieces, __apply__ a
function to each piece and then __combine__ all the results back
together. For example, you might want to:
* fit the same model each patient subsets of
# testthat
Testing your code is normally painful and boring. `testthat` tries to
make testing as fun as possible, so that you get a visceral
satisfaction from writing tests. Testing should be fun, not a drag, so
you do it all the time. To make that happen, `testthat`:
* Provides functions that
I've looked in Deepayan Sarkar's book without finding how to specify the
order of conditioning panels in a trellis plot. Here's the issue I'm trying
to resolve:
Sampling locations along a stream channel are not sequentially numbered.
For example, the panel order for one plot (lower left to
On 11-12-30 9:56 AM, Rich Shepard wrote:
I've looked in Deepayan Sarkar's book without finding how to specify the
order of conditioning panels in a trellis plot. Here's the issue I'm trying
to resolve:
Sampling locations along a stream channel are not sequentially numbered.
For example,
This makes sense. Guess I should have put a pencil to it.
Further investigation revealed that it is indeed a possibility
that the relation between x and y is nonlinear:
ax+bx^2+c
where a, b and c are to be determined. My question is
how to code this in my simulated data. I could do
Sarah and elisacarli21
principal in the psych package will do principal components of a correlation or
covariance matrix.
ex:
library(psych)
principal(Thurstone,3,rotate=none) #First three principal components of the
Thurstone correlation matrix
#compare with eigen
e - eigen(Thurstone)
On Fri, 30 Dec 2011, Duncan Murdoch wrote:
You don't describe the variable holding those labels. If it is a factor with
the levels in the desired order then it should display properly.
Duncan,
Apologies; I did not know what supporting information to provide.
It's a factor in the data
Great lists, I always find them useful, thank you to
everyone who contributes to them.
My question is regarding non-integer values from some data I
collected on parrots when using the poisson GLM. I observed the parrots on a
daily basis to see if they were affected by tourist presence. My key
Dear all,
I would like to create a vertically stacked area chart in R. The data are
presented in the attached text file.
I would like to see the trend in values for the different groups with
sediment depth (that's why I would like to create a vertically stacked
chart; normally sed_depth should
there is also colwise in the plyr package.
library(plyr)
colwise(class)(data6)
v13 v14 v15 f4 v16
1 integer numeric character factor logical
Justin
On Thu, Dec 29, 2011 at 4:47 PM, Jean V Adams jvad...@usgs.gov wrote:
Dan Abner wrote on 12/29/2011 06:13:11 PM:
Thanks, Marc. That did it. I thought yum provides would have been
smart enough to tell me about any devel package, but I guess I have
something left to learn there. Thanks.
--Sam
-Original Message-
From: Marc Schwartz [mailto:marc_schwa...@me.com]
Sent: Thursday, December 29, 2011
Happy holidays all!
I know it's very subjective to determine whether some data is outlier or
not...
But are there reasonally good and realistic methods of identifying outliers
in R?
Thanks a lot!
[[alternative HTML version deleted]]
__
Hi Michael,
I'm afraid this is one of those cases where the short answer is No
and the long answer is, No.
If you are working with a data set stored in a data frame, something like:
sapply(mtcars, function(x) if (is.numeric(x)) range(x, na.rm = TRUE)
else c(NA, NA))
should give you the range
Hi Sam,
It has been almost 3 years since I last used Linux (Fedora), but if memory
serves using, as root, either:
yum provides readline.h
or perhaps
yum search readline
might be possible ways of getting the names of the relevant RPMs in the repos.
You might have to tweak the 'readline'
On Fri, Dec 30, 2011 at 9:03 AM, Michael comtech@gmail.com wrote:
Happy holidays all!
I know it's very subjective to determine whether some data is outlier or
not...
But are there reasonally good and realistic methods of identifying outliers
in R?
What kind of data do you have? For
But be careful because class is a character vector (not necessarily a
character vector of length 1)
On Fri, Dec 30, 2011 at 10:21 AM, Justin Haynes jto...@gmail.com wrote:
there is also colwise in the plyr package.
library(plyr)
colwise(class)(data6)
v13 v14 v15 f4 v16
Hi,
Use offset variables if count occurrences of an event and you want to
model the
observation time.
glm(count ~ predictors + offset(log(observation_time)), family=poisson)
If you want to compare durations, look at library(survival), ?coxph
If tnoise_sqrt is the square root of tourist noise,
Thanks a lot,
it was very helpful, I did something like that:
EV - ifelse(sprd mediaSDP sprd_d = mediaSDP_d, -1, 0)
SV - ifelse(sprd media sprd_d = media_d, -17, 0)
EC - ifelse(sprd mediaSDN sprd_d = mediaSDN_d,1,0)
SC - ifelse(sprd media sprd_d = media_d,17,0)
in order to have 4
Dear R addicts,
I´m trying to merge two vectors like these and I don´t want to use loops.
EV
[1] 9 16 19 21 67 71 82 87 110 112 141 156 201 211 248 264 272 298
331
[20] 341 347 361 373 468 490 493 504 508 806 830 833 883 885 897 953 955
SV
[1] 3 21 28 33 229 379 396 532 535 541
Hi Bert,
Thank you for the idea - but this will only move the text to different
sides of the text...
Right-to-left languages are a known issue in open source projects
(libreoffice, WordPress, etc...)
Any advice on who should I contact regarding this?
(Not that it is urgent for me, but long term
Is there a concise list somewhere of the Sweave options for a code
chunk? By that I mean
chunk-name, options..
where the options are fig=T/F, echo=T/F, etc. I keep forgetting parts of
the etc.
Terry T.
__
R-help@r-project.org mailing list
On Dec 30, 2011, at 12:23 PM, Terry Therneau wrote:
Is there a concise list somewhere of the Sweave options for a code
chunk? By that I mean
chunk-name, options..
where the options are fig=T/F, echo=T/F, etc. I keep forgetting parts of
the etc.
Terry T.
Terry,
See ?RweaveLatex which
Does the following do what you want? You didn't
describe the rule and the example output was truncated:
EV - c(
9,16,19,21,67,71,82,87,110,112,141,156,201,211,248,264,272,298,331,
341,347,361,373,468,490,493,504,508,806,830,833,883,885,897,953,955)
SV - c(
On 30 December 2011 10:21, Michael comtech@gmail.com wrote:
But are there reasonably good and realistic methods of identifying
outliers/errornous quotes in tick data in R?
Check out the OutlierD package at
http://www.bioconductor.org/packages/release/bioc/html/OutlierD.html.
--
Sent from
On 30/12/2011 1:29 PM, Marc Schwartz wrote:
On Dec 30, 2011, at 12:23 PM, Terry Therneau wrote:
Is there a concise list somewhere of the Sweave options for a code
chunk? By that I mean
chunk-name, options..
where the options are fig=T/F, echo=T/F, etc. I keep forgetting parts of
the etc.
Matthias Gondan matthias-gondan at gmx.de writes:
Hi,
Use offset variables if count occurrences of an event and you want to
model the
observation time.
glm(count ~ predictors + offset(log(observation_time)), family=poisson)
If you want to compare durations, look at
Hi Ben,
Thanks for clarifying this, I used a misleading word, model the
observation time
sounds as if observation time were the dependent variable - which it is
not, of course,
instead, in the scenario described, the parrot counts are modeled.
Best wishes,
Matthias
Am 30.12.2011 20:50,
Version 2.2 of coxme has been posted to CRAN, Windows versions and
mirrors should appear in due course. This is a major update with three
features of note:
1. A non-upwardly compatable change:
Extractor functions: beta= fixed effects, b=random effects
nlmelme4 coxme 2.2
Hi all,
I am new to R, and am trying to do feature selection on my text data that
has about 30k observations and about 15k features. I am interested in using
Chi-Sqaured and Mutual Information based feature selection. I tried using
FSelector package but found it too slow for my purposes.
Are
elpape wrote on 12/30/2011 09:04:53 AM:
Dear all,
I would like to create a vertically stacked area chart in R. The data
are
presented in the attached text file.
I would like to see the trend in values for the different groups with
sediment depth (that's why I would like to create a
On Fri, 30 Dec 2011, Ayanendu Sanyal wrote:
Respected Sir
I tried the strucchange
My data is attached. However I tried the attached commands (last
save.txt) to perform Bai Perron 2003... I t worked well but in the end
it is giving warning that overlapping confidence interval... I am not
sure
Asher Meir asher.m...@gmail.com wrote on 12/30/2011 03:45:48 AM:
Yes this is the solution to my problem.
I think there is a small typo in old.index, instead of:
k=rep(1:nks, rep(njs*nks, nks)))
it should be:
k=rep(1:nks, rep(nis*njs, nks)))
That is, the k index
Ellen,
Is this what you are looking for?
library(HH)
sed - read.table(textConnection(
sed_depth FT value
0-1 1A 35.16591742
1-2 1A 36.21447839
2-3 1A 19.09701388
3-4 1A 35.86953345
4-10 1A 30.04823571
0-1 1B 15.02829003
1-2 1B 19.15637318
2-3 1B 12.45405429
3-4 1B 25.23130364
4-10 1B
I have fit an accelerated failure time model using coxph, and have what seems
to be a simple question that I can't figure out.
Given a vector of predictor values X, the survival time S[t|X] is the
probability the entity will survive longer than some time t. Now, how do I
calculate this for a
Hi all,
I am trying to analyze a before-after control-impact study. I'm looking for a
reference guide to help as I am pretty new to R. If anyone has any suggestions
on a guide I would appreciate it. Thanks.
Nate Fronk
[[alternative HTML version deleted]]
On 30/12/11 17:03, Michael wrote:
Happy holidays all!
I know it's very subjective to determine whether some data is outlier or
not...
But are there reasonally good and realistic methods of identifying outliers
in R?
Thanks a lot!
Ignoring the moral questions for a moment (totaly depends on
Le vendredi 30 décembre 2011 à 12:46 -0800, thebennjammin a écrit :
I have fit an accelerated failure time model using coxph, and have what seems
to be a simple question that I can't figure out.
Please specify the exact package, functions and actual code you use.
coxph(), as the name says,
On Dec 30, 2011, at 6:19 PM, Milan Bouchet-Valat wrote:
Le vendredi 30 décembre 2011 à 12:46 -0800, thebennjammin a écrit :
I have fit an accelerated failure time model using coxph, and have
what seems
to be a simple question that I can't figure out.
Please specify the exact package,
Hi,
I have met a tough problem when using PHP to call R to generate some plots.
I tested it okay in my personal computer with WinXP. But when I was trying
to update to my server (Win2003 server), I found it did not work. Below is
the details:
1 r-code file (E:/mycode.r):
--
Dear all,
I would like to create a vertically stacked area chart in R. The data are
presented in the attached text file.
I would like to see the trend in values for the different groups with
sediment depth (that's why I would like to create a vertically stacked
chart; normally sed_depth
Hi, R newb here. I've coded a function that inputs N dimensional array(s) [or
class=numeric if it's dim=1] of coefficients and tstats, where
dim(coef_matrix)=dim(tstat_matrix), it will then output a same dimension
matrix of coefficients pasted to tstats in brackets pasted to significance
stars.
Okay it's working perfectly now. I restarted R and it worked on my first 5
goes.
Can anybody shed light on how this kind of thing can happen?
-
Isaac
Research Assistant
Quantitative Finance Faculty, UTS
--
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