re always twist the original data and spits only
> descriptive results.
>
> All your results are quite consistent with the available values as they are
> close to 1, so for me, each approach works.
>
> Thank you again.
>
> Best regards.
> Petr
>
> > -----Origina
David Winsemius
> Sent: Sunday, March 7, 2021 1:33 AM
> To: Abby Spurdle ; PIKAL Petr
>
> Cc: r-help@r-project.org
> Subject: Re: [R] quantile from quantile table calculation without original
> data
>
>
> On 3/6/21 1:02 AM, Abby Spurdle wrote:
> > I came up w
I am aware of that... I have my own functions for this purpose that use
splinefun. But if you are trying to also do other aspects of probability
distribution calculations, it looked like using fBasics would be easier than
re-inventing the wheel. I could be wrong, though, since I haven't used fBa
> Jeff Newmiller
> on Fri, 05 Mar 2021 10:09:41 -0800 writes:
> Your example could probably be resolved with approx. If
> you want a more robust solution, it looks like the fBasics
> package can do spline interpolation.
base R's spline package does spline interpolation
On 3/6/21 1:02 AM, Abby Spurdle wrote:
I came up with a solution.
But not necessarily the best solution.
I used a spline to approximate the quantile function.
Then use that to generate a large sample.
(I don't see any need for the sample to be random, as such).
Then compute the sample mean and
I came up with a solution.
But not necessarily the best solution.
I used a spline to approximate the quantile function.
Then use that to generate a large sample.
(I don't see any need for the sample to be random, as such).
Then compute the sample mean and sd, on a log scale.
Finally, plug everythi
I'm sorry.
I misread your example, this morning.
(I didn't read the code after the line that calls plot).
After looking at this problem again, interpolation doesn't apply, and
extrapolation would be a last resort.
If you can assume your data comes from a particular type of
distribution, such as a
I note three problems with your data:
(1) The name "percent" is misleading, perhaps you want "probability"?
(2) There are straight (or near-straight) regions, each of which, is
equally (or near-equally) spaced, which is not what I would expect in
problems involving "quantiles".
(3) Your plot (appro
On 3/5/21 1:14 AM, PIKAL Petr wrote:
Dear all
I have table of quantiles, probably from lognormal distribution
dput(temp)
temp <- structure(list(size = c(1.6, 0.9466, 0.8062, 0.6477, 0.5069,
0.3781, 0.3047, 0.2681, 0.1907), percent = c(0.01, 0.05, 0.1,
0.25, 0.5, 0.75, 0.9, 0.95, 0.99)), .Na
Your example could probably be resolved with approx. If you want a more robust
solution, it looks like the fBasics package can do spline interpolation. You
may want to spline on the log of your size variable and use exp on the output
if you want to avoid negative results.
On March 5, 2021 1:14
Dear all
I have table of quantiles, probably from lognormal distribution
dput(temp)
temp <- structure(list(size = c(1.6, 0.9466, 0.8062, 0.6477, 0.5069,
0.3781, 0.3047, 0.2681, 0.1907), percent = c(0.01, 0.05, 0.1,
0.25, 0.5, 0.75, 0.9, 0.95, 0.99)), .Names = c("size", "percent"
), row.names = c
My R package, "probhat", provides plots of bivariate PDFs and bivariate
CDFs, using kernel smoothing.
Note that there is no bivariate quantile function, as such.
Here's the vignette:
https://cran.r-project.org/web/packages/probhat/vignettes/probhat.pdf
This contains examples.
Note that I'm not s
Thanks Abs - I was able to get the plot I needed with the hdrcde package but I
will check out your package as well.
I continue to be impressed with the power Of R and the various packages
available.
Thanks again
Bernard
Sent from my iPhone so please excuse the spelling!"
> On Mar 29, 2019, a
John, I have attached a pdf of the plot. Hopefully you can read this.
If I understand correctly, this plot is basically the 2-D version of the 1-D
quantile plot.
Thanks
Bernard McGarvey
Director, Fort Myers Beach Lions Foundation, Inc.
Retired (Lilly Engineering Fellow).
> On March 27, 20
The figure did not get through. Perhaps try a pdf?
On Tue, 26 Mar 2019 at 13:41, Bernard McGarvey
wrote:
>
> I want to see if I can reproduce the plot below in R. If I understand it
> correctly, i takes my bivariate data and creates quantile density contours.
> My interpretation of these conto
I want to see if I can reproduce the plot below in R. If I understand it
correctly, i takes my bivariate data and creates quantile density contours. My
interpretation of these contours is that they enclose a certain % of the total
data. I am using the bkde2D function in library KernSmooth which
Hi,
I would like to fit the following model with quantile regression:
y ~ alpha + beta
where both alpha and beta are factors. The conceptual model I have in my
head is that alpha is a constant set of values, that should be independent
of the quantile, tau and that all of the variability arises d
Greetings R Community,
I am running quantile regressions using quantreg in R. I also plot the
residuals in a QQplot which indicate fat tails. I would like to try using
Student distribution, but I do not know if the R software allows it for my task
in hand.
In my opinion it is very likely that
: R-help@r-project.org
Subject: Re: [R] quantile regression: warning message
see the output from the quantreg FAQ:
FAQ()
especially point 2.
url:www.econ.uiuc.edu/~rogerRoger Koenker
emailrkoen...@uiuc.eduDepartment of Economics
vox: 217-333-4558
see the output from the quantreg FAQ:
FAQ()
especially point 2.
url:www.econ.uiuc.edu/~rogerRoger Koenker
emailrkoen...@uiuc.eduDepartment of Economics
vox: 217-333-4558University of Illinois
fax: 217-244-6678Urba
Greetings R Community,
I am trying to run a quantile regression using the quantreg package. My code
looks as follows:
RegressionUtilitiesUK<-rq(ReturnUtilities~yield.spread.change+ReturnFTSE,
tau=0.01,data=State_variables_UK_calm)
Unfortunately, the summary() function returns the results but als
me implies 0 expenditure, then all (quantile)
Engel curves pass through the origin and
one might want to impose this. On the other hand maybe not...
> From: Roger Koenker
> Sent: 06-10-2015 07:09 PM
> To: Lorenz, David
> Cc: r-help@r-project.org
> Subject: Re: [R] Quantile R
e that.
> >
> >
> >
> >> Date: Mon, 5 Oct 2015 21:14:04 +0530
> >> From: Preetam Pal
> >> To: stephen sefick
> >> Cc: "r-help@r-project.org"
> >> Subject: Re: [R] Quantile Regression without intercept
> >> Mess
To wit:
> y <- rnorm(100, 10)
> x <- 1:100
> sum(resid(lm(y~x)))
[1] 1.047773e-15
> sum(resid(lm(y~x-1)))
[1] 243.0583
and replicating this should convince you that the mean residual really is not
zero in the severely misspecified model with no intercept. (This has to do with
the fact that resi
ate: Mon, 5 Oct 2015 21:14:04 +0530
> >> From: Preetam Pal
> >> To: stephen sefick
> >> Cc: "r-help@r-project.org"
> >> Subject: Re: [R] Quantile Regression without intercept
> >> Message-ID: <56129a41.025f440a.b1cf4.f...@mx.google.com>
> &g
>> Date: Mon, 5 Oct 2015 21:14:04 +0530
> >> From: Preetam Pal
> >> To: stephen sefick
> >> Cc: "r-help@r-project.org"
> >> Subject: Re: [R] Quantile Regression without intercept
> >> Message-ID: <56129a41.025f440a.b1cf4.f...@mx.goo
etam Pal
>> To: stephen sefick
>> Cc: "r-help@r-project.org"
>> Subject: Re: [R] Quantile Regression without intercept
>> Message-ID: <56129a41.025f440a.b1cf4.f...@mx.google.com>
>> Content-Type: text/plain; charset="UTF-8"
>>
>> Ye
:04 +0530
> From: Preetam Pal
> To: stephen sefick
> Cc: "r-help@r-project.org"
> Subject: Re: [R] Quantile Regression without intercept
> Message-ID: <56129a41.025f440a.b1cf4.f...@mx.google.com>
> Content-Type: text/plain; charset="UTF-8"
>
> Ye
Yes..it works. Thanks 😃
-Original Message-
From: "stephen sefick"
Sent: 05-10-2015 09:01 PM
To: "Preetam Pal"
Cc: "r-help@r-project.org"
Subject: Re: [R] Quantile Regression without intercept
I have never used this, but does the formula inter
I have never used this, but does the formula interface work like lm? Y~X-1?
On Mon, Oct 5, 2015 at 10:27 AM, Preetam Pal wrote:
> Hi guys,
>
> Can you instruct me please how to run quantile regression without the
> intercept term? I only know about the rq function under quantreg package,
> but i
as for lm() or any other linear model fitting….
rq( y ~ x - 1, … )
url:www.econ.uiuc.edu/~rogerRoger Koenker
emailrkoen...@uiuc.eduDepartment of Economics
vox: 217-333-4558University of Illinois
fax: 217-244-6678U
Hi guys,
Can you instruct me please how to run quantile regression without the intercept
term? I only know about the rq function under quantreg package, but it
automatically uses an intercept model. Icant change that, it seems.
I have numeric data on Y variable (Gdp) and 2 X variables (Hpa and
Hi all,I would like to know how to predict a new y value and its confidence
interval for the prediction given a new observation x when using a linear(or
non-linear) quantile regression model.How it is possible to transform the
confidence prediction in to an interval prediction?
Is it correct to
Well, presumably you have the pmf and can create a matrix of the form:
(where mypmf is your pmf)
x <- seq_len(1000) ## or whatever your discrete support sorted in
increasing order
## for individual quantile q:
max(x[cumsum(mypmf(x)) <= q] )
## This probably could be vectorized for a vector of q
I thank all for your reply. My question was not well formulated.
I will do it again:
Suppose that the random variable X is discrete with probability mass function
(pmf) F (binomial, poisson, ) not necessarily available in R.
Is there a general method to get the quantiles (as qbinom, qpois, ...
On Jun 25, 2015, at 7:26 AM, L... L... wrote:
> Dear all, is there a general method for calculating the quantile of a
> discrete random variable? If yes, is there a R function to do this?
The `quantile` function would seem to be the first place to go. It may depend
on the object-type of your r
sity
College Station, TX 77840-4352
-Original Message-
From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of L... L...
Sent: Thursday, June 25, 2015 9:27 AM
To: r-help@r-project.org
Subject: [R] quantile of a discrete random variable
Dear all, is there a general method for calcula
Dear all, is there a general method for calculating the quantile of a discrete
random variable? If yes, is there a R function to do this?
Best regards
Marcelo Lamack
[[alternative HTML version deleted]]
_
loop therefore does not really help...
-Original Message-
From: Roger Koenker [mailto:rkoen...@illinois.edu]
Sent: Donnerstag, 11. Juni 2015 15:33
To: Waltl, Sofie (sofie.wa...@uni-graz.at)
Cc: r-help@r-project.org
Subject: Re: [R] Quantile regression model with nonparametric effec
The main effect trend seems rather dangerous, why not just estimate the f’s in
a loop?
url:www.econ.uiuc.edu/~rogerRoger Koenker
emailrkoen...@uiuc.eduDepartment of Economics
vox: 217-333-4558University of Illinois
fax: 217-244-6678
Dear all,
I would like to estimate a quantile regression model including a bivariate
nonparametric term which should be interacted with a dummy variable, i.e.,
log p ~ year + f(a,b):year.
I tried to use Roger Koenker's quantreg package and the functions rqss and qss
but it turns out that interac
Dear r Users,
I am new in r. I am trying to estimate regression quantiles in complex
surveys.I used these commands.
mydesign
<-svydesign(ids=~IDSCHOOL,strata=~IDSTRATE,data=TUNISIA,nest=TRUE,weights=~TOTWGT)
bootdesign <- as.svrepdesign(mydesign,type="auto",replicates=150)
fit+dictionary+in
On Sep 15, 2014, at 11:17 AM, Felix Dietrich wrote:
> Hi, I want to use the quantile function, the example shown under "help"
>
> x <- rnorm(1001)
> quantile(x <- rnorm(1001)) # Extremes & Quartiles by default
> quantile(x, probs = c(0.1, 0.5, 1, 2, 5, 10, 50, NA)/100)
>
> I get the following
Hi, I want to use the quantile function, the example shown under "help"
x <- rnorm(1001)
quantile(x <- rnorm(1001)) # Extremes & Quartiles by default
quantile(x, probs = c(0.1, 0.5, 1, 2, 5, 10, 50, NA)/100)
I get the following error:
Error in quantile(x, probs = c(0.1, 0.5, 1, 2, 5, 10, 50, NA)
Hi AK,
Thanks very much for the updated code.
My simulated results are even more consistent with observations after apply the
updated version of the code.
Cheers,
Atem.
On Wednesday, April 16, 2014 11:31 PM, Zilefac Elvis
wrote:
Hi AK,
Thanks very much.
Atem.
On Wednesday, April 16, 2014
Hi AK,
Thanks very much.
Atem.
On Wednesday, April 16, 2014 9:32 PM, arun wrote:
Hi,
Use this code after `lst2`.
lapply(seq_along(lst2), function(i) {
lstN <- lapply(lst2[[i]], function(x) {
datN <- as.data.frame(matrix(NA, nrow = 101, ncol = length(names1),
dimnames = list(NULL,
Hi,
Use this code after `lst2`.
lapply(seq_along(lst2), function(i) {
lstN <- lapply(lst2[[i]], function(x) {
datN <- as.data.frame(matrix(NA, nrow = 101, ncol = length(names1),
dimnames = list(NULL,
names1)))
x1 <- x[, -1]
qt <- numcolwise(function(y) quan
Hi AK,
Thanks very much. I worked great.
Many thanks.
Atem.
On Tuesday, April 15, 2014 9:20 AM, arun wrote:
Hi Atem,
May be this works.
### Q1: working directory: Observed #Only one file per Site. Assuming this is
the
### case for the full dataset, then I guess there is no need to average
di
Hi Atem,
May be this works.
### Q1: working directory: Observed #Only one file per Site. Assuming this is
the
### case for the full dataset, then I guess there is no need to average
dir.create("final")
lst1 <- split(list.files(pattern = ".csv"), gsub("\\_.*", "",
list.files(pattern = ".csv")))
Hi AK,
All codes for simulation files work great.
I will try the code for observations and let you know.
Thanks very much.
Atem.
On Tuesday, April 15, 2014 12:01 AM, arun wrote:
Yes,
my new solution ignores such cases.
On Monday, April 14, 2014 11:58 PM, Zilefac Elvis
wrote:
Hi AK,
Hi Atem,
I guess this is what you wanted.
###Q1:
###
###working directory: Observed
#Only one file per Site. Assuming this is the case for the full dataset, then
I guess there is no need to average
dir.create("final")
lst1 <- split(list.files(pattern = ".csv"), gsub("\\_.*", "",
list.files(
Hi AK,
Thanks very much.
I did send you another email with a larger Sample.zip file. The
Quantilecode.R which you initially developed for a smaller sample.zip did
not complete the task when I used it for a larger data set. Please check to
rectify the error message.
Hi,
Q1 solution already sent.
Regarding Q2, one of the files in the new Observed folder doesn't have any
data (just the Year column alone).
That may be the reason for the problem.
### Q1: working directory: Observed #Only one file per Site. Assuming this is
the
### case for the full data
Hi,
It is because of different dimensions of Simulation data within each Site.
Try:
dir.create("final")
lst1 <- split(list.files(pattern = ".csv"), gsub("\\_.*", "",
list.files(pattern = ".csv")))
sapply(lst1,length)
#G100 G101 G102 G103 G104 G105 G106 G107 G108 G109 G110 G111 G112 G113 G114
G
Hi AK,
I must admit that you did an excellent job.
Thanks very much.
My analysis is manageable now.
Regards,
Atem.
On Sunday, April 13, 2014 8:54 AM, arun wrote:
Hi,
I am formatting the codes using library(formatR). Hopefully, it will not be
mangled in the email.
dir.create("final")
lst1 <- s
Hi,
I am formatting the codes using library(formatR). Hopefully, it will not be
mangled in the email.
dir.create("final")
lst1 <- split(list.files(pattern = ".csv"), gsub("\\_.*", "",
list.files(pattern = ".csv")))
lst2 <- lapply(lst1, function(x1) lapply(x1, function(x2) { lines1 <-
readLine
Mike,
Do something like:
require(rms)
dd <- datadist(mydatarame); options(datadist='dd')
f <- Rq(y ~ rcs(age,4)*sex, tau=.5) # use rq function in quantreg
summary(f) # inter-quartile-range differences in medians of y (b/c tau=.5)
plot(Predict(f, age, sex)) # show age effect on median as a co
Or cast to vector:
> set.seed(28)
> x<- sample(1:40,20,replace=TRUE)
> qx<-quantile(x,probs=0.10)
> qx
> #10%
> #3.8
> as.vector(qx)
> #3.8
***
This email and any attachments are confidential. Any use...{{dropped:8}}
g"
Cc:
Sent: Wednesday, June 19, 2013 3:44 AM
Subject: [R] quantile
Hello,How do I extract only the value from the quantile
function?example:quantile (x, probs = 0.10) 10%-1.83442I want to add salt
only the number -1.83442
SincerelyFrancesco Miranda
[[alt
Hello,How do I extract only the value from the quantile
function?example:quantile (x, probs = 0.10) 10%-1.83442I want to add salt
only the number -1.83442
SincerelyFrancesco Miranda
[[alternative HTML version deleted]]
This is a bit like asking how should I tweak my sailboat so I can explore the
ocean floor.
url:www.econ.uiuc.edu/~rogerRoger Koenker
emailrkoen...@uiuc.eduDepartment of Economics
vox: 217-333-4558University of Illinois
fax: 217-244-6678
Hallo.
Is there any package / code snippet to estimate quantile regression
for a binary choice model (like probit) and selection model (like
heckit)? I found that quantreg package can estimate tobit-like model,
but I can't figure out how to tweak it for probit / heckit.
Best wishes,
Michal
_
Hi Marius,
I have tried debugging the qua.regressCOP2 function.
The error I'am getting is:
"Error in cop(u, v + delv, ...) : unused argument(s) (v + delv)".
Unable to decipher it.
And have mailed to william.asquith at ttu.edu>.
Thanks
indu
--
View this message in context:
http://r.789695.n4
Please note:
1) your example is not working in the way you provided it (see
http://www.minimalbeispiel.de/mini-en.html)
2) you receive a warning, not an error
3) I'd try and debug qua.regressCOP2 to see why the warning appears
4) in case 3) does not help, contact the maintainer of copBasic (Willia
Hi all,
Has anyone used the qua.regressCOP2 function from the copBasic package???
The default copula function used in this function is plackett copula and I
wanted to use archimedean copula. Attached below is my code:
mycop<-frankCopula
V=seq(0.001,0.99,by=0.000217)
R<-qua.regressCOP2(0.25,V,cop=
Good evening (in Italy),
Someone of you have ever read anything about quantile cointegration?
I want to use the test statistic explained in Chuang et al. (2009), that
fundamentally followed the suggestion of Koenker and Machado (1999). This is
a Wald test used for quantile cointegration proposed
Dear R users,
I am trying to estimate a median regression with fixed effects. I have an
unbalanced panel data set with 5,000 individuals and 10 years, resulting in a
total of 20,000 observations.
When I try to add individual (firmid) fixed effects to the quantile regression
using the followin
Hi, everyone.
I have some questions about quantile regression in R.
I am running an additive quantile regression first for a complete matrix and
then with some selected rows.
I am doing the following:
datos <-read.table("Regresion multiple.txt",header=T)
Fit<-rqss(datos$campings
~datos$Cobarb
Take a look at demo(Mel) in the quantreg package.
Roger Koenker
rkoen...@illinois.edu
On Jul 14, 2012, at 6:55 AM, stefan23 wrote:
> Dear all,
> I am searching for a way to compute a test comparable to Chuang et al.
> ("Causality in Quantiles and Dynamic Stock
> Return-Volume Relations"). Th
Dear all,
I am searching for a way to compute a test comparable to Chuang et al.
("Causality in Quantiles and Dynamic Stock
Return-Volume Relations"). The aim of this test is to check wheter the
coefficient of a quantile regression granger-causes Y in a quantile range. I
have nearly computed every
Optim() by default is using Nelder-Mead which is an extremely poor way to
do linear programming, despite the fact that ?optim says that: "It will work
reasonably well for
non-differentiable functions."I didn't check your coding of the objective
function fully, but at the
very least you sho
Hello Everyone,
I'm currently learning about quantile regressions. I've been using an
optimizer to compare with the rq() command for quantile regression.
When I run the code, the results show that my coefficients are consistent
with rq(), but the intercept term can vary by a lot.
I don't thi
On Thu, Mar 1, 2012 at 12:07 PM, Doran, Harold wrote:
> Typically this list doesn't support general statistical questions and
> unfortunately I don't have a better recommendation. It may be more helpful
> for you to work with a statistician than seek help here.
>
> My point is simply that quanti
ay, February 29, 2012 5:52 PM
To: Doran, Harold
Cc: Rob James; r-help@r-project.org
Subject: Re: [R] Quantile scores as dependent variables.. an R and general
method question
On Wed, Feb 29, 2012 at 1:23 PM, Doran, Harold wrote:
>
> The OP is looking for a way to deal with outcomes scores that
r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf
> Of ilai [ke...@math.montana.edu]
> Sent: Wednesday, February 29, 2012 1:30 PM
> To: Rob James
> Cc: r-help@r-project.org
> Subject: Re: [R] Quantile scores as dependent variables.. an R and general
> metho
eeded.
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of
ilai [ke...@math.montana.edu]
Sent: Wednesday, February 29, 2012 1:30 PM
To: Rob James
Cc: r-help@r-project.org
Subject: Re: [R] Quantile scores as dependent variables.. an R and general
method question
On Tue, F
hers).
>
> Are there methods for dealing with quantile dependent variables. My atempt
> to find such methods has not bee successful.
>
Really? because google found 227k hits for "R quantile regression" -
none of them lead anywhere ?
> Any leads to the
I have a dataset that does not include native scores, but only serial
quantile rankings for a set of units.
Clearly these observations are dependent (in that you can't alter one
observation without also altering others).
Are there methods for dealing with quantile dependent variables. My atempt
t
Hello,
I need to analyse some data coming from a questionnaire which have for
each item a likert scale 1-5. I need to find the lowest scores in the
distribution, and for this purpose I thought to use the quantile()
function to identify the participants belonging to the 5% with lowest
scores (w
Dear all,
I need to run a quantile regression to estimate the coefficients of the
following model: Q_{Y}(Ï|X)=exp(βâ(Ï)+Xâ²Î²â(Ï)).
Since the model is nonlinear, I need to use nlrq(.). However, if I try
nlrq(Y~exp(X), tau=Ï), the software does not accept and also does not
unders
. Geological Survey
Fort Collins Science Center
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8818
email: brian_c...@usgs.gov
tel: 970 226-9326
From:
"Prew, Paul"
To:
"r-help@R-project.org"
Date:
07/11/2011 11:42 AM
Subject:
[R] quantile regression: out of memory error
Sent
Paul,
Yours is NOT a large problem, but it becomes a large problem when you ask for
ALL the distinct
QR solutions by specifying tau = -1. You probably don't want to see all these
solutions, I suspect
that only tau = 1:19/20 or so would suffice. Try this, and see how it goes.
Roger
url:ww
Koenker [mailto:rkoen...@uiuc.edu]
Sent: Monday, July 11, 2011 12:48 PM
To: Prew, Paul
Cc: r-help@r-project.org help
Subject: Re: [R] quantile regression: out of memory error
Paul,
Yours is NOT a large problem, but it becomes a large problem when you ask for
ALL the distinct
QR solutions by
Hello, I’m wondering if anyone can offer advice on the out-of-memory error I’m
getting. I’m using R2.12.2 on Windows XP, Platform: i386-pc-mingw32/i386
(32-bit).
I am using the quantreg package, trying to perform a quantile regression on a
dataframe that has 11,254 rows and 5 columns.
> obje
Pls disregard...I have it figured out. Thank you.
Regards,
Peter D. Sheldrick
Hartford Financial Services Group
> _
> From: Sheldrick, Peter (Specialty Casualty UW Support)
> Sent: Friday, April 08, 2011 9:53 AM
> To: 'r-help@R-project.
Dear Peter,
Quantile regression is a nice tool but one that requires some statistical
training in order to use it and interpret the results properly. I suggest
backing up a bit.
Frank
Sheldrick,
Peter (Specialty Casualty UW Support) wrote:
>
> Sir or Madam:
>
> I am new to R and the
Sir or Madam:
I am new to R and the use of quantile regeression. In addition, I am a
finance person not a true statistcian. Basic regression form is Y =
(Coefficient * Variable) + Error Term
I have results from a quantile regression where I used the Barro and
Roberts method with bootstrapping f
On 2011-03-28 02:51, Mohamed Lajnef wrote:
HI Laszlo,
q<-quantile(small_df,probs=0.95)
q[[1]]
[1] 12.85
Regrads
Or, perhaps more succinctly:
unname(q)
since '95%' is just the name of the vector.
Peter Ehlers
Le 28/03/11 11:37, Bodnar Laszlo EB_HU a écrit :
Hi,
I am using the quan
HI Laszlo,
q<-quantile(small_df,probs=0.95)
q[[1]]
[1] 12.85
Regrads
Le 28/03/11 11:37, Bodnar Laszlo EB_HU a écrit :
> Hi,
>
> I am using the quantile function currently and I have just bumped into a
> little problem.
>
> I have a very small data frame something like this:
>
> small_df<-
Hi,
I am using the quantile function currently and I have just bumped into a little
problem.
I have a very small data frame something like this:
small_df <-
c(7,3,4,7,1,10,12,1,12,4,4,8,6,11,9,10,4,13,3,9,6,5,2,10,7,14,2,7,10,10,7,8,2,11,3,10,11,3,11,14,12,7,6,11)
small_df
Now in the next ste
You could use the survey package to run the bootstrapping, if you mean
the Rao & Wu bootstrap that samples n-1 of n PSUs in each replicate.
Set up a survey design object with bootstrap replicate weights: use
svrepdesign() if you already have replicate weights, use svydesign()
and then as.svrepdesi
I am new to R and am interested in using the program to fit quantile
regression models to data collected from a multi-stage probability
sample of the US population. The quantile regression package, rq, can
accommodate person weights. However, it is not clear to me that
boot.rq is appropriate for
-4480
"Is the room still a room when its empty? Does the room,
the thing itself have purpose? Or do we, what's the word... imbue it."
- Jubal Early, Firefly
r-help-boun...@r-project.org wrote on 01/19/2011 11:30:49 AM:
> [image removed]
>
> [R] Quantile Regressi
Dear R users
Is there a way to obtain the residuals from a model fitted by quantile
regression? Thank you.
Thanaset
--
View this message in context:
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Sent from the R help mailing list archive at Nabbl
We would like to use the qrnn package for building a quantile linear ridge
regression.
To this end we need to use the function qrnn.rbf.
The meaning of the second argument x.basis, isn't clear to me.
What should I give it as an argument? Does the contents of this matrix have
any meaning or only it
I don't understand why 'quantile' works in this case:
> tt <- rep(c('a','b'),c(10,3))
> sapply(0:6/6,function(q) quantile(tt,probs=q,type=1))
0% 16.7% 33.3% 50% 66.7% 83.3% 100%
"a" "a" "a" "a" "a" "b" "b"
and also
> qua
Thank you all for the explanation!
Best,
Julia
> Date: Thu, 7 Oct 2010 22:37:32 +1100
> Subject: Re: [R] quantile regression
> From: michael.bedw...@gmail.com
> To: martyn.b...@nag.co.uk
> CC: julia.l...@hotmail.co.uk; r-help@r-project.org
>
> Hi Julia,
>
&
nsim by 2 matrix, with each row holding the
> coefficients from a different simulation. You could also look at
> removing the loop by vectorising the code.
>
> Hope this helps
>
> Martyn
>
>
> -Original Message-
> From: r-help-boun...@r-project.org [mailt
On Oct 7, 2010, at 6:40 AM, Julia Lira wrote:
Dear all,
I am a new user in r and I am facing some problems with the quantile
regression specification. I have two matrix (mresultb and mresultx)
with nrow=1000 and ncol=nsim, where I specify (let's say) nsim=10.
Hence, the columns in my
imulation. You could also look at
removing the loop by vectorising the code.
Hope this helps
Martyn
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On Behalf Of Julia Lira
Sent: 07 October 2010 11:40
To: r-help@r-project.org
Subject: [R]
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