[R] quantile regression - estimation of CAViaR
How is it possible to estimate the conditional autoregressive Value-at-Risk model qantile_t(tau)=a0+a1*qantile_(t-1)(tau)+a2*abs(r_(t-1)) see http://www.faculty.ucr.edu/~taelee/paper/BLSpaper1.pdf (page 10)) of Engle Manganelli in R? The qantile_(t-1)(tau)-term causes headache. Kind regards, Jaci -- Ein Herz für Kinder - Ihre Spende hilft! Aktion: www.deutschlandsegelt.de Unser Dankeschön: Ihr Name auf dem Segel der 1. deutschen America's Cup-Yacht! __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] quantile regression and moments
Hi, how to derive an estimate of skewness and kurtosis out of a predicted distribution by quantile regression? Example: library(quantreg) data(airquality) airq - airquality[143,] f - rq(Ozone ~ ., data=airquality,tau=seq(0.01,0.99,0.01)) predict(f,newdata=airq) Any suggestions? Kind regards, Jaci -- __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Quantile Regression: Measuring Goodness of Fit
Hi Martin, Efferz, Martin efferz at finance.uni-mainz.de writes: Hi, how to measure the goodness of fit, when using the rq() function of quantreg? I need something like an R^2 for quantile regression, a single number which tells me if the fit of the whole quantile process (not only for a single quantile) is o.k. or not. Is it possible to compare the (conditional) quantile process with the (unconditional) empirical distribution function? Perhaps with a Chi^2 or Kolmogorv-Smirnov Test? Thanks for feedback. Martin Please see: http://www.econ.uiuc.edu/~roger/research/rq/rq.html Anupam. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Quantile Regression: Measuring Goodness of Fit
Hi, how to measure the goodness of fit, when using the rq() function of quantreg? I need something like an R^2 for quantile regression, a single number which tells me if the fit of the whole quantile process (not only for a single quantile) is o.k. or not. Is it possible to compare the (conditional) quantile process with the (unconditional) empirical distribution function? Perhaps with a Chi^2 or Kolmogorv-Smirnov Test? Thanks for feedback. Martin __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Quantile regression questions
I am relatively new to R, but am intrigued by its flexibility. I am interested in quantile regression and quantile estimation as regards to cotton fiber length distributions. The length distribution affects spinning and weaving properties, so it is desirable to select for certain distribution types. The AFIS fiber testing machinery outputs a vector for each sample of type c(12, 235, 355, . . . n) with the number of fibers in n=40 1/16 inch length categories. My question is what would be the best way to convert the raw output to quantiles and whether it would be appropriate to use quantile regression to look at whether location, variety, replication, etc. modify the length distribution. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Quantile regression questions
Brian, It is hard to say at this level of resolution of the question, but it would seem that you might be able to start by considering each sample vector as as repeated measurement of the fiber length -- so 12 obs in the first 1/16th bin, 235 in the next and so forth, all associated with some vector of covariates representing location, variety, etc, then the conventional quantile regression would serve to estimate a conditional quantile function for fiber length for each possible covariate setting --- obviously this would require some model for the way that the covariate effects fit together, linearity, possible interactions, etc etc, and it would also presume that it made sense to treat the vector of responses as independent measurements. Building in possible dependence involves some new challenges, but there is some recent experience with inferential methods for microarrays that have incorporated these effects. I'd be happy to hear more about the data and possible models, but this should be routed privately since the topic is rather too specialized for R-help. url:www.econ.uiuc.edu/~rogerRoger Koenker email[EMAIL PROTECTED]Department of Economics vox: 217-333-4558University of Illinois fax: 217-244-6678Champaign, IL 61820 On Oct 26, 2006, at 7:20 AM, Brian Gardunia wrote: I am relatively new to R, but am intrigued by its flexibility. I am interested in quantile regression and quantile estimation as regards to cotton fiber length distributions. The length distribution affects spinning and weaving properties, so it is desirable to select for certain distribution types. The AFIS fiber testing machinery outputs a vector for each sample of type c(12, 235, 355, . . . n) with the number of fibers in n=40 1/16 inch length categories. My question is what would be the best way to convert the raw output to quantiles and whether it would be appropriate to use quantile regression to look at whether location, variety, replication, etc. modify the length distribution. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting- guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Quantile Regression
Hi, how is it possible to retrieve the corresponding tau value for each observed data pair (x(t) y(t), t=1,...,n) when doing a quantile regression like rq.fit - rq(y~x,tau=-1). Thank you for your help. Jaci -- __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Quantile Regression
data(engel) attach(engel) rq(y~x) Call: rq(formula = y ~ x) Coefficients: (Intercept) x 81.4822474 0.5601806 Degrees of freedom: 235 total; 233 residual rq(y~x)-f f$tau [1] 0.5 url:www.econ.uiuc.edu/~rogerRoger Koenker email[EMAIL PROTECTED]Department of Economics vox: 217-333-4558University of Illinois fax: 217-244-6678Champaign, IL 61820 On Oct 25, 2006, at 4:39 AM, [EMAIL PROTECTED] wrote: Hi, how is it possible to retrieve the corresponding tau value for each observed data pair (x(t) y(t), t=1,...,n) when doing a quantile regression like rq.fit - rq(y~x,tau=-1). Thank you for your help. Jaci -- __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting- guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Quantile Regression Object
Hi, I load my data set and separate it as folowing: presu - read.table(C:/_Ricardo/Paty/qtdata_f.txt, header=TRUE, sep=\t, na.strings=NA, dec=., strip.white=TRUE) dep-presu[,3]; exo-presu[,4:92]; Now, I want to use it using the wls and quantreg packages. How I change the data classes for mle and rq objects? Thanks a lot, Ricardo __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] quantile regression problem
Dear List members, I would like to ask for advise on quantile regression in R. I am trying to perform an analysis of a relationship between species abundance and its habitat requirements - the habitat requirements are, however, codes - 0,1,2,3... where 0123 and the scale is linear - so I would be happy to treat them as continuos The analysis of the data somehow does not work, I am trying to perform linear quantile regression using rq function and I cannot figure out whether there is a way to analyse the data using quantile regression ( I would really like to do this since the shape is an envelope) or whether it is not possible. I tested that if I replace the categories with continuous data of the same range it works perfectly. In the form I have them ( and I cannot change it) I am getting errors - mainly about non-positive fis. Could somebody please let me know whether there was a way to analyse the data? The data are enclosed and the question is Is there a relationship between abundance and absdeviation? I am interested in the upperlimit so I wanted to analyze the upper 5%. Thanks a lot for your help All the best Zuzana Munzbergova www.natur.cuni.cz/~zuzmun abundance absdeviation 1 0.051643192 0 2 0.056338028 1 3 0.075117371 0 4 0.131455399 0 5 0.075117371 1 6 0.009389671 1 7 0.028169014 1 8 0.009389671 1 9 0.098591549 1 10 0.093896714 0 11 0.037558685 1 12 0.028169014 2 13 0.0657277 0 14 0.028169014 0 15 0.037558685 2 16 0.0657277 0 17 0.075117371 0 18 0.03286385 3 19 0.065420561 0 20 0.08411215 1 21 0.037383178 0 22 0.08411215 0 23 0.028037383 0 24 0.070093458 1 25 0.065420561 1 26 0.018691589 1 27 0.056074766 1 28 0.102803738 0 29 0.037383178 1 30 0.018691589 0 31 0.018691589 0 32 0.08411215 2 33 0.028037383 0 34 0.037383178 0 35 0.121495327 0 36 0.042056075 3 37 0.048076923 0 38 0.105769231 1 39 0.115384615 0 40 0.096153846 0 41 0.072115385 1 42 0.009615385 1 43 0.052884615 1 44 0.009615385 1 45 0.048076923 1 46 0.038461538 0 47 0.096153846 0 48 0.009615385 1 49 0.019230769 0 50 0.009615385 0 51 0.028846154 0 52 0.086538462 0 53 0.076923077 0 54 0.076923077 3 55 0.052631579 0 56 0.078947368 1 57 0.065789474 0 58 0.078947368 0 59 0.046052632 1 60 0.039473684 1 61 0.039473684 1 62 0.078947368 1 63 0.026315789 0 64 0.138157895 0 65 0.032894737 0 66 0.026315789 0 67 0.092105263 0 68 0.026315789 0 69 0.131578947 1 70 0.046052632 3 71 0.03626943 0 72 0.056994819 1 73 0.046632124 0 74 0.103626943 0 75 0.077720207 1 76 0.020725389 1 77 0.025906736 1 78 0.186528497 1 79 0.020725389 0 80 0.103626943 0 81 0.010362694 1 82 0.025906736 0 83 0.051813472 0 84 0.015544041 0 85 0.025906736 0 86 0.077720207 0 87 0.03626943 0 88 0.03626943 1 89 0.020725389 3 90 0.020725389 1 91 0.093457944 0 92 0.074766355 1 93 0.102803738 0 94 0.018691589 0 95 0.093457944 1 96 0.028037383 1 97 0.028037383 1 98 0.009345794 1 99 0.070093458 1 100 0.018691589 0 101 0.242990654 0 102 0.018691589 1 103 0.009345794 1 104 0.023364486 0 105 0.046728972 0 106 0.018691589 0 107 0.042056075 0 108 0.018691589 0 109 0.042056075 3 110 0.122641509 0 111 0.056603774 1 112 0.056603774 0 113 0.099056604 0 114 0.018867925 0 115 0.122641509 1 116 0.009433962 1 117 0.04245283 1 118 0.028301887 0 119 0.150943396 0 120 0.018867925 1 121 0.037735849 0 122 0.047169811 0 123 0.028301887 0 124 0.08490566 0 125 0.009433962 0 126 0.066037736 1 127 0.12987013 0 128 0.060606061 1 129 0.112554113 0 130 0.138528139 1 131 0.043290043 1 132 0.034632035 1 133 0.025974026 1 134 0.017316017 1 135 0.017316017 0 136 0.12987013 0 137 0.034632035 0 138 0.025974026 0 139 0.043290043 0 140
Re: [R] quantile regression problem
Since almost all (95%) of the observations are concentrated at x=0
and x=1,
any fitting you do is strongly influenced by what would be obtained
by simply fitting quantiles at these two points and interpolating, and
extrapolating according to your favored model. I did the following:
require(quantreg)
formula - log(y) ~ x
plot(x,y)
z - 1:30/10
for(tau in 10:19/20){
f - rq(formula,tau = tau)
lines(z,exp(cbind(1,z) %*% f$coef))
}
url:www.econ.uiuc.edu/~rogerRoger Koenker
email [EMAIL PROTECTED] Department of Economics
vox:217-333-4558University of Illinois
fax:217-244-6678Champaign, IL 61820
On Dec 10, 2005, at 11:30 AM, [EMAIL PROTECTED] wrote:
Dear List members,
I would like to ask for advise on quantile regression in R.
I am trying to perform an analysis of a relationship between
species abundance and its habitat requirements -
the habitat requirements are, however, codes - 0,1,2,3... where
0123 and the scale is linear - so I would be happy to treat them
as continuos
The analysis of the data somehow does not work, I am trying to
perform linear quantile regression using rq function and I cannot
figure out whether there is a way to analyse the data using
quantile regression ( I would really like to do this since the
shape is an envelope) or whether it is not possible.
I tested that if I replace the categories with continuous data of
the same range it works perfectly. In the form I have them ( and I
cannot change it) I am getting
errors - mainly about non-positive fis.
Could somebody please let me know whether there was a way to
analyse the data?
The data are enclosed and the question is
Is there a relationship between abundance and absdeviation?
I am interested in the upperlimit so I wanted to analyze the upper 5%.
Thanks a lot for your help
All the best
Zuzana Munzbergova
www.natur.cuni.cz/~zuzmun
GSS1a.txt
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Re: [R] quantile regression problem
On 10-Dec-05 [EMAIL PROTECTED] wrote: Dear List members, I would like to ask for advise on quantile regression in R. I am trying to perform an analysis of a relationship between species abundance and its habitat requirements - the habitat requirements are, however, codes - 0,1,2,3... where 0123 and the scale is linear - so I would be happy to treat them as continuos As well as Roger Koenker's comments, you may also wish to consider the following. (By the way, despite what you say above, you have codes at values 0, 0.5, 1, 1.5, 2. 3 -- is there anything special about the 0.5 and 1.5, or are they on the same footing as 0, 1, 2, 3? Also, I am curious as to why habitat requirement is named absdeviation in the data file. What does habitat requirement mean?). The analysis of the data somehow does not work, I am trying to perform linear quantile regression using rq function and I cannot figure out whether there is a way to analyse the data using quantile regression (I would really like to do this since the shape is an envelope) or whether it is not possible. As Roger noted, the distribution of data is very variable over the values of absdeviation: absdeviation: 0 0.51 1.52 3 Number of data: 673 15493 3 19 20 Total data: 1223 Therefore you chiefly have information about the cases 0 and 1. I have loked at the data the opposite way round from you: For each value of absdeviation (H for habitat in the following), consider the values of abundance (A). For H=0 and H=1, the values of A are quite well approximated by a negative exponential distribution, thought the fit is better for H=1 than for H=0 -- in a more careful examination, I would try to emulate a for the continuous variable A a distribution inspired by the logarithmic distribution p(n) = (t^n)/(n*log(1-t)), n=0,1,2... which is a classic distribution for the probability that a species will be represented by n individuals in a sample of a large number of species whose different abundances are variable (Fisher, Corbett and Williams, and much later work). The mean A for H=0 is m0 = 0.09389265 (n0=673), and the mean A for H=1 is m1 = 0.08407791 (n1=493). with respective atandard deviations s0 = 0.1262238 s1 = 0.08952975 on the basis of which (m0-m1)/(sqrt((s0^2)/n0 + (s1^2)/n1)) = 1.553156 which is not particularly large. While the histograms hist(A[H==0],breaks=0.02*(0:50),freq=FALSE) and hist(A[H==1],breaks=0.02*(0:50),freq=FALSE) do somewhat indicate a tendency for higher values of A to occur when H=0 than when H=1 there are only a few of these. So on a first look, I am induced to conclude that there is little evidence in the two dominant data groups (H=0 and H=1) to indicate that these two groups differ. I doubt that the information for the H=0.5, H=1.5, H=2 anf H=3 would have more than a slight effect on this (though I have not looked on detail). The corresponding means, however, are m0.5 = 0.1273273(n = 15) m1.5 = 0.03003003 (n = 3) m2 = 0.02908183 (n = 19) m3 = 0.03830066 (n = 20) which at first sight does suggest that, while m0.5 is similar to m0 and m1 above, m1.5 and m2 and m3 are distinctly smaller. However, for m1.5 this is based on a very small sample, and in any case the distribution of the raw values of A is so skew that the larger values of A occurring for H=0 and H=1 are unlikely to occur in such small samples. Therefore, preliminary conclusion: I cannot see strong evidence of a relationship between absdeviation and abundance. Hoping this is useful, Best wishes, Ted. I tested that if I replace the categories with continuous data of the same range it works perfectly. In the form I have them (and I cannot change it) I am getting errors - mainly about non-positive fis. Could somebody please let me know whether there was a way to analyse the data? The data are enclosed and the question is Is there a relationship between abundance and absdeviation? I am interested in the upperlimit so I wanted to analyze the upper 5%. Thanks a lot for your help All the best Zuzana Munzbergova www.natur.cuni.cz/~zuzmun E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 10-Dec-05 Time: 23:10:15 -- XFMail -- __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Quantile Regression, S-Function Rreg
I have the following problem: I would like to do a
nonparametric quatile regression. Thus far I have used
the quantreg package and done a local quadratic, but
it does not seem to work well.
Alternatively, I have tried with an older S version I
have the function rreg, and used
rreg(datax,datay,method=function(u)
{(abs(u)+(2*alpha-1)*u)},iter=100)
which gave me pretty acceptable results. What I would
like to do now is to have a similar command in R, but
with the functions
rlm and lqs
I do not seem to be able to get somewhere. Can anybody
help?
I found in the archive under
Message-ID:
[EMAIL PROTECTED]
a reply from Brian Ripley on a similar question, but
was not able to download the experimental file from
his website...
Thanks
Stefan
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Re: [R] Quantile Regression, S-Function Rreg
Dear Brian, thanks for your mail. For other reasons I need a local polynomial. The nonparametric regression code is very scetchy, but I have used it as base anyway. Best Stefan __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Quantile Regression in R
I recently learn about Quantile Regression in R.
I am trying to study two time series (attached) by Quantile Regression in R.
I wrote the following code and do not know how to interpret the lines.
What kind of information can I get from them? Correlation for quantiles,
conditional probabilties (i.e. P(X in Quantile i | Y in Quantile i)) , and etc.
Many thanks in advance for any help.
Best,
Ali
library(quantreg)
#help.start()
Data - read.table(RESvsMOVE2.dat)
#
x - Data[,2]
y - Data[,1]
par(mfrow=c(2,2))
qqnorm(x,main=MOVE Norm Q-Q Plot, xlab=Normal Qunatiles,ylab = MOVE Quantiles)
qqline(x)
qqnorm(y,main=Residuals Norm Q-Q Plot, xlab=Normal Qunatiles,ylab = Residuals
Quantiles)
qqline(y)
plot(x,y,xlab=MOVE,ylab=Residuals,cex=.5)
xx - seq(min(x),max(x),.5)
# Just a linear regression
g - coef(lm(y~x))
yy - (g[1]+g[2]*(xx))
lines(xx,yy,col=yellow)
taus - c(.05,.1,.25,.5,.75,.9,.95)
for(tau in taus){
f - coef(rq(y~x,tau=tau,method=pfn))
yy - (f[1]+f[2]*(xx))
if (tau ==.05){
lines(xx,yy,col=red)
}
if (tau ==.95){
lines(xx,yy,col=green)
}
if (tau != .05 tau != .95){
lines(xx,yy,col=blue)
}
}
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Re: [R] Quantile Regression in R
The short answer to your question is that quantile regression
estimates are estimating linear conditional quantile functions,
just like lm() is used to estimate conditional mean functions.
A longer answer would inevitably involve unpleasant suggestions
that you should follow the posting guide:
a.) send questions about packages to the maintainer, not R-help
b.) not attach datasets in modes that are stripped by R-help
c.) make a token effort to read the documentation and related
literature
url:www.econ.uiuc.edu/~rogerRoger Koenker
email [EMAIL PROTECTED] Department of Economics
vox:217-333-4558University of Illinois
fax:217-244-6678Champaign, IL 61820
On Jun 29, 2004, at 10:26 AM, Ali Hirsa wrote:
I recently learn about Quantile Regression in R.
I am trying to study two time series (attached) by Quantile Regression
in R.
I wrote the following code and do not know how to interpret the lines.
What kind of information can I get from them? Correlation for
quantiles,
conditional probabilties (i.e. P(X in Quantile i | Y in Quantile i)) ,
and etc.
Many thanks in advance for any help.
Best,
Ali
library(quantreg)
#help.start()
Data - read.table(RESvsMOVE2.dat)
#
x - Data[,2]
y - Data[,1]
par(mfrow=c(2,2))
qqnorm(x,main=MOVE Norm Q-Q Plot, xlab=Normal Qunatiles,ylab =
MOVE Quantiles)
qqline(x)
qqnorm(y,main=Residuals Norm Q-Q Plot, xlab=Normal Qunatiles,ylab
= Residuals Quantiles)
qqline(y)
plot(x,y,xlab=MOVE,ylab=Residuals,cex=.5)
xx - seq(min(x),max(x),.5)
# Just a linear regression
g - coef(lm(y~x))
yy - (g[1]+g[2]*(xx))
lines(xx,yy,col=yellow)
taus - c(.05,.1,.25,.5,.75,.9,.95)
for(tau in taus){
f - coef(rq(y~x,tau=tau,method=pfn))
yy - (f[1]+f[2]*(xx))
if (tau ==.05){
lines(xx,yy,col=red)
}
if (tau ==.95){
lines(xx,yy,col=green)
}
if (tau != .05 tau != .95){
lines(xx,yy,col=blue)
}
}
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[R] quantile regression
Dear colleagues, How can I do quantile regression with R? Best regards Chris. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] quantile regression
Please read the footer of the message, and follow the link. Besides, you
don't need people googling for you, do you?
Andy
From: Christoph Scherber
Dear colleagues,
How can I do quantile regression with R?
Best regards
Chris.
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Re: [R] quantile regression
Christoph Scherber [EMAIL PROTECTED] writes: Dear colleagues, How can I do quantile regression with R? Package quantreg springs to mind... -- O__ Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] quantile regression
OK, Thank you all very much for the help! Best regards Chris. Christoph Scherber wrote: Dear colleagues, How can I do quantile regression with R? Best regards Chris. __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Quantile Regression Packages
I'd like to mention that there is a new quantile regression package nprq on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as functions of z1 and z2. Rather than estimating conditional mean functions, conditional quantile functions are estimated using penalty methods. When z1 is univariate fitting is based on the total variation penalty methods described in Koenker, Ng and Portnoy (Biometrika, 1994). When z2 is bivariate fitting is based on the total variation penalty (triogram) methods described in Koenker and Mizera (2003), available at http://www.econ.uiuc.edu/~roger/research/goniolatry/gon.html and forthcoming in JRSS(B). There are options to constrain the qss components to be monotone and/or convex/concave for univariate components, and to be convex/concave for bivariate components. Fitting is done by new sparse implementations of the dense interior point (Frisch-Newton) algorithms already available in the package quantreg. The new package nprq thus supplements the existing packages quantreg and nlrq that can be used for linear and nonlinear parametric quantile regression fitting respectively. In particular, nprq provides general fitting functions for quantile regression problems with sparse design matrices paralleling the functionality of least squares function slm() in the SparseM package. There has also been some recent updating of the quantreg package, which now includes some functionality for resampling based inference methods. The package nprq is joint work with Pin Ng of Northern Arizona University. Comments and suggestions, as always, would be most welcome. url:www.econ.uiuc.edu/~roger/my.htmlRoger Koenker email [EMAIL PROTECTED] Department of Economics vox:217-333-4558University of Illinois fax:217-244-6678Champaign, IL 61820 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
