1. This doesn't make much sense:
smoothdf <- data.frame(
x = 1:n,
y = as.vector(smooth(dat$g)),
method = "smooth()"
)
What do you think the "method" invocation does (data.frame has no "method"
argument)?
2. Show us what you have tried -- it depends on what graphics system you
use. In latti
Dear all,
I have panel data for a series (g) for three time periods. The variable is
likely autocorrelated. I would like to generate a new variable using
exponential/loess smoothing by group (gid).
For time series, I could have done something like this;
smoothdf <- data.frame(
x = 1:n,
y = a
Dear R-Team,
i only want to smooth a time series with a Kalmen Filter in R (KFAS).
I found code in the Internet, which I had to change a little bit.
Now I get the following error-message. I don’t know what I have
to do now.
Fehler in is.SSModel(do.call(updatefn, args = c(list(inits, model),
u
On Oct 18, 2014, at 7:57 AM, umair durrani wrote:
> Please note that I have already asked this question on stackoverflow.com but
> did not get a satisfactory answer.
You should say what was unsatisfactory about the answer you were offered:
http://stackoverflow.com/questions/26434652/data-smooth
Please note that I have already asked this question on stackoverflow.com but
did not get a satisfactory answer. I have a data set containing velocities of
2169 vehicles recorded at
intervals of 0.1 seconds. So, there are many rows for an individual
vehicle. Here I am reproducing the data only f
thanks you so much
On Thu, Mar 20, 2014 at 10:25 AM, MacQueen, Don wrote:
> Perhaps you will understand the meaning better if you try the following
> sequence:
>
> plot(datalist, ylab='Value')
> lines(lowess(datalist),col="blue", lwd=2)
> lines(lowess(datalist,f=1/3),col="red", lwd=2)
> lines(l
Perhaps you will understand the meaning better if you try the following
sequence:
plot(datalist, ylab='Value')
lines(lowess(datalist),col="blue", lwd=2)
lines(lowess(datalist,f=1/3),col="red", lwd=2)
lines(lowess(datalist,f=1/10),col="green", lwd=2)
-Don
--
Don MacQueen
Lawrence Livermore Nat
Hi friends
Iwould like to smooth my plot. I have a plot which is wiggly and I want to
smooth it. here is my data
539, 532, 531, 538, 544, 554, 575, 571, 543, 559, 511, 525, 512, 540, 535,
514, 524, 527, 532, 547, 564, 548, 572, 564, 549, 532, 519, 520, 520, 543,
550, 542, 528, 523, 531, 548, 554,
...@comcast.net]
Sent: Tuesday, June 19, 2012 10:00 AM
To: dcarl...@tamu.edu
Cc: "'Kehl Dániel'"; r-help@r-project.org
Subject: Re: [R] Smoothing a persp graph
On Jun 19, 2012, at 10:02 AM, David L Carlson wrote:
kde2d is for two dimensional data. The persp graph is 3d.
Huh
?loess, mgcv, kriging, ... There must be hundreds. Have you consulted the
CRAN "spatial" task view?
-- Bert
On Tue, Jun 19, 2012 at 8:34 AM, Guillaume Chapron <
carnivorescie...@gmail.com> wrote:
> On Jun 19, 2012, at 10:02 AM, David L Carlson wrote:
>
> kde2d is for two dimensional data. The p
standard problem in
geostatistics.
---
David 2
> -Original Message-
> From: David Winsemius [mailto:dwinsem...@comcast.net]
> Sent: Tuesday, June 19, 2012 10:00 AM
> To: dcarl...@tamu.edu
> Cc: "'Kehl Dániel'"; r-help@r-project.org
> Subject: Re: [R] Smo
On Jun 19, 2012, at 10:02 AM, David L Carlson wrote:
kde2d is for two dimensional data. The persp graph is 3d.
Huh? The question asked about plotting data that was 2d. The third
dimension was to be the density. kde2d in package MASS or the
similarly named function in package KernSmooth would
.org [mailto:r-help-bounces@r-
project.org] On Behalf Of Kehl Dániel
Sent: Tuesday, June 19, 2012 8:42 AM
To: r-help@r-project.org
Subject: Re: [R] Smoothing a persp graph
Take a look at the
kde2d
function in the MASS package, maybe it helps.
Best
kd
2012.06.19. 14:26 keltezissel, Guillaume Cha
om: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Kehl Dániel
> Sent: Tuesday, June 19, 2012 8:42 AM
> To: r-help@r-project.org
> Subject: Re: [R] Smoothing a persp graph
>
> Take a look at the
>
> kde2d
> function in the MASS package, maybe
Take a look at the
kde2d
function in the MASS package, maybe it helps.
Best
kd
2012.06.19. 14:26 keltezéssel, Guillaume Chapron írta:
> Hi,
>
> I'm unable to find a way to smooth data for a persp() graph.
>
> Example, suppose that I have data x,y,z like this:
>
> x<- 1:10
> y<- 1:10
>
> k<- 20
Hi,
I'm unable to find a way to smooth data for a persp() graph.
Example, suppose that I have data x,y,z like this:
x <- 1:10
y <- 1:10
k <- 20
z <- outer(x, y, "*") + matrix( k*runif(100, -1, 1), 10, 10)
persp(x, y, z, theta = 35, phi = 25)
The graph is not very nice. Is there a way to smooth
something like
library(mgcv)
gam(y~s(x,k=50),method="REML")
is one option for spline smoothing y w.r.t. x and choosing the smoothing
parameter by GML (GML and REML do the same thing).
On 18/12/11 12:26, ali_protocol wrote:
Hi there,
How may I smooth spline two vectors with the smoothing
Hi there,
How may I smooth spline two vectors with the smoothing parameter selected
by generalized maximum likelihood (GML) .?
Thanks a lot.
--
View this message in context:
http://r.789695.n4.nabble.com/Smoothing-spline-with-smoothing-parameters-selected-by-generalized-maximum-likelihood-tp4
Dear R Users,
Using the following R-script I created the first image
> require(akima)
> require(spatial)
> dep <- interp(long, lat, depth, xo=seq(1,990,10), yo=seq(1,990,10),
+ extrap=FALSE, ncp=0,duplicate = "mean", dupfun = NULL)
http://r.789695.n4.nabble.com/file/n3431391/Rpics.bmp
W
> [mailto:r-help-boun...@r-project.org] On Behalf Of Ralf B
> Sent: Tuesday, May 11, 2010 3:17 AM
> To: r-help@r-project.org
> Subject: [R] Smoothing Techniques - short stepwise functions
> with spikes
>
> R Friends,
>
> I have data from which I would like to learn
This removes runs of length 1 and 2. It replaces the values in any
such run with NA and then uses na.locf from the zoo package to fill
those NA's by carrying forward the last occurrence of a non-NA. In
this example the run consisting of a single 2, the run consisting of
two 3's and the run consi
Hi Ralf,
I can't offer you many resources, but the few I came across are:
1) loess (or the older version: lowess)
2) smooth
3) rollapply (from the zoo pacakge)
I used a combination of 1 and 3 when creating an R implementaion for a
(simplistic) quantile loess, you might find the code useful:
http:
R Friends,
I have data from which I would like to learn a more general
(smoothened) trend by applying data smoothing methods. Data points
follow a positive stepwise function.
|x
x
|
| xx
|xx
s the value of the
bandwidth is fixed (by default) in the package, unless we specify our own value?
Cheers,
Fir
- Original Message
From: "Liaw, Andy"
To: FMH ; r-help@r-project.org
Sent: Mon, May 10, 2010 4:00:51 PM
Subject: RE: [R] smoothing parameter in locfit package
--- Original Message
> From: "Liaw, Andy"
> To: FMH ; r-help@r-project.org
> Sent: Mon, May 10, 2010 4:00:51 PM
> Subject: RE: [R] smoothing parameter in locfit package
>
> See the "kern" argument in ?locfit.raw.
>
> Andy
>
> From: FMH
> >
See the "kern" argument in ?locfit.raw.
Andy
From: FMH
> Hi,
>
> In the locfit package, could someone please let me know the
> automatic selection of smoothing parameter if Gauss kernel
> density function is used as weight function?
>
> thanks
> Fir
>
>
>
> [[alternative HTML
Hi,
In the locfit package, could someone please let me know the automatic selection
of smoothing parameter if Gauss kernel density function is used as weight
function?
thanks
Fir
[[alternative HTML version deleted]]
__
R-help@r-proj
On Apr 13, 2010, at 10:58 AM, Danai Katsande wrote:
I am trying to plot a smoothing spline to using the following code,
and an
error pops up:
car <- read.table("car.csv",sep=",",header=T)
attach(car)
names(car)
[1] "veh_value" "exposure" "clm" "numclaims" "claimcst0"
"veh_body"
>
> I am trying to plot a smoothing spline to using the following code, and an
> error pops up:
>
> > car <- read.table("car.csv",sep=",",header=T)
> > attach(car)
> > names(car)
> [1] "veh_value" "exposure" "clm" "numclaims" "claimcst0" "veh_body"
>
> [7] "veh_age" "gender""area"
Hello,
I'm new to R.
In Sapa package there's an example for function SDF like
data <- as.numeric(sunspots)
methods <- c("direct","wosa","multitaper","lag window")
S <- lapply(methods, function(x, data) SDF(data, method=x), data)
x <- attr(S[[1]], "frequency")[-1]
y <- lapply(S,function(x) decibel(
Thanks, Derek.
I used your code, but H still has a lot of columns being 0. I'm not sure
why.
I'm dealing the data http://www-stat.stanford.edu/~tibs/ElemStatLearn/
with X=age, Y=spnbmd
I think X has too many duplicated data???
I'll check the book you recommended in the library tomorrow. Thanks
Thank you. D Sonderegger.
Did you mean use
bs <- bs(X, knots = spl$fit$knot)
H <- predict(bs, X)
Then H should be the matrix for the original data under the smoothing spline
basis?
However, another problem arises, since I need to use H to estimate the
coefficient beta, which involves (H'H)^{-
Close, but not quite. Your design matrix is:
H <- bs(X, knots=spl$fit$knot, intercept=TRUE)
So long as you have data in all of the inter-knot regions, you should be ok.
A good book to check out about this sort of thing is Ruppert, Wand and
Carroll's Semiparametric Regression book. I can't reco
I believe that smooth.spline fits a cubic B-spline to the data. So you just
need to know the knot points (which are returned by smooth.spline as
spl$fit$knot) and then use the bs() function in the splines library.
pinkdd wrote:
>
> Anybody knows how to generate the basis matrix for smoothing s
Anybody knows how to generate the basis matrix for smoothing spline?
And how about the smoother matrix? I tried to use the following code, but
there exist replicated data in X, and the length of smooth.spline(X,
S[,i])$y is smaller than X, and then there is error in the last step.
spl <- smoot
I was looking for suggestions as to how to smooth a timeseries and, having
accomplished that, how to find the fitted curve values for intermediate points.
I've tried numerous examples of possible approaches in R that I've found on the
web, but when applied to my simple data, R returns an error
forestra wrote:
>
> Hello to everybody,
>
> I need to know the Smoothing Parameter to obtain Home Range of an animal
> through the Area Kernel. I have 200 locations with x and y. How can I
> obtain the Smoothing Parameter with R for LSCV, CV and Href method???
>
>
We're sorry, but this is
Hello to everybody,
I need to know the Smoothing Parameter to obtain Home Range of an animal
through the Area Kernel. I have 200 locations with x and y. How can I obtain
the Smoothing Parameter with R for LSCV, CV and Href method???
Thank you.
--
View this message in context:
http://www.
Hello to everybody,
I need to know the Smoothing Parameter to obtain Home Range of an animal
through the Area Kernel. I have 200 locations with x and y. How can I obtain
the Smoothing Parameter with R for LSCV, CV and Href method???
Thank you.
--
View this message in context:
http://www.nab
Sorry, I did not notice you were using GAM package. Most R users are using
Simon Wood's MGCV package. I recommend you to use it. I have never used GAM
package, so I cannot make further comments. Good luck!
楊 詩韻 wrote:
>
>
> dear all,
>
>
>
> i have a little question, but it make me torment
Strangely, summary.gam(m1) should give you significance results of parametric
terms such as ost, wst, park10, sch50, comm, build and suite. These results
should be located above the summary results for smooth terms.
Please using summary.gam(m1) to have a look if there is the information you
need.
dear all,
i have a little question, but it make me torment long time
hope you can help me and give some advices , thanks
i use smoothing spline in package gam
the model
> m1=gam(y~ost+wst+park10+sch50+comm+build+suite+y05+y06+y07+y99+y98+s(builarea)+s(age)+s(fl)+s(totfl)+s(c
the thin plate spline, or tensor product smooths built into `mgcv' might be
useful here (by default mgcv does automatic bandwidth selection for these).
On Sunday 01 March 2009 09:51, Žroutík wrote:
> Hi R-users,
>
> I'd like to smooth a matrix to dismiss spikes and to interpolate in plane
>
> ex
Žroutík gmail.com> writes:
> I'd like to smooth a matrix to dismiss spikes and to interpolate in plane
>
interp.loess in package tgp works nicely for me.
Dieter
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE
Hi R-users,
I'd like to smooth a matrix to dismiss spikes and to interpolate in plane
example of a matrix:
Map[1:3,1:3]
[,1] [,2] [,3]...
[1,] 34.4 34.2 35.1
[2,] 33.4 34.2 35.4
[3,] 34.1 33.2 32.1
dim(Map)[1] =/= dim(Map)[2]
What functions can I use?
Thanks a lot for any response,
You might take a look at this competitor to conv2:
http://www.advancedsourcecode.com/ffw.asp
... which is Open Source, not too long, and appears to be built out
of tools which have analogs in the signal package:
http://cran.r-project.org/web/packages/signal/signal.pdf
--
David winsemius
Hi all,
is there a function / package in R that provides a function like
Matlab's conv2 or filter2 for smoothing a vector- / velocity- field.
I unfortunately could not find anything.
Thanks a lot.
__
R-help@r-project.org mailing list
https://stat.eth
Hi,
I need to use the Smoothing Spline Clustering (SSC):
genemerge.bioteam.net/SSClust-Manual.pdf
But it doesn't work!
If someone can try it and help me!
In the folder you will find a file named SSClust.R, but, I do not why,
it doesn't work. Also the SSClust.test.R file doesn't work, but it
produ
Use ksmooth(x, y, kernel="normal")
Andy
From: dxc13
> useR's,
>
> Does anyone know what function or package allows one to
> specify use of the
> Gaussian kernel to smooth a data set, or is this just the
> default for most
> functions?
>
> Thanks,
> dxc13
> --
> View this message in context
useR's,
Does anyone know what function or package allows one to specify use of the
Gaussian kernel to smooth a data set, or is this just the default for most
functions?
Thanks,
dxc13
--
View this message in context:
http://www.nabble.com/smoothing-with-the-Gaussian-kernel-tp19323294p19323294.h
Ruby_Stanford gmail.com> writes:
> Hey. I have a set of data points (x1,y1,z1;
> x2,y2,z2;...xn,yn,zn). I need to smooth these in 3D.
> For example if these were in 2 D then one would use inverse distance
> weighting or moving averages.
interp.loess {tgp} is easiest to u
Hey. I have a set of data points (x1,y1,z1;
x2,y2,z2;...xn,yn,zn). I need to smooth these in 3D.
For example if these were in 2 D then one would use inverse distance
weighting or moving averages. Does anyone know of any funtion in R that can
be used to do this (Using 3D dat
"Emmanuel Levy" <[EMAIL PROTECTED]> wrote in
news:[EMAIL PROTECTED]:
> Dear David,
>
> Thanks a lot for pointing out kde2d, just tried it out but the
> problem is that it indeed takes the density of points into account,
> which I dont want.
>
> For example, if in an region of surface S I've go
Dear David,
Thanks a lot for pointing out kde2d, just tried it out but the problem is that
it indeed takes the density of points into account, which I dont want.
For example, if in an region of surface S I've got 10,000 points, and that their
average height is 0.5, and in an other region I've got
"Emmanuel Levy" <[EMAIL PROTECTED]> wrote in
news:[EMAIL PROTECTED]:
> Dear Bert,
>
> Thanks for your reply - I indeed saw a lot of functions using:
> help.search("smooth")
>
> The problem is that most seem to not be very appropriate to what I'd
> like, or they seem extremely complicated (e.g.
"Emmanuel Levy" <[EMAIL PROTECTED]> wrote in
news:[EMAIL PROTECTED]:
> Dear Bert,
>
> Thanks for your reply - I indeed saw a lot of functions using:
> help.search("smooth")
>
> The problem is that most seem to not be very appropriate to what I'd
> like, or they seem extremely complicated (e.g.
in
> the base distribution, which will do exactly what you requested.
>
>
> Bert Gunter
> Genentech Nonclinical Statistics
>
>
>
> -Original Message-----
> From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
> Behalf Of Emmanuel Levy
> Sent: Wednesday
om: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of Emmanuel Levy
Sent: Wednesday, March 19, 2008 12:42 PM
To: r-help@r-project.org
Subject: [R] Smoothing z-values according to their x, y positions
Dear All,
I'm sure this is not the first time this question comes up but I
couldn't fi
Dear All,
I'm sure this is not the first time this question comes up but I
couldn't find the keywords that would point me out to it - so
apologies if this is a re-post.
Basically I've got thousands of points, each depending on three variables:
x, y, and z.
if I do a plot(x,y, col=z), I get somet
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