Yes. You may use the acf.pacf.plot, tsacfplots and related functions
in the HH package.
>From ?HH::tsacfplots
tsacfplots(co2)
acf.pacf.plot(co2)
If you want just the acf, and not the pacf also, you can use
update(acf.pacf.plot(co2)[1], layout=c(1,1), main="ACF: co2")
On Sat, Aug
Undoubtedly. Consider nlme::plot.ACF as one possibility. Roll your own is also
feasible.
--
Sent from my phone. Please excuse my brevity.
On August 20, 2016 5:28:04 AM PDT, Naresh Gurbuxani
wrote:
>Using lattice package, is it possible to plot autocorrelation functions
>similar to acf in sta
Hi all
I have a powerline network connection which I'm investigating.
The test network contains some nodes to which I ping from one host.
The source host is always the same and I split the data to get files
for each connection.
A lot of ping requests get lost and I'm trying to plot an
autocorrelat
Hello all,
I have a general question about time series and I wonder if someone
could help me. I have time series data of this form:
x=c(rnorm(500,0,1),rnorm(500,5,1),rnorm(500,10,1),rnorm(500,3,1),rnorm(500,8,1),rnorm(500,4,1),rnorm(500,1,1),rnorm(500,7,1))
time=1:4000
plot(time,x)
Each "rnor
Hi Anna,
I think you use acf() function to calculate the variable autocorrelation.
I'd do:
ac <- acf(y, lag.max = 100)$acf
Here, you use $acf, then you can extract the values only.
Best regards,
Márcio Diniz
PhD Student
IME - Mathematical and Statistics Institute
USP - University - São Paulo
Hi,
I am attempting to correct my models p-values due to temporal
autocorrelations. It is not possible to model the correlation, so I have to
make do with the p-value correction. I am feeling a bit thick hereI
cannot get the autocorrelation values. What is the argument?
My aim is to multi
Dear All,
I am looking for a cointegration relationship between Spot and Future Price
of commodites. The problem i am facing follows:
1. After estimating by Engle-Grranger Method, i found that the residuals are
stationary at their level I (o), which is required to fulfill the
cointegration test.
cf(y, 1)
>
> Autocorrelations of series y, by lag
>
> 0 1
> 1.000 0.343
>
> I am simply not able to figure out how acf is used?
>
> Thanking you in advance.
>
> Regards
>
> Vincy
>
> --- On Wed, 8/24/11, Prof Brian Ripley wrote:
>
> From: Prof
: Prof Brian Ripley
Subject: Re: [R] Autocorrelation using library(tseries)
To: "Vincy Pyne"
Cc: r-help@r-project.org
Received:
Wednesday, August 24, 2011, 9:08 AM
Your understanding is wrong. For a start, there is no function acf() in
package tseries: it is in stats.
And the au
Your understanding is wrong. For a start, there is no function acf()
in package tseries: it is in stats.
And the autocorrelation at lag one is not the correlation omitting the
first and last values: it uses the mean and variance estimated from
the whole series and divisor n.
Have you looked
Dear R list
I am trying to understand the auto-correlation concept. Auto-correlation is the
self-correlation of random variable X with a certain time lag of say t.
The article
"http://www.mit.tut.fi/MIT-3010/luentokalvot/lk10-11/MDA_lecture16_11.pdf";
(Page no. 9 and 10) gives the methodology
On Wed, 8 Jun 2011, Iuri Gavronski wrote:
Hi,
I am trying to learn time series, and I am attending a colleague's
course on Econometrics. However, he uses e-views, and I use R. I am
trying to reproduce his examples in R, but I am having problems
specifying a AR(1) model. Would anyone help me wit
Hi,
I am trying to learn time series, and I am attending a colleague's
course on Econometrics. However, he uses e-views, and I use R. I am
trying to reproduce his examples in R, but I am having problems
specifying a AR(1) model. Would anyone help me with my code?
Thanks in advance!
Reproducible
Hey all!
I am working on my master thesis and I am desperate with my model.
It looks as following:
Y(t) = β1*X1(t) + β2*X2(t) + δ*(β1*((1+c)/(δ+c))+β2)*IE(t) -
β2*α*((1+c)/(δ+c))*(δ+g)* IE(t-1)
note: c and g is a constant value
The problem I encounter is that between IE(t) and IE(t-1) there is
Arni Magnusson hafro.is> writes:
>
> I have been reading about autocorrelation in linear models over the last
> couple of days, and I have to say the more I read, the more confused I
> get. Beyond confusion lies enlightenment, so I'm tempted to ask R-Help for
> guidance.
>
> Most authors are
I have been reading about autocorrelation in linear models over the last
couple of days, and I have to say the more I read, the more confused I
get. Beyond confusion lies enlightenment, so I'm tempted to ask R-Help for
guidance.
Most authors are mainly worried about autocorrelation in the resi
Dear R Users,
Kindly advice me what's wrong in my programming.
I'm using the Cochrane-Orcutt two stage procedure with Prais Wisten
transformation, below is my R programming :
>Y<-c(60.8,62.5,64.6,66.1,67.7,69.1,71.7,73.5,76.2,77.3,78.8,80.2,82.6,84.3,83.3,84.1,86.4,87.6,89.1,89.3,89.1,
>,
+
89
You can fit this model with AD Model Builder's random effects module.
there is an example fitting a Poisson and negative binomial to the
venerable
polio data set with ar(1) random effects at
http://admb-project.org/examples/count-data/negative-binomial-serially-correlated-counts
A big
see
http://onlinelibrary.wiley.com/doi/10./j.1467-9892.2010.00684.x/abstract
kjetil
On Fri, Nov 19, 2010 at 6:02 PM, wrote:
> hello,
>
> I try to model traffic accidents with the following model:
>
> glm.nb(y~j+w+m+sf+b+ft,data=fr[]). the problem is that there exist
> autocorrelation in th
hello,
I try to model traffic accidents with the following model:
glm.nb(y~j+w+m+sf+b+ft,data=fr[]). the problem is that there exist
autocorrelation in the data. one possibility is to model traffic
accidents with inar(1)-models. has anyone an idea how to change this
model in order to abta
Hello,
*I have some issues with furnction that I've used before and that are not
working anymore. I want to check the autocorrelation of an object merMCMC
but the function autocorr did not acccept merMCMC object.Is there any ither
funtion that I could use?*
*>mm0.REML<-lmer(TD~1+SEXE+(1|PROVINCE)
Hello
Can someone please let me know how to test for Autocorrelation in R ( eg.
like durbin-watson statistic or any other test) after performing Non linear
least squares and what can be the best solution for it.
Thanks
Regards
Ruchita
[[alternative HTML version deleted]]
__
Hi Bernardo,
I suggest you give a look at:
Dale MRT & Fortin MJ, 2009. Spatial Autocorrelation and Statistical Tests:
Some Solutions.
Journal of Agricultural, Biological and Environmental Statistics,
14(2):188-206.
Cheers
milton
On Tue, Aug 25, 2009 at 1:08 PM, B Garcia Carreras <
bernardo.garc
Hi,
I have two sets of data for a given set of (non-lattice) locations. I would
like to know whether the two are significantly different. This would be
simple enough if it wasn't for the fact that the data is spatially
autocorrelated. I have come across several possible solutions (including
Cliff
>
> Hi
> Is any multiple regression-like test with correction for
> autocorrelation ?
If I understand your question, yes. Take a look at the spdep package for
starters. Also you may find the following references helpful.
Dormann et al. 2007. Methods to account for spatial autocorrelation in t
Hi
Is any multiple regression-like test with correction for autocorrelation ?
Wojciech
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http:
Keeping Gavin's advice in mind, you may also want to look at ?acf (and
see section 14.1 of MASS) and help(ACF, package=nlme) (see section 5.3
of MEMSS). These are useful functions for exploring the 1d empirical
autocorrelation structure of model residuals.
hth,
Kingsford Jones
On Fri, Aug 15, 20
On Thu, 2008-08-14 at 16:12 +0100, Abigail McQuatters-Gollop wrote:
> Hi,
>
> I am looking at the effects of two explanatory variables on chlorophyll.
> The data are an annual time-series (so are autocorrelated) and the
> relationships are non-linear. I want to account for autocorrelation in
> my
Hi,
I am looking at the effects of two explanatory variables on chlorophyll.
The data are an annual time-series (so are autocorrelated) and the
relationships are non-linear. I want to account for autocorrelation in
my model.
The model I am trying to use is this:
Library(mgcv)
gam1 <-g
Thanks. Here are some information about my computer and file:
Operating system: Windows 2000
RAM: 1.99 GB
After I run the program:
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 468065 12.5 818163 21.9 818163 21.9
Vcells 1160828 8.9 46162021 352.2 5486949
More information is needed. What is your operating system? How much RAM do
you have? Are there other objects in memory that you could delete to
recover some space? What does 'str' and 'object.size' say for the data you
are analyzing? What does 'gc()' report - you may want to do this
before/a
Dear R community,
Below you may find the details of my model (lm11). I receive the error
message "Error: cannot allocate vector of size 220979 Kb" after
applying the autocorrelation function update(lm11, corr=corAR1()).
lm11<-lme(Soil.temp ~ Veg*M+Veg*year,
data=a,
ran
Namens J S
> Verzonden: vrijdag 16 mei 2008 23:06
> Aan: r-help@r-project.org
> Onderwerp: [R] autocorrelation in nlme; Error: cannot allocate vector of
> size
>
> Dear R community,
>
>
>
> I used a linear mixed model (named lm11) to model daily soil temperature
&g
ata and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-Oorspronkelijk bericht-
Van: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
Namens J S
Verzonden: vrijdag 16 mei 2008 23:06
Aan: r-help@r-project.org
Ond
Dear R community,
I used a linear mixed model (named lm11) to model daily soil temperature
depending upon vegetation cover and air temperature. I have almost 17,000
observations for six years.
I can not account for autocorrelation in my model, since I receive the error
message after applying
Dear R community,
I used a linear mixed model (named lm11) to model daily soil temperature
depending upon vegetation cover and air temperature. I have almost 17,000
observations for six years.
I can not account for autocorrelation in my model, since I receive the error
message after applying
Hi all!
(How) is it possible to fit a mixed model with group specific auto-correlation
structure ? For instance, not all my groups display auto-correlation so I would
like to use a corMatrix (corAR1) only for the auto-correlated ones. If I
construct manually a
the corMatrix, is it possible to
Hi,
I am trying to calculate the autocorrelation matrix for an input matrix with
the size n*m where n=7 (the dimensionality of my input feature vectors) and m
being the time. Thus one could think of the input data as a 7-dimensional
time-series. Does anyone know of any way to calculate the autoc
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