Displaying 20 results from an estimated 6000 matches similar to: "GLS models - bootstrapping"
2007 Feb 21
0
GLS models - bootstrapping
Dear Lillian,
I tried to estimate parameters for time series regression using time
series bootstrapping as described on page 434 in Davison & Hinkley
(1997) - bootstrap methods and their application. This approach is based
on an AR process (ARIMA model) with a regression term (compare also with
page 414 in Venable & Ripley (2002) - modern applied statistics with S)
I rewrote the code
2006 Feb 08
1
logLik == -Inf in gls
I am trying to fit a generalised least squares model using gls in the nlme
package.
The model seems to fit very well when I plot the fitted values against the
original
values, and the model parameters have quite narrow confidence intervals
(all are
significant at p<5%).
The problem is that the log likelihood is always given as -Inf. This
doesn't seem to make sense because the model
2007 Aug 02
1
Xyplot - adding model lines to plotted points
Hello,
I have written code to plot an xyplot as follows:
library(lattice)
xyplot(len~ageJan1|as.factor(cohort),groups=sex,as.table=T,strip=strip.c
ustom(bg='white',fg='white'),data=dat,
xlab="Age (January 1st)",ylab="Length (cm)",main="Linear models for male
and female cod, by cohort",type='p',
2006 Mar 29
1
Plotting shapefiles on existing maps
Dear All,
This is probably a very basic question but:
I have plotted a map of the Barents Sea and surrounding coastline using:
map('worldHires',ylim=c(50,85),xlim=c(5,65),fill=T,resolution=0)
map.axes()
map.scale(x=30,metric=T)
Next, I imported a shapefile with depth contours for the sea:
contours<-read.shape("D://My Documents/BarentsSea.shp",dbf.data=T)
(This is in
2006 Mar 30
0
Converting shapefiles to use in contour plots
Dear R-users,
I have imported a shapefile with depth contours for a sea:
depths<-read.shape("D://My Documents/BarentsSea.shp",dbf.data=T)
(This is in mercator projection)
**Is there a way to convert this shapefile into a format that it may be
plotted on a contour plot?**
I wish to add these contours onto a map (already coded using 'maps'
package) to map the sea contours
2004 Jun 08
0
bootstrap: stratified resampling
Dear All,
I was writing a small wrapper to bootstrap a classification algorithm, but if
we generate the indices in the "usual way" as:
bootindex <- sample(index, N, replace = TRUE)
there is a non-zero probability that all the samples belong to only
one class, thus leading to problems in the fitting (or that some classes will
end up with only one sample, which will be a problem
2001 Jun 04
0
question on bootstrapping mean and sd
Not so much an R question, as a methodology one...
Dealing with some reviewers comments, one of the reviewers suggests
bootstrapping my group means and standard deviations since 2 of my 3 groups
have a small sample size. My data is geochemical data, and a variety of
clustering methods finds 3 groups in the data. One group has 50 members,
another group has 10 members and another group 12
2010 Mar 01
1
p-values from bootstrapping of time series (tsboot)
Does anyone know how p-values can be generated if tsboot (stationary
bootstrap) for time series is performed?
That would be of great help. Thanks a lot for your comments.
Markus
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2006 Aug 09
1
Joint confidence intervals for GLS models?
Dear All,
I would like to be able to estimate confidence intervals for a linear
combination of coefficients for a GLS model. I am familiar with John
Foxton's helpful paper on Time Series Regression and Generalised Least
Squares (GLS) and have learnt a bit about the gls function.
I have downloaded the gmodels package so I can use the estimable
function. The estimable function is very
2010 Mar 09
1
Computation of AIC for gls models
Dear Colleagues,
We are using the phylog.gls.fit() function from the R package "PHYLOGR" (Diaz-Uriarte R, Garland T: PHYLOGR: Functions for phylogenetically based statistical analyses. 2007. Available at [http://cran.r-project.org/web/packages/PHYLOGR/index.html]) to correct for lack of independence between data points. (In our particular case, the lack of independence is due to
2011 Jun 22
0
GLS models and variance explained
Dear list,
Inspecting residuals of my linear models, I detected spatial autocorrelation.
In order to take this into account, I decided to use the GLS method
with the correlation = corGaus ( ~ X + Y).
Then, I can sort my GLS models based on their AIC.
But ... how to know the proportion of the variance explained by the
best one (it can be best of the worst models) ?
R-squared value has not the
2010 May 28
1
latex.rms and models fit with GLS
Hi,
I have fit a model using the rms package with the Gls() function.
Is there a way to get the model estimates, std errors, and p-values (i.e. what
you get with print(fit)) into latex format?
I have tried:
f <- Gls(...)
latex(f, file='')
... but I get the following error
Error in replace.substring.wild(s, old, new, test = test, front = front, :
does not handle > 1 * in
2005 Sep 08
1
Time series ARIMAX and multivariate models
Dear List,
The purpose of this e-mail is to ask about R time series procedures - as a
biologist with only basic time series knowledge and about a year's
experience in R.
I have been using ARIMAX models with seasonal components on seasonal data.
However I am now moving on to annual data (with only 34 time points) and
understand that ARIMA is not suitable for these shorter time periods -
does
2008 May 09
1
Which gls models to use?
Hi,
I need to correct for ar(1) behavior of my residuals of my model. I noticed
that there are multiple gls models in R. I am wondering if anyone
has experience in choosing between gls models. For example, how
should one decide whether to use lm.gls in MASS, or gls in nlme for
correcting ar(1)? Does anyone have a preference? Any advice is appreciated!
Thanks,
--
Tom
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2003 Oct 31
0
strange logLik results in gls (nlme)
I am trying to analyse a data with gls/lm using the following set of models
prcn.0.lm <- lm( log10(Y)~(cond-1)+(cond-1):t ,prcn)
prcn.1.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn,cor=corAR1())
prcn.0.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn)
prcn.1m.gls <- gls( log10(Y)~(cond-1)+(cond-1):t ,prcn,cor=corAR1(),method="ML")
I get the following AICs for these models:
2004 Sep 03
0
ML vs. REML with gls()
Hello listmembers,
I've been thinking of using gls in the nlme package to test for serial
correlation in my data set. I've simulated a sample data set and have
found a large discrepancy in the results I get when using the default
method REML vs. ML.
The data set involves a response that is measured twice a day (once for
each level of a treatment factor). In my simulated data set, I
2004 Dec 06
2
Missing Values
I have just started using R for my PhD. I am importing my data from Excel
via notepad into Word. Unfortunately, my data has many missing values. I
have put '.' and this allowed me to import the data into R. However, I
now want to interpolate these missing values. Please can someone give me
some pointers as to the method/code I could use?
Thankyou,
Lillian.
2010 Jul 08
0
Psudeo R^2 (or other effect size) in spatial gls regressions
Dear all,
I have been using the function gls in the package nlme in R to fit some spatial
regressions (as described in Dormann et al.). However, I have been struggling
trying to find a way to calculate a measure of effect size from these models, so
I wanted to know if any of you had an idea on how to do this.
More precisely, I am producing a multiple model with an exponential correlation
2002 Apr 14
0
gls
Dear all, I am confused.
I have encountered some strange behaviour of gls
> data(co2)
> co2.y <- aggregate(co2,1,mean)
> co2.y.data <- data.frame(co2=as.numeric(co2.y),year=seq(1959-1980,along=co2.y))
> co2.1.gls <- gls(co2~year+I(year^2), co2.y.data)
> co2.2.gls <- update(CO2.1.gls, corr=corAR1())
> summary(CO2.2.gls)
> plot(CO2.2.gls)
plot shows standardized
2011 Nov 22
0
Error in gls function in loop structure
Hi, r-users
I got a problem when I try to call a *gls* function in loop structure.
The gls function seems not able to recognize the parameters that I pass
into the loop function!
(But, if I use lm function, it works.)
The code looks like this:
=================================================
gls.lm <- function(Data, iv1, dv1)
{
gls.model <- gls(Data[ , dv1] ~ Data[ , iv1], correlation =