Displaying 20 results from an estimated 3000 matches similar to: "Psudeo R^2 (or other effect size) in spatial gls regressions"
2012 May 29
1
GLMMPQL spatial autocorrelation
Dear all,
I am experiencing problems using the glmmPQL function in the MASS package
(Venables & Ripley 2002) to model binomial data with spatial
autocorrelation.
My question - is the presence of birds affected by various hydrological
parameters?
Presence/absence data were collected from 83 sites and coupled against
hydrological data from the same site. The bird survey sampling effort
2006 Jul 01
1
nlme: correlation structure in gls and zero distance
Dear listers,
I am trying to model the distribution of fox density over years in the
Doubs department. Measurements have been taken on 470 plots in March
each year and georeferenced. Average density is supposed to be different
each year.
In a first approach, I would like to use a general model of this type,
taking spatial correlation into account:
2004 Mar 19
1
Spatial Statistics: surf.gls
In an experimental setup we obtain z-data samples at equidistant grid points.
The surf.gls (Kriging) algorithm produces an error under this circumstance when performing the Choleski decomposition.
A workaround is to dither the grid coordinates using (x <- rnorm(length(x)) ; y<- rnowm(length(y))).
Question: Is this an expected behaviour of the surf.gls function ?
Regards,
Berthold
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
2007 Jan 15
0
spatial correlaton in lme and gls
Hello,
I have multiple observations (replicates) per station...and the stations are distributed in space.
I've applied models using lme and gls from the nlme library. I've messed around with compound symmetry to capture the correlation within stations. My question is.....I have x and y coordinates for each station (the subsamples have nearly the same coordinates)...how do you
2009 Aug 24
1
lme, lmer, gls, and spatial autocorrelation
Hello folks,
I have some data where spatial autocorrelation seems to be a serious
problem, and I'm unclear on how to deal with it in R. I've tried to do my
homework - read through 'The R Book,' use the online help in R, search the
internet, etc. - and I still have some unanswered questions. I'd greatly
appreciate any help you could offer. The super-super short explanation is
2009 Sep 01
1
understanding the output from gls
I'd like to compare two models which were fitted using gls, however I'm
having trouble interpreting the results of gls. If any of you could offer
me some advice, I'd greatly appreciate it.
Short explanation of models: These two models have the same fixed-effects
structure (two independent, linear effects), and differ only in that the
second model includes a corExp structure for
2009 Sep 22
1
odd (erroneous?) results from gls
A couple weeks ago I posted a message on this topic to r-help, the response
was that this seemed like odd behavior, and that I ought to post it to one
of the developer lists. I posted to r-sig-mixed-models, but didn't get any
response. So, with good intentions, I decided to try posting once more, but
to this more general list.
The goal is (1) FYI, to make you aware of this issue, in case it
2005 Apr 11
0
correlation range estimates with nlme::gls
I'm trying to do a simple (?) analysis of a 1D spatial data set,
allowing for spatial autocorrelation. (Actually, I'm comparing expected
vs. observed for a spatial model of a 1D spatial data set.) I'm using
models like
gls(obs~exp,correlation=corExp(form=~pos),data=data)
or
gls(obs~exp,correlation=corLin(form=~pos),data=data)
This form is supposed to fit a linear model of
2005 May 17
0
problem with gls : combining weights and correlation structure
Dear R-users,
I hope you will have time to read me and I will try to be brief. I am also
sorry for my poor english.
I used gls function from the package nlme to correct two types of bias in my
database. At first, because my replicates are spatially aggregated, I would
like to fit a corStruct function like corLin, corSpher, corRatio, corExp or
corGaus in my gls model, and simultaneously,
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
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 =
2009 Mar 04
0
'anova.gls' in 'nlme' (PR#13567)
There is a bug in 'anova.gls' in the 'nlme' package (3.1-90). The=20
bug is triggered by calling the function with a single 'gls' object=20
and specifying the 'Terms' argument but not the 'L' argument:
> library(nlme)
> fm1Orth.gls <- gls(distance ~ Sex * I(age - 11), Orthodont,
+ correlation =3D corSymm(form =3D ~ 1 |
2011 Dec 12
0
Confidence intervals of gls function?
Dear gls-experts,
while reading and testing some examples of the book
"introductionary time series analysis with R",
I encountered the following fact which puzzles me.
Confidence intervals for global temperature time series (P99)
computed from general least squares (GLS) to fit the time series.
I repeat the example from the book and get the same results:
temp.gls=gls(temp ~
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
2005 Feb 02
0
Not reproducing GLS estimates
Dear List:
I am having some trouble reproducing some GLS estimates using matrix
operations that I am not having with other R procedures. Here are some
sample data to see what I am doing along with all code:
mu<-c(100,150,200,250)
Sigma<-matrix(c(400,80,16,3.2,80,400,80,16,16,80,400,80,3.2,16,80,400),n
c=4)
sample.size<-100
temp <-
2012 Apr 19
2
Gls function in rms package
Dear R-help,
I don't understand why Gls gives me an error when trying to fit a
model with AR(2) errors, while gls (from nlme) does not. For example:
library(nlme)
library(rms)
set.seed(1)
d <- data.frame(x = rnorm(50), y = rnorm(50))
gls(y ~ x, data=d, correlation = corARMA(p=2)) #This works
Gls(y ~ x, data=d, correlation = corARMA(p=2)) # Gives error
# Error in
2003 Sep 25
1
Error from gls call (package nlme)
Hi
I have a huge array with series of data. For each cell in the array I
fit a linear model, either using lm() or gls()
with lm() there is no problem, but with gls() I get an error:
Error in glsEstimate(glsSt, control = glsEstControl) :
computed gls fit is singular, rank 2
as soon as there are data like this:
> y1 <- c(0,0,0,0)
> x1 <- c(0,1,1.3,0)
> gls(y1~x1)