Displaying 20 results from an estimated 106 matches for "annale".
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2004 Oct 04
3
(off topic) article on advantages/disadvantages of types of SS?
Hello. Please excuse this off-topic request, but I know that the
question has been debated in summary form on this list a number of
times. I would find a paper that lays out the advantages and
disadvantages of using different types of SS in the context of
unbalanced data in ANOVA, regression and ANCOVA, especially including
the use of different types of contrasts and the meaning of the
2008 Jul 21
2
Time Series - Long Memory Estimation
Dear R-Users,
I am doing a research on Time Series, especially on the estimation of the
fractional exponent in long memory time series (for those who know).
However there are three estimators already built-in the fracdiff package
(GPH, Sperio, MLE)
I was wondering if there is someone who had used an estimation introduced by
P.M. Robinson (related paper: "Log-Periodogram regression of time
2009 Mar 12
3
avoiding termination of nls given convergence failure
Hello. I have a script in which I repeatedly fit a nonlinear regression to
a series of data sets using nls and the port algorithm from within a loop.
The general structure of the loop is:
for(i in 1:n){
… extract relevant vectors of dependent and independent variables …
… estimate starting values for Amax and Q.LCP…
2011 Jan 07
2
survval analysis microarray expression data
For any given pre-specified gene or short list of genes, yes the Cox
model works fine. Two important caveats:
1. Remeber the rule of thumb for a Cox model of 20 events per variable
(not n=20). Many microarray studies will have very marginal sample
size.
2. If you are looking at many genes then a completely different strategy
is required. There is a large and growing literature; I like Newton
2004 Apr 26
2
mixed model with binomial link?
Hello. I have to fit a mixed model from a repeated measures split-plot
experiment in which the response variable is binary. This requires a
generalised linear mixed model in which I can specify a binomial
distribution. I can’t find the appropriate package in R. I have looked
at glmmML, but it doesn’t seem to allow any mixed structure beyond a
simple 2-level one. Can anyone point me to the
2004 Sep 30
1
histograms with more than one variable
Hello. I want to plot the distribution of a continuous variable (y) in
each of two groups on the same graph as histograms. I suppose one could
call this a 2-d histogram? Can this be done in R? Here is a typical
data.set:
y group
1.2 1
3.3 1
2.4 2
5.7 1
0.2 2
etc.
Bill Shipley
Subject Matter Editor, Ecology
North American Editor, Annals of
2006 Apr 13
1
obtaining residuals from lmer
Hello. I cannot find out how to extract the residuals from a mixed model
using the lmer function. Can someone help?
Bill Shipley
North American Editor, Annals of Botany
Editor, "Population and Community Biology" series, Springer Publishing
Département de biologie, Université de Sherbrooke,
Sherbrooke (Québec) J1K 2R1 CANADA
Bill.Shipley@USherbrooke.ca
2009 Jul 08
1
Dantzig Selector
Hi,
I was wondering if there was an R package or routines for the Dantzig
Selector (Candes & Tao, 2007). I know Emmanuel Candes has Matlab
routines to do this but I was wondering if someone had ported those to
R.
Thanks,
T
---Reference---
@article{candes2007dantzig,
title={{The Dantzig selector: statistical estimation when p is much
larger than n}},
author={Candes, E. and Tao, T.},
2005 Oct 17
1
mauchly.test (instead of mauchley.test) ?
Wherever I look up the following reference
the name of the author is spelled Mauchly
contrary to the naming of the R function.
Mauchly, J.W.,
Significance test for sphericity
of a normal $n$-variate distribution,
Annals of mathematical statistics, 11(1940),
p. 204-209.
Is this a typo on the original article (which
R Core has corrected) or is it a typo in the
function name ?
Best regards,
Tobias
2005 May 06
0
FW: distance between distributions
Sorry, forgot to send this to the list originally.
-----Original Message-----
From: Mike Waters [mailto:dr.mike at ntlworld.com]
Sent: 06 May 2005 18:40
To: 'Campbell'
Subject: RE: [R] distance between distributions
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Campbell
Sent: 06 May 2005 11:19
To:
2006 Feb 16
1
help downloading lme4 from CRAN
Hello. I am having trouble downloading the lme4 package from the CRAN
site. The error is:
> local({a <- CRAN.packages()
+ install.packages(select.list(a[,1],,TRUE), .libPaths()[1],
available=a, dependencies=TRUE)})
trying URL `http://cran.r-project.org/bin/windows/contrib/2.0/PACKAGES'
Content type `text/plain; charset=iso-8859-1' length 26129 bytes
opened URL
downloaded 25Kb
2008 Jan 11
1
Adding weights to ecdf
I would like you consider that the function ecdf
could be extended in the following way to handle weights
when computing Empirical distribution Functions. There
exist particular cases that supports this kind of
extension, see for example:
Rao, C. R., 1997.
Statistic and True. Putting chance to work.
World Scientific Publishing.
Cox, D. R., 1969.
Some Sampling Problems in Technology.
New
2003 Oct 28
1
setting up complicated ANOVA in R
Hello. I am about to do a rather complicated analysis and am not sure
how to do it. The experiment has a split-plot design and also repeated
measures. Both of these complications require one to define an error
term and it seems that one cannot specify two such terms. The
split-plot command is:
aov(y~covariates +A*B+Error(C), data=) where A and B are the fixed
effects and C is the
2004 Oct 22
1
p-values for the dip test
Hi all,
I am using Hartigan & Hartigan's [1] "dip test" of unimodality via the
diptest package in R. The function dip() returns the value of the test
statistic but I am having problems calculating the p-value associated with
that value. I'm hoping someone here is familiar with this process and can
explain it.
In the original article there is an example using n=63 and a
2004 Apr 01
1
nls function
Hello. I am trying to fit a non-rectangular hyperbola function to data
of photosynthetic rate vs. light intensity. There are 4 parameters that
have to be estimated. I find the nls function very difficult to use
because it often fails to converge and then gives out cryptic error
messages. I have tried playing with the control parameters but this
does not always help.
Is there another
2003 Nov 04
3
help with lme()
Hello. I am trying to determine whether I should be using ML or REML
methods to estimate a linear mixed model. In the book by Pinheiro &
Bates (Mixed-effects models in S and S-PLUS, page 76) they state that
one difference between REML and ML is that « LME models with different
fixed-effects structures fit using REML cannot be compared on the basis
of their restricted likelihoods. In
2005 Jan 05
1
cubic spline smoother with heterogeneous variance.
Hello. I want to estimate the predicted values and standard errors of
Y=f(t) and its first derivative at each unique value of t using the
smooth.spline function. However, the data (plant growth as a function
of time) show substantial heterogeneity of variance since the variance
of plant mass increases over time. What is the consequence of such
heterogeneity of variance in terms of bias in the
2003 Dec 11
2
typeIII SS for lme?
To avoid angry replies, let me first say that I know that the use of
Type III sums of squares is controversial, and that some statisticians
recommend instead that significance be judged using the non-marginal
terms in the ANOVA. However, given that type III SS is also demanded by
some… is there a function (equivalent to drop1 for lm) to obtain type
III sums of squares for mixed models using the
2008 May 06
1
Type I or III SS with mixed model function lme
Hello, I have come across a result that I cannot explain, and am hoping that
someone else can provide an answer. A student fitted a mixed model using
the lme function: out<- lme(fixed=Y~A+B+A:B, random=~1|Site). Y is a
continuous variable while A and B are factors. The data set is balanced
with the same number of observations in each combination of A and B. There
are two hierarchical
2005 Feb 15
1
shrinkage estimates in lme
Hello. Slope estimates in lme are shrinkage estimates which pull the
OLS slope estimates towards the population estimates, the degree of
which depends on the group sample size and the distance between the
group-based estimate and the overall population estimate. Although
these shrinkage estimates as said to be more precise with respect to the
true values, they are also biased. So there is a