search 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