similar to: question regarding variance function in gls

Dear R-help members, I have a question regarding how to use varComb function to specify a variance function for the "weights" in the gls. I need to fit a linear model with heteroscedasticity. The variance function is exp(c0+nu0*W +nu1*W^2) where W is a covariate. Initially I want to use varFunc to define my own variance function following the instruction in the Pinheiro and

Hi, I am trying to add multiple variance structures such as the first example below: vf1 <- varComb(varIdent(form = ~1|Sex), varPower()) However my code below will not work can anybody please advise me? VFcomb<-varComb(varExp(form=~depcptwithextybf),varFixed(form=~FebNAO)) also if you have two variables with the same weights function would you write that as:

Hello - I'm trying to modify an option for the lme() or nlme() macros. I want to write my own specification for the variance function and am following homework problem 4, Chapter 5, page 268 of Pinheiro and Bates book on mixed effect. I'm up to point where I've created a new class using an existing variance function class, varExp as a template. Next I need to write an

Hello. I am trying my hand at modifying the varFunc class varExp, but I must be missing a step. All I want to do right now is make a working copy of varExp, call it varExp2, and then later change it. coef.varExp2, coef<-.varExp2, and Initialize.varExp2 all seem to work properly after I construct them. I can successfully use the commands: v2 <- varExp2(form = ~age|Sex,fixed =

Works perfectly. Thank you. -Hugh Rand -----Original Message----- From: Spencer Graves [mailto:spencer.graves at pdf.com] Sent: Sunday, January 15, 2006 6:41 PM To: Rand, Hugh Cc: 'r-help at lists.R-project.org' Subject: Re: [R] trouble with extraction/interpretation of variance structure para meters from a model built using gnls and varConstPower How about this: >

Dear list, I was trying to create a VarClass for nlme to work with Fay-Herriot (FH) models. The idea was to create a modification of VarComb that instead of multiplying the variance functions made their sum (I called it varSum). After some fails etc... I found that the I was not getting the expected results because I needed to make sigma fixed. Trying to find how to make sigma fixed I run into

I have been using gnls with the weights argument (and varConstPower) to specify a variance structure for curve fits. In attempting to extract the parameters for the variance model I am seeing results I don't understand. When I simply display the model (or use "summary" on the model), I get what seem like reasonable values for both "power" and "const". When I

Warning: I am a complete newbie to R. I have read ISwR, but I am still finding myself completely stuck on some simple concepts. I have tried everything I can think of to solve this one, and finally decided that enough was enough and I need a pointer to a solution. I have the following summary from lm(): ---- > summary(lm(nu1~nu4)) Call: lm(formula = nu1 ~ nu4) Residuals: Min 1Q

Hi all, I wondered whether anyone has some advice on a stats-related 'sanity check', as I ran a nonparametric multivariate test (mulrank function as decribed by R. Wilcox, 2005) on both systems, but got different results (please see below for the system-specific outputs)! The functions I used are attached as well. Any advice would be much appreciated! Thanks in advance for getting back to

Dear lmer users, The experiment includes 15 groups of (3 males and 1 female). The female is characterized by its quality Q1 and Q2. Each male of a group is characterized by the number of MatingAttempts (with Poisson distribution). I want to examine if male mating attempts depend on female quality. I can see from graphic exploration that the within-group heterogeneity of male attempts increases

Dear All, I am using the function gls (in the nlme package) and I would like to fit a heteroscedastic model, with different variances for each of the levels of two stratification variables. In p. 210 of Pinheiro & Bates ("Mixed effects models in S and S-Plus", 2000, Springer), the authors show the use of the "*" operator. However, that is not what I want, because it

Dear R-Users I have a problem accessing some values in the output from the summary of an lme fit. I fit the model below: ggg <- lme (ST~ -1 + as.factor(endp):Z.sas + as.factor(endp), data=dat4a, random=~-1 + as.factor(endp) + as.factor(endp):Z.sas|as.factor(trials), correlation = corSymm(form=~1|as.factor(trials)/as.factor(id)), weights=varIdent(form=~1|endp)) hh

Background: I administer a cluster of RedHat EWS 3U4 Linux workstations at a university. I built R 2.1.0 from source: ./configure \ --prefix=/sscc/opt/R-2.1.0 \ --with-blas=no \ 2>&1 \ | tee NUInstall.configure R is now configured for i686-pc-linux-gnu Source directory: . Installation directory: /sscc/opt/R-2.1.0 C compiler:

Good day R-Users, I have a problem accessing some values in the output from the summary of an lme fit. The structure of my data is as shown below (I have attached a copy of the full data). id trials endp Z.sas ST 1 1 -1 -1 42.42884 1 1 1 -1 48.12007 2 1 -1 -1 43.42878 2 1 1 -1

Dear R-Users, I have a question concerning extraction of parameter estimates of variance function from lme fit. To fit my simulated data, we use varConstPower ( constant plus power variance function). fm<-lme(UPDRS~time,data=data.simula,random=~time,method="ML",weights=varConstPower(fixed=list(power=1))) I extract the results of this function by using the following codes:

Dear R users The following function is R code for the main compuations in the article: M. Eliasziw, S Lorraine Young, M Gail Woodbury and Karen Fryday-Field (1994): Statistical Methodology for the Concurrent Assessment of Intrarater and Intrarater Reliability: Using Goniometric Measurements as an Example. Physical Therapy 74 (8); 777-788 The function gives the estimated inter- and intrarater

Dear R users; I was trying to fit a nonlinear model using gnls (nlme version 3.1-80, R 2.5.0, WinXP) and I got the following error and warning message: Error in gnls(ht ~ a1 * hd * (1 - a2 * exp(-a3 * (dbh/dq2))), data = hdat, : Step halving factor reduced below minimum in NLS step In addition: Warning message: $ operator is deprecated for atomic vectors, returning NULL in:

Hi all, I understand that gls() uses generalized least squares, but I thought that maybe optimum weights from gls might be used as weights in lm (as shown below), but apparently this is not the case. See: library(nlme) f1 <- gls(Petal.Width ~ Species / Petal.Length, data = iris, weights = varIdent(form = ~ 1 | Species)) aa <- attributes(summary(f1)$modelStruct$varStruct)$weights f2 <-

Hi, I am trying to plot the original data with the line of the model using the predict function. I want to add SE to the graph, but not sure how to get them out as the predict function for gls does not appear to allow for SE=TRUE argument. Here is my code so far: f1<-formula(MaxNASC40_50~hu3+flcmax+TidalFlag) vf1Exp<-varExp(form=~hu3) B1D<-gls(f1,correlation=corGaus(form=Lat~Lon,

Hi, I am not sure this is a bug but I can repeat it, The functions and data are below. I know this is nasty data, and it is very questionable whether a 4pl model is appropriate, but it is data fed to an automated tool and I would have hoped for an error. Does this repeat for anyone else? My details: > version _ platform i686-pc-linux-gnu