search for: varstruct

...data=data,weights=varConstPower(const=1,power=0,form= ~fitted(.)))); #Examine results model3; #const = .6551298, power = .8913665 summary(model3); #const = .6551298, power = .8913665 coef(model3$modelStruct$varStruct)["power"]; # power = .8913665 coef(model3$modelStruct$varStruct)["const"]; #const = -0.4229219 coef.varFunc(model3$modelStruct$varStruct); #R can't seem to find this function, Splus can Any advice on what I am doing wrong would be appreciated....

...df.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: > exp(coef(model3$modelStruct$varStruct)["const"]) const 0.6551298 Does that answer the question about not understanding the connection between summary(model3) and coef(model3$modelStruct$varStruct)["const"]? Regarding the question about R not being able to find 'coef.varFunc', I tried the follo...

R 1.6.2 for Windows, Win2k: I have fitted a weighted least squares model using the code "wls.out <- gls(y ~ x1 + x2 + x3 + x4 + x5 + x6 - 1, data = foo.frame, weights = varConstPower(form = ~ fitted(.), fixed = list(power = 0.5), const = 1))" The data has 62 rows and the response is zero when the covariates are zero. The purpose of the model was to account for the the fact that

...ated 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: y<-summary(fm) x<-y$modelStruct$varStruct > x Variance function structure of class varConstPower representing const power 1.184535e-15 1.000000e+00 These are the constants and power that apppear in the summary(fm), and that I want to extract. Hower I got different value of const when extracting from x: > x$const...

...essian matrix is not positive definite or something like that (i am not quite sure), the intervals function delivers an error message. Thus, i will like to ask if there is another way to access these values. I tried using the following code: hh$modelStruct$corStruct[1] hh$modelStruct$varStruct[1] Rather the output was: > hh$modelStruct$corStruct[1] [1] -1.308580 > hh$modelStruct$varStruct[1] [1] -0.03124255 I presume there is a way to calculate the correlation and variance function coefficients using these values. Could you tell me how to access those value...

...essian matrix is not positive definite or something like that (i am not quite sure), the intervals function delivers an error message. Thus, i will like to ask if there is another way to access these values. I tried using the following code: hh$modelStruct$corStruct[1] hh$modelStruct$varStruct[1] Rather the output was: > hh$modelStruct$corStruct[1] [1] -1.308580 > hh$modelStruct$varStruct[1] [1] -0.03124255 I presume there is a way to calculate the correlation and variance function coefficients using these values. Could someone tell me how to access those v...

...alpha0=0.1 alpha1=3 m=200 n=200 c0=-2.413; nu0=-0.2; nu1=0.3 simu.dat=simulate.pilot(m, mn, sigma, alpha0, alpha1, c0, nu0, nu1) fit1=try(gls(Y~W, data=simu.dat, weights=varComb(varExp(form=~W), varExp(form=~I(W^2))))) c0.hat= log(fit1$sigma^2) nu0.hat=2*fit1$modelStruct$varStruct$A[1] nu1.hat=2*fit1$modelStruct$varStruct$B[1] > c0.hat [1] -1.570104 > nu0.hat [1] -0.787264 > nu1.hat [1] 0.4057129 > Thanks Xuesong CONFIDENTIALITY NOTICE: This e-mail message, including a...{{dropped:12}}

...sigma=sqrt(0.5) alpha0=0.1 alpha1=3 m=200 n=200 c0=-2.413; nu0=-0.2; nu1=0.3 simu.dat=simulate.pilot(m, mn, sigma, alpha0, alpha1, c0, nu0, nu1) fit1=try(gls(Y~W, data=simu.dat, weights=varComb(varExp(form=~W), varExp(form=~I(W^2))))) c0.hat= log(fit1$sigma^2) nu0.hat=2*fit1$modelStruct$varStruct$A[1] nu1.hat=2*fit1$modelStruct$varStruct$B[1] > c0.hat [1] -1.570104 > nu0.hat [1] -0.787264 > nu1.hat [1] 0.4057129 > Thanks Xuesong

...hought 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)$weights f2 <- lm(Petal.Width ~ Species / Petal.Length, data = iris, weights = aa) summary(f1)$tTable; summary(f2) So, the two models with the very same weights do differ (in terms of standard errors). Could you please explain why? Are these different types of weights? Many thanks in advance,...

...version.string R version 2.6.0 (2007-10-03) start=c(-1.5, 9.5, 0.09, 10.25) names(start)<-c("A","B","xmid","scal") gnls(response~SSllogis(conc,A,B,xmid,scal),tdat,start=start,weights=varPower(),verbose=TRUE) **Iteration 1 GLS step: Objective: NULLvarStruct parameters: power 0.3373199 NLS step: RSS = 0 model parameters:-0.799941 8.99983 -0.522623 212.314 iterations: 2 Convergence: params varStruct 1.172208 1.000000 ### After about 10 min hit cntrl C Warning messages: 1: In log(xmid) : NaNs produced 2: In log(xmid) : NaNs pro...

...ts levels) I used function contrast (package contrast) whit following script to probe the hypotesis 1.: con<-contrast(FINAL, list(Lugar= 'Xmatkuil', Tra=1), list(Lugar='Xmatkuil', Tra = 0)) but i found this error message: Error en gendata.default(fit = list(modelStruct = list(varStruct = c(-0.253689933940456, : not enough factors I would be grateful if somebody tell me I'm doing wrong with my contrast function script. Thanks in advance, Marylin Bejarano PHd candidate in Ecology Institute of Mexico's National Autonomous University [[alternative HTML version delete...

...------------------------------------------------------------------------------------------------- Can somebody tell me how to access the values in blue above? Also, when i tried accessing these values i obtained the following bm$modelStruct corStruct parameters: [1] -1.394879 varStruct parameters: [1] 0.5680815 What do these values represent. Thanks in advance.. Pryseley sample data: RowNames Trial subject VISUAL0 TRT VISUAL24 VISUAL52 TREAT outcome endpoint trt 4 1 1003 65 4 65 55 2 0 1 1 8 1...

...mula. It should not be part of the data frame, as it is a vector of a different length. How can this be done? The gls() code suggests it may be difficult to keep the auxillary arg out of the model.frame but in the evaluation frame of the formula: glsSt <- glsStruct(corStruct = correlation, varStruct = varFunc(weights)) mfArgs <- list(formula = asOneFormula(formula(glsSt), model, groups), data = data, na.action = na.action) if (!missing(subset)) { mfArgs[["subset"]] <- asOneSidedFormula(Call[["subset"]])[[2]] } mfArgs$drop.unused.levels...

...to eliminate the >method entirely. Any objections to my doing that? > >The method is currently defined as > >getVarCov.gls <- > function(obj, individual = 1, ...) >{ > S <- corMatrix(obj$modelStruct$corStruct)[[individual]] > if (!is.null( obj$modelStruct$varStruct)) > { > ind <- obj$groups==individual > vw <- 1/varWeights(obj$modelStruct$varStruct)[ind] > } > else vw <- rep(1,nrow(S)) > vars <- (obj$sigma^2)*vw > result <- t(S * sqrt(vars))*sqrt(vars) > class(result) <- c(&...

Hello: I have an lme object, say lme_res2, which was generated using the varIdent. I'm trying to extract the double 1.532940 from the object, but I can't find it by attributes(lme_res2) or attributes(summary(lme_res2)). How can I pull it out (so that I can save it to another variable)? Thanks. Shin Linear mixed-effects model fit by REML Data: dat Log-restricted-likelihood:

Hi, lme() users, Can some one tell me how to do this. I model Orthodont with the same G for random variables, but different R{i}'s for boys and girls, so that I can get sigma1_square_hat for boys and sigma2_square_hat for girls. The model is Y{i}=X{i}beta + Z{i}b + e{i} b ~ iid N(0,G) and e{i} ~ iid N(0,R{i}) i=1,2 orth.lme <- lme(distance ~ Sex * age, data=Orthodont, random=~age|Subject,

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess

...-------------------------------------------------------------------- ---------------------------- Can somebody tell me how to access the values in blue above? Also, when i tried accessing these values i obtained the following bm$modelStruct corStruct parameters: [1] -1.394879 varStruct parameters: [1] 0.5680815 What do these values represent. Thanks in advance.. Pryseley sample data: RowNames Trial subject VISUAL0 TRT VISUAL24 VISUAL52 TREAT outcome endpoint trt 4 1 1003 65 4 65 55 2 0 1 1 8 1...