search for: apvar

...ntrol to change to get convergence. Cheers Dan Coleman Genentech Inc. > gnlsControl function (maxIter = 50, nlsMaxIter = 7, msMaxIter = 50, minScale = 0.001, tolerance = 1e-06, nlsTol = 0.001, msTol = 1e-07, msScale = lmeScale, returnObject = FALSE, msVerbose = FALSE, apVar = TRUE, .relStep = (.Machine$double.eps)^(1/3), nlmStepMax = 100, opt = c("nlminb", "optim"), optimMethod = "BFGS", minAbsParApVar = 0.05) { list(maxIter = maxIter, nlsMaxIter = nlsMaxIter, msMaxIter = msMaxIter, minScale = minScale, tolera...

...ed random intercept terms. I want to save the predicted values from that model and show the log curve in a plot ; predicted~log(x) mod<-lme(B~log(x)+as.factor(y), random=~1|cohort/Study, weights=varFixed(~I(SE^2)), na.action=na.omit, data=subset(meta), control = lmeControl(sigma = 1, apVar = FALSE)) summary(mod) newdat <- data.frame(x=seq(min(meta$x), max(meta$x),,118)) # I have 118 observations. #How do I add the factor variable to my newdat? newdat$pred <- predict(mod, newdat,level = 0,type="response") plot(B ~ x, data=meta) lines(B ~ x, data=newdat) Can you ple...

...13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite approximate variance-covariance" *However*, if I do the same on a Windows or a Mac OS X machine, I get: > fm0$apVar reStruct.subj1 reStruct.subj2 reStruct.subj3 reStruct.subj1 reStruct.subj1 1.952757e-01 3.130089e-01 5.766955e-01 -0.1...

My apologies if this is obvious: Is there a simple way (other than simulation or bootstrapping) to obtain a (approximate)confidence interval for the ratio of 2 variance components in a fitted lme model? -- In particular, if there are only 2 components (1 grouping factor). I'm using nlme but lme4 would be fine, too. -- Bert Gunter Genentech Non-Clinical Statistics South San Francisco, CA

...'d like to be able to fit the model using gnls. The format is a little different, but I get an error when I use the following syntax: gnls(Y~log(exp(a0-a1*X)+exp(b0-b1*X)),params=a0+a1+b0+b1~K,start=list(rep(c (6.02,0.2,4.5,0.001),16)),data=data.frame(Y=y,X=x,K=k),control=list (msVerbose=TRUE,apVar=FALSE,returnObject=TRUE)) Error in gnls(Y ~ log(exp(a0 - a1 * X) + exp(b0 - b1 * X)), params = a0 + : Approx. covariance matrix for parameter estimates not of full rank I assume that I'm getting the format of my starting values wrong. Any suggestions would be greatly appreciated....

...gt; I want to save the predicted values from that model and show the log curve > in a plot ; predicted~log(x) > > mod<-lme(B~log(x)+as.factor(y), random=~1|cohort/Study, > weights=varFixed(~I(SE^2)), na.action=na.omit, data=subset(meta), > control = lmeControl(sigma = 1, apVar = FALSE)) > summary(mod) > > newdat <- data.frame(x=seq(min(meta$x), max(meta$x),,118)) # I have 118 > observations. #How do I add the factor variable to my newdat? > newdat$pred <- predict(mod, newdat,level = 0,type="response") > > plot(B ~ x, data=meta) >...

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

Dear listers, I can't find the variance or se of the coefficients in a multilevel model using lme. I want to calculate a Chi square test statistics for the variability of the coefficients across levels. I have a simple 2-level problem, where I want to check weather a certain covariate varies across level 2 units. Pinheiro Bates suggest just looking at the intervals or doing a rather

...he predicted values from that model and show the log curve >> in a plot ; predicted~log(x) >> >> mod<-lme(B~log(x)+as.factor(y), random=~1|cohort/Study, >> weights=varFixed(~I(SE^2)), na.action=na.omit, data=subset(meta), >> control = lmeControl(sigma = 1, apVar = FALSE)) >> summary(mod) >> >> newdat <- data.frame(x=seq(min(meta$x), max(meta$x),,118)) # I have 118 >> observations. #How do I add the factor variable to my newdat? >> newdat$pred <- predict(mod, newdat,level = 0,type="response") >> >>...

...deviance = Fixed effects + Random Effects + Residuals If yes how to do it ? A lme object provides the following: > names(glm6) [1] "modelStruct" "dims" "contrasts" "coefficients" [5] "varFix" "sigma" "apVar" "logLik" [9] "numIter" "groups" "call" "method" [13] "fitted" "residuals" "fixDF" "family" so no $null.deviance and $deviance elements as in glm obje...

...one knew if there was anything else in the control values I should try changing. Below are the defaults.. lmeControl function (maxIter = 50, msMaxIter = 50, tolerance = 1e-06, niterEM = 25, msTol = 1e-07, msScale = lmeScale, msVerbose = FALSE, returnObject = FALSE, gradHess = TRUE, apVar = TRUE, .relStep = (.Machine$double.eps)^(1/3), minAbsParApVar = 0.05, nlmStepMax = 100, optimMethod = "BFGS", natural = TRUE) I was reading on the R listserve that lmer from the lme4 package may be preferable to lme (for convergence problems) but lmer seems to need you to...

...dapt[r]<-sqrt(vcov(test.lme1)) else all.se.fix.coef.adapt[r]<-sqrt(vcov(glm)) est.ICC[r] <- as.numeric(VarCorr(test.lme1)[1,1])/( as.numeric(VarCorr(test.lme1)[1,1])+test.lme1$sigma^2) all.varcomp.g [r] <-as.vector(as.numeric(VarCorr(test.lme1)[1,1])) if (test.lme1$apVar[1] == "Non-positive definite approximate variance-covariance") { all.se.varcomp.g2[r] <- NA tstat2[r] <- 0 } else { all.se.varcomp.g2[r] <- sqrt(test.lme1$apVar[2,2]) tstat2[r]<- all.varcomp.g[r]/all.se.varcomp.g2[r] } all.se.varcom...

Hi R-users, I am using lme to fit a linear mixed model with the nlme package, does anyone know if it is possible to obtain standard error estimates of the variance components estimators and an adequate method to test the significance of the variance component? Thanks, Berta. [[alternative HTML version deleted]]

Hi, I have been trying glht() from multcomp package and contrast() from contrast package to test a contrast that I am interested in. With the following simulated dataset (fixed effect "type" with 3 levels (b, m, t), and random effect "batch" of 4 levels, a randomized block design with interaction), sometimes both glht() and contrast() worked and gave nearly the same p values;

...lt;Pine.LNX.4.33.0306261037560.6707-100000 at penguin.rand.org> Content-Type: TEXT/PLAIN; charset=US-ASCII > > Dear listers, > > I can't find the variance or se of the coefficients in a multilevel model > using lme. > The component of an lme() object called "apVar" provides the estimated asymptotic covariance matrix of a particular transformation of the variance components. Dr. Bates can correct me if I'm wrong but I believe it is the matrix logarithm of Cholesky decomposition of the covariance matrix of the random effects. I believe the details ar...