search for: pdsymm

...y nlme. However, I have yet to get even a simple example to work. No doubt this is because I am confusing my syntax, but I would appreciate any guidance as to how. I have studied Pinheiro & Bates carefully (though it's always possible I've missed something), the few posts mentioning pdSymm (some of which suggest lme is suboptimal here anyway) and ?pdSymm (which has only a trivial example, see later) but have not yet found a successful example of syntax for this particular problem. I am using the pdSymm class to specify a positive definite matrix corresponding to the covariance s...

Hi Folks, I'm trying to understand the model specification formalities for 'lme', and the documentation is leaving me a bit confused. Specifically, using the example dataset 'Orthodont' in the 'nlme' package, first I use the invocation given in the example shown by "?lme": > fm1 <- lme(distance ~ age, data = Orthodont) # random is ~ age Despite the

Dear all, I am estimating a mixed-model in Ubuntu Raring (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 mach...

...subject=pt g gcorr v vcorr; run; The key to specifying different covariance structure for the random effects seems to be the highlighted portion in the code. What would be it's equivalent in R? In R, I tried the following Model1 <- lme(y ~ g1+Tg1+g2+Tg2-1,random = pdBlocked(list(pdSymm(~g1+Tg1-1),pdSymm(~g2+Tg2-1))),data=X.gr,control=c on) where, g1 and g2 are the group indicator variables and Tg1 = t*(grp==1), similarly Tg2 is defined. I am forced to assume different intercept for each group in this approach but anyway this works and gives me some output. Now, I have 5...

Moi! I was helping to teach a course on mixed models this week, and we came across a problem with coding more than one random effect in lme when they aren't nested. As an example, suppose we have an experiment where we sample moths from several populations, and place the moths on different trees, and measure a trait (in this case survival of offspring, but that's less important). We

Hi, I am trying to install the nlme package, but during installation I get the message <snip> pdSymm text html latex example plot.ACF text html latex example missing link(s): xyplot plot.Variogram text html latex example missing link(s): xyplot plot.augPred text...

...xed=y~x*time, random=pd, weights=varIdent(form=~1|subgroup), na.action=na.omit, data=G.Data) # END R CODE Unfortunately, the pdDiag command constrains the random intercepts and slopes among all groups to be independent. The default class for pdMat, pdSymm, assumes that ALL random intercept and slope parameters are correlated. The reality is that the intercepts and slopes should be correlated within each subgroup, but these random parameters are independent between the subgroups. Use of the pdSymmm class leads to parameter non-identifiability. What...

The print method for lme *prints out* the fitted correlation matrix for the random effects. Is there any way to get these values as an object in R? I have examined the components of the lme object (called "junk" in the example below) and the components of summary(junk) without finding these numbers. (How I did this: I dumped the entire lme object to a text file and then used egrep to

...> relationship matrix whose elements are equal to the genetic > correlation between individuals. > > The test data is simulated to have a heritibility of 0.8 (i.e. 80% of > the variation can be explained by genetic effects) > > My initial attempt has been: > > CM<-pdSymm(AGRM, ~1|animal) > lme(trait~1, data=pedigree, CM) > > but which ever combination I use I always get "invalid > formula for groups". > > Does any one know how to code this sort of model? > > Thanks for your help, > > Jarrod Hadfield. > > copy and...

...he matrix. I have tried to reproduce this using lme, but it is unclear from the documentation (and Pinheiro & Bates text) how the pdBlocked and compound symmetric structure can be combined. fit.lme<-lme(expression~End+logpgc,random=list(RefSeqID=pdBlocked(list (~1,~ProbeID-1),pdClass="pdSymm")),data=dataset,correlation=corCompSym m(form=~1|RefSeqID/ProbeID/ProbeNo)) The point estimates are essentially the same comparing R and SAS for the fixed effects, but the 95% confidence intervals are much shorter using lme(). In order to find the difference in the algorithms used by SAS and...

Dear R-users Coming from a proc mixed (SAS) background I am trying to get into the use of (n)lme. In this connection, I have some (presumably stupid) questions which I am sure someone out there can answer: 1) With proc mixed it is easy to get a hold on the estimated variance parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the

...he matrix. I have tried to reproduce this using lme, but it is unclear from the documentation (and Pinheiro & Bates text) how the pdBlocked and compound symmetric structure can be combined. fit.lme<-lme(expression~End+logpgc,random=list(RefSeqID=pdBlocked(list (~1,~ProbeID-1),pdClass="pdSymm")),data=dataset,correlation=corCompSym m(form=~1|RefSeqID/ProbeID/ProbeNo)) The point estimates are essentially the same comparing R and SAS for the fixed effects, but the 95% confidence intervals are much shorter using lme(). In order to find the difference in the algorithms used by SAS and...

...ry=gmail&source=g>=0,D.a3=0) try({withTimeout(nlme(model=y~fpl.B.range(x,exp(1),A,B,C,D), control=nlmeControl(maxIter=50,pnlsMaxIter=7,msMaxIter=50,niterEM=25), data=dat, na.action=na.omit, fixed=myFormula,random=list(f.block=pdSymm(A+B+C+D~1)), start=INIT),timeout=10)}) [[alternative HTML version deleted]]

HI, I would like to extract the variance components estimation in lme function like a.fit<-lme(distance~age, data=aaa, random=~day/subject) There should be three variances \sigma_day, \sigma_{day %in% subject } and \sigma_e. I can extract the \sigma_e using something like a.fit$var. However, I cannot manage to extract the first two variance components. I can only see the results in

Hi folks, I was wondering how to run a mixed models approach to analyze a linear regression with a user-defined covariance structure. I have my model y = xa +zb +e and b ~ N (0, C*sigma_square). (and a is a fixed effects) I would like to provide R the C (variance-covariance) matrix I can easily provide an example, but at this point I am first trying to know what is the best package the

Dear helpers, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Hi al, I have a dataset (see attached), which basically involves 4 treatments for a chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy were taken at 2 time points. So each subject has 4 data points (from 2 biopsy locations and 2 time points). The objective is to study treatment difference.? I used lme to fit a mixed model that uses "biopsy.site nested within pid"

...at), mean = 0, sd = 0.1) try({withTimeout(nlme(model=y~fpl.B.range(x,exp(1),A,B,C,D), control=nlmeControl(maxIter=50,pnlsMaxIter=7,msMaxIter=50,niterEM=25), data=dat, na.action=na.omit, fixed=myFormula,random=list(f.block=pdSymm(A+B+C+D~1)), start=INIT),timeout=10)}) } [[alternative HTML version deleted]]