search for: pdlogchol

...=="Right",random=~1|Dog/Day/Record) This allows to determine the variance attributable to each factor. Record represents the within-day variation, Day represents the between-day variation. This gives the following results : VarCorr (Dog.Left) Variance StdDev Dog = pdLogChol(1) (Intercept) 564.55587 23.760384 Day = pdLogChol(1) (Intercept) 54.63027 7.391229 Record = pdLogChol(1) (Intercept) 23.29377 4.826362 Residual 27.46464 5.240672 VarCorr(Dog.Right) Variance StdDev Dog = pdLogChol(1)...

...oing. I am tryg to reproduce the nested analysis on page 368: model<-aov(Glycogen~Treatment/Rat/Liver + Error(Treatment/Rat/Liver), rats) using lme. The code: model1<- lme(Glycogen~Treatment, random = ~1|Rat/Liver, data=rats) VarCorr(model1) Variance StdDev Rat = pdLogChol(1) (Intercept) 20.6019981 4.538942 Liver = pdLogChol(1) (Intercept) 0.0540623 0.232513 Residual 42.4362241 6.514309 Does NOT give me the same variance componets I find in Crawley's book (page 371 onwards). The code: model2<- lme(Glycogen~Treatment, random =...

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious model with pdDiag replacing pdLogChol I cannot reproduce with lmer. The obvious choice for me would be my model lmer2, but this is yielding different result. Somebody any idea? Thanks, Joris...

...ee if they're significant or not) . But I'm unsure on how to proceed, I had done: allrandom <- lme(Diameter~1,random=~1|Group/Lineage/Dish/Disk,data=Dataset) So as I could do a Variance components analysis: > VarCorr(allrandom) Variance StdDev Group = pdLogChol(1) (Intercept) 1.8773750 1.3701734 Lineage = pdLogChol(1) (Intercept) 0.2648475 0.5146333 Dish = pdLogChol(1) (Intercept) 0.0601047 0.2451626 Disk = pdLogChol(1) (Intercept) 0.1456451 0.3816348 Residual 1.3456346 1.1600149 >...

...in terms of AIC or LR-Tests since they are the same model with different parametrization (I guess...). Now, I suppose I did everything right, and I want to compare the variance decomposition in islands and mainland, I use > VarCorr(f11) Variance StdDev Corr loc = pdLogChol(isla) (Intercept) 1643.5904 40.54122 (Intr) islaT 962.2991 31.02095 -0.969 grp = pdLogChol(isla) (Intercept) 501.7315 22.39936 (Intr) islaT 622.5393 24.95074 -0.818 Residual 547.0888 23.38993 > VarCorr...

...I would like to make a diagnosis of the model, and I think it is more appropriate. Looking at Pinheiro and Bates, I have tried the following, library(nlme) material.lme<-lme(purity~suppli,random=~1|suppli/batch,data=material) VarCorr(material.lme) Variance StdDev suppli = pdLogChol(1) (Intercept) 1.563785 1.250514 batch = pdLogChol(1) (Intercept) 1.709877 1.307622 Residual 2.638889 1.624466 material.lme Linear mixed-effects model fit by REML Data: material Log-restricted-likelihood: -71.42198 Fixed: purity ~ suppli (Intercept) suppli2 sup...

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

...I would like to make a diagnosis of the model, and I think it is more appropriate. Looking at Pinheiro and Bates, I have tried the following, library(nlme) material.lme<-lme(purity~suppli,random=~1|suppli/batch,data=material) VarCorr(material.lme) Variance StdDev suppli = pdLogChol(1) (Intercept) 1.563785 1.250514 batch = pdLogChol(1) (Intercept) 1.709877 1.307622 Residual 2.638889 1.624466 material.lme Linear mixed-effects model fit by REML Data: material Log-restricted-likelihood: -71.42198 Fixed: purity ~ suppli (Intercept) suppli2 sup...

....86667 Random effects: Formula: ~1 | molde (Intercept) Residual StdDev: 2.610052 2.412176 Number of Observations: 30 Number of Groups: 3 I want to obtain \rho = \sigma_b^2 / (\sigma_b^2 + \sigma^2) I know that I obtain \sigma_b^2 and \sigma^2 with > VarCorr(modeloMx1) molde = pdLogChol(1) Variance StdDev (Intercept) 6.812374 2.610052 Residual 5.818593 2.412176 But, I want to know if I can obtain \rho = 6.8123/(6.8123 + 5.8185) = 0.53934 straightforward. Thank you for you help. Kenneth

...0.08125895 0.70609383 1.87201306 Number of Observations: 36 Number of Groups: Supplier Batch %in% Supplier 3 12 Warning message: In pt(q, df, lower.tail, log.p) : NaNs produced > VarCorr(proclme) Variance StdDev Supplier = pdLogChol(1) (Intercept) 1.563785 1.250514 Batch = pdLogChol(1) (Intercept) 1.709877 1.307622 Residual 2.638889 1.624466 > intervals(proclme) Error in intervals.lme(proclme) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate v...

...ibrary "bootstrap" to estimate confidence intervals of ICC values calculated in lme. In lme, the ICC is calculated as tau/(tau+sigma-squared). So, for instance the ICC in the following example is 0.116: > tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=TDAT) > VarCorr(tmod) IDGRUP = pdLogChol(1) Variance StdDev (Intercept) 0.1829931 0.427777 Residual 1.3907732 1.179310 > 0.18299/(0.18299+1.39077) [1] 0.1162757 Using the bootstrap library, I can set up theta to do the ICC as follows: >theta<-function(x,DATA){tmod<-lme(CINISMO~1,random=~1|IDGRUP,data=DATA[...

...del: fit.lme <- lme(response~soiltype*habitat, random=~1|destination/origin) fit.lme0 <- lme(response~soiltype*habitat, random=~1|destination) The answers seemed to be identical except for one thing: > VarCorr(fit.lme) Variance StdDev destination = pdLogChol(1) (Intercept) 0.004149471 0.06441639 origin = pdLogChol(1) (Intercept) 0.060968550 0.24691810 Residual 0.007265180 0.08523603 > VarCorr(fit.lme0) destination = pdLogChol(1) Variance StdDev (Intercept) 0.004149471 0.06441639 Residual 0.068233730 0.26...

Dear R users, Has anyone implemented a "matrix log" function in R similar to the function logm() in Matlab? I did a quick R site search and browsed the contributed packages to no avail. The octave function is far too simplistic and fails for the Matlab test matrix. Ideally, the code of Cheng, Higham, and Laub (2001) or something similar could be utilized. Just checking before I

...nternaly optimize f(u2c(p)) instead of f. Clearly more sophisticated constraint specifications (than "lower" and "upper") would be required, like a way to declare that some parameters form a simplex or that others are encoding a variance-covariance matrix (in the latter case the pdLogChol function of nlme already provides the reparametrization if I'm not wrong). Assuming that such a functionality does not exist yet in R (if it does, sorry to have missed it) do you guys think that: 1) it's not necessary because users can take care of it for themselves 2) it would be complica...

Sorry if this is obvious, but my S skills aren't great and I haven't been able to find it documented anywhere. I want to write a new function for use with lme objects; the function will simply calculate an ICC (aka "rho") for each level of a mixed-effects model. What I need for this is pretty simple: (c(var1..varn, residual)) / sum(c(var1..varn, residual)) where var1..varn

...nding here is that ignoring nonindependence (i.e., using lm) actually results in SE estimates that are too large, while modeling the nonindependence reduces SE and increases power. Here is an example: # lme model > mod.lme<-lme(GWB.ADD4~HOR,random=~1|GRP,data=TBH) > VarCorr(mod.lme) GRP = pdLogChol(1) Variance StdDev (Intercept) 0.3160445 0.5621783 Residual 0.7449425 0.8631005 > 0.3160445/(0.3160445+0.7449425) [1] 0.2978778 #Note the large ICC (high nonindependence) > summary(mod.lme)$tTable Value Std.Error DF t-value p-value (Intercept) 1...

...t; library(nlme) > example(nlme) nlme> fm1 <- nlme(height ~ SSasymp(age, Asym, R0, lrc), nlme+ data = Loblolly, nlme+ fixed = Asym + R0 + lrc ~ 1, nlme+ random = Asym ~ 1, nlme+ start = c(Asym = 103, R0 = -8.5, lrc = -3.3)) Error in pdFactor.pdLogChol(X[[i]], ...) : object 'logChol_pd' not found So before I start spamming the Debian BTS, what would be the right way to deal with this? Do we need r-api-x here? Cheers, Johannes P.S.: Sorry of the other post, I pressed send before typing and even before thinking ...

Dear all, Here is the question: For example, using the "petrol" data offered with R. pet3.lme<-lme(Y~SG+VP+V10+EP,random=~1|No,data=petrol) pet3.lme$sigma gives the residual StdDev. But I can't figure out how to extract the "(intercept) StdDev", although it is in the print out if I do "summary(pet3.lme)". In

Am Dienstag, 25. April 2017, 08:50:34 schrieb Dirk Eddelbuettel: > On 25 April 2017 at 14:58, G?ran Brostr?m wrote: > | hello, > | > | I just installed R-3.4.0 from scratch: > | > | $ sudo apt install r-base > | > | but when I try > | > | > library(survival, lib.loc = "/usr/lib/R/library") > | > fit <- coxph(Surv(exit, event) ~ x, data =

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