similar to: Problems with subsets in NLME

I am writing a program in which I would like to take in a formula, change the response (Y) variable into something else, and then pass the formula, with the new Y variable to another function. That is, I am starting with formula <- Y~X1+X2+X3 and I'd like to do something like Y <- formula$Y newY <- f(Y) lm(newY~X1+X2+X3) So far, it seems that my

When I use a model fit with LME, I get an error if I try to use "predict" with a dataset consisting of a single line. For example, using this data: > simpledata Y t D ID 1 -1.464740870 1 0 1 2 1.222911373 2 0 1 3 -0.605996798 3 0 1 4 0.155692707 4 0 1 5 3.849619772 1 0 2 6 4.289213902 2 0 2 7 2.369407737 3 0 2 8 2.249052533 4 0 2 9 0.920044316 1

I am in the process of submitting a package to CRAN. R CMD check ran successfully on the package on my local computer, using R version 2.1.1. However, on the computers for CRAN (with version 2.10.0), the following errors occurred: Warning in parse_Rd("./man/predict.Rd", encoding = "unknown") : ./man/predict.Rd:28: unknown macro '\dquote' *** error on file

I am creating an R package. I ran R CMD check on the package, and everything passed until it tried to run the examples. Then, the result was: * checking examples ... ERROR Running examples in REEMtree-Ex.R failed. The error most likely occurred in: > ### * AutoCorrelationLRtest > > flush(stderr()); flush(stdout()) > > ### Name: AutoCorrelationLRtest > ### Title: Test for

Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1<-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method="REML") anova(fm1) numDF DenDF F-value p-value (Intercept) 1

Using formulas constructed from strings only partially works for me in lme: library(nlme) data(Orthodont) fm2<-lme(as.formula("distance~age"),data=Orthodont,random=~1|Subject) summary(fm2) # works augPred(fm2) # fails #Error in inherits(object, "formula") : #Argument "object" is missing, with no default I assume that my use of as.formula is wrong, but

I am trying to optimize a likelihood function using NLMINB. After running without a problem for quite a few iterations (enough that my intermediate output extends further than I can scroll back), it tries a vector of parameter values NaN. This has happened with multiple Monte Carlo datasets, and a few different (but very similar) likelihood functions. (They are complicated, but I can send them

lme() and gls() models from the nlme package are all crashing with R.3.4.0. Identical code ran correctly, without error in R 3.3.3 and earlier versions. The behavior is easily demonstrated using one of the examples form the lme() help file, along with two simple variants. I have commented the errors generated by these calls, as well as the lines of code generating them, in the code example below.

Dear R developers, first of all let me join the chorus of congratulations for the release of R 1.0.0. Well, done! Unfortunately, I find it necessary to e-mail in a bug report regarding the `nlme' package. On my office machine I experience the following trouble: bossiaea:/opt/R$ R CMD check -c nlme Checking package `nlme' ... Massaging examples into `nlme-Ex.R' ... Running

Hi, I have a string that I want to use in a variable call. How can I remove the quotes and/or the string properties of the string to use it in a variable call? Here's an example: library(lme) fm2 <- lme(distance ~ age, data = Orthodont, random = ~ 1) summary(fm2) I want to update the above regression to include new predictors according to what is in a string: predictors <-

Dear friends. I tried the session below with 10 MB in both vsize and nsize but didn't get the example work. Is this a problem in LME or in me or both or somewhere else or undefined ? R : Copyright 1999, The R Development Core Team Version 0.64.0 Patched (May 3, 1999) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type

On 11 May 2017 at 10:17, Erwan Le Pennec wrote: | Dear all, | | I've stumbled a similar issue with the package cluster when | compiling the 3.4.0 version with the settings of Fedora RPM specs. | Compiling R with the default setting of configure yields a version that | works for cluster... and nlme. | | I did not find the exact option that was the cause of this issue | but

Full_Name: Per Broberg Version: 1.00 OS: Windows 98 Submission from: (NULL) (62.20.231.229) I tested my installation with the following: > library(lme) Loading required package: nls Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "C:\PROGRAM\R\RW1000/library/nls/libs/nls.dll": LoadLibrary failure > data(Orthodont) > fm1

I am having the same problem as one Rebecca Sela(see bellow). On 21/12/2007 12:07 AM, Rebecca Sela wrote: >* I am trying to optimize a likelihood function using NLMINB. After running without a problem for quite a few iterations (enough that my intermediate output extends further than I can scroll back), it tries a vector of parameter values NaN. This has happened with multiple Monte Carlo

Hi everyone, I have a quick and probably easy question about lme for this list. Say, for instance you want to model growth in pituitary distance as a function of age in the Orthodont dataset. fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data = Orthodont) You notice that there is substantial variability in the intercepts (initial distance) for people at 8 years, and that

Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can obtain random effects for intercept and slope of a certain level (say: 1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm mistaken here, but the results are identical. However, if I try to get the standardized random effects adding the paramter "standard=T" to the

Dear R-helpers I am running a simple mixed effects model using lme(). The call looks like this: fit <- lme(Analyte~Sample, data=Data, random=~1 | Run) I am particularly interested in the estimated random effects. When I print the 'fit' object, it looks something like example below: (...) Random effects: Formula: ~1 | Run (Intercept) Residual StdDev: 3.483794 3.637523

Dear All -- I am trying to use within a little table producing code an anova comparison of two gls fitted objects, contained in a list of such object, obtained using nlme function gls. The anova procedure fails to locate the second of the objects. The following code, borrowed from the help page of anova.gls, exemplifies: --------------- start example code --------------- library(nlme) ##

Dear R-helpers, When I try > anova(txtE2.lme, txtE2.lme1) Model df AIC BIC logLik Test L.Ratio p-value txtE2.lme 1 10 8590 8638 -4285 txtE2.lme1 2 7 8591 8624 -4288 1 vs 2 6.79 0.0789 > latex(anova(txtE2.lme, txtE2.lme1)) Error: object "n.group" not found I don't even see n.group as one of the arguments of latex() I checked to see >

Dear All, I've come across a problem in predict.lme. Assigning a model formula to a variable and then using this variable in lme (instead of typing the formula into the formula part of lme) works as expect. However, when performing a predict on the fitted model I gan an error messag - predict.lme (but not predictlm) seems to expect a 'properly' typed in formula and a cannot extract