similar to: bug report: nlme model-fitting crashes with R 3.4.0

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 I'm willing to help. Erwan PS: This is the reason why R is

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

On Thu, 2017-05-11 at 06:37 -0500, Dirk Eddelbuettel wrote: > 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?

Hi I have a huge array with series of data. For each cell in the array I fit a linear model, either using lm() or gls() with lm() there is no problem, but with gls() I get an error: Error in glsEstimate(glsSt, control = glsEstControl) : computed gls fit is singular, rank 2 as soon as there are data like this: > y1 <- c(0,0,0,0) > x1 <- c(0,1,1.3,0) > gls(y1~x1)

Hello, Does anyone on this list know what inner_perc_table is or where it is typically found? I am trying to modify some source code and it is used with the .C() function. When I try and run it, it states that 'inner_perc_table is not found'. It is only called in such a way and isn't defined at any point in the previous code in which the function works. Should this be in some other

Greetings, I am working on modifying some code from the nlme package. I have had many discussions on the mixed models mailing list and have been directed to simply 'hack' the source code to have the degrees of freedom generated by one function to use in the output of another function that doesn't generate them. My current holdup is an error regarding a .c file called

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

Hi, I'm analyzing a large number of large simulation datasets, and I've isolated one of the bottlenecks. Any help in speeding it up would be appreciated. `dat` is a dataframe of samples from a regular grid. The first two columns are the spatial coordinates of the samples, the remaining 20 columns are the abundances of species in each cell. I need to calculate the species richness in

Hi, I apologize for this often/old question. I found some hints but couldn't solve the problem so far. I have C functions (incl. the header files) as well as the R wrapper functions which I want to use for faster calculations. These functions are included in a R package. The installation process seems to be ok (no errors). I also can load the package without errors. But when I call the

Hello everyone, I'm stumped. I'd like to create a scatterplot matrix with circular reference lines. Here is an example in 2d: library(ellipse) set.seed(1) dat <- matrix(rnorm(300), ncol = 3) colnames(dat) <- c("X1", "X2", "X3") dat <- as.data.frame(dat) grps <- factor(rep(letters[1:4], 25)) panel.circ <- function(x, y, ...) { circ1

I am trying to fit a B-spline regression model with a corStruct using gls. I am using bs() and specifying the knots myself. If I make the knots data-dependent, this works but has undesirable side-effects. I prefer to reference an auxilliary variable "knots" in my model formula. It should not be part of the data frame, as it is a vector of a different length. How can this be done? The

Df contains Session_Setup DCT RevDataVols_bin counts comp 1 Session_Setup RLL 1 NA Session_Setup+RLL+1 2 Session_Setup RLL 2 NA Session_Setup+RLL+2 3 Session_Setup RLL 3 NA Session_Setup+RLL+3 4 Session_Setup RLL 4 NA Session_Setup+RLL+4 5 Session_Setup RLL 5

Hi, we're using a GSM-Gateway on asterisk to forward incoming calls to the cellphones, but, of course, the cellphones always display the callerid from the gateway. Does anyone know a symbian app that could (on an incoming call) connect via grps/3G to a database behind the asterisk and fetch the real callerid and do a calleridname-lookup on a number? -------------- next part -------------- An

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

Hello. I am trying my hand at modifying the varFunc class varExp, but I must be missing a step. All I want to do right now is make a working copy of varExp, call it varExp2, and then later change it. coef.varExp2, coef<-.varExp2, and Initialize.varExp2 all seem to work properly after I construct them. I can successfully use the commands: v2 <- varExp2(form = ~age|Sex,fixed =

Dear R-philes: I am having an issue with exporting contingency tables with xtable(). I set up a contingency and convert it to a matrix for passing to xtable() as shown below. v.cont.table <- table(v_lda$class, grps, dnn=c("predicted", "observed")) v.cont.mat <- as.matrix(v.cont.table) Both produce output as follows: observed predicted uh uh~ uh 201

l would like to create the following matrice treatmentgrp strata 1 1 1 1 1 1 1 2 1 2 1 2 2 1 2 1 2 1 2 2 2 2 2 2 l should be able to choose the size of the treatment grps and stratas the method l used intially creates the

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a