similar to: unexpected "false convergence"

Hi, I'm trying to fit some data using a logistic function defined as y ~ a * (1+m*exp(-x/tau)) / (1+n*exp(-x/tau) My data is below: x <- 1:100 y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,1,1,2,2,2,2,2,3,4,4,4,5, 5,5,5,6,6,6,6,6,8,8,9,9,10,13,14,16,19,21, 24,28,33,40,42,44,50,54,69,70,93,96,110,127,127,141,157,169,

I know it's conventional to report bugs to the maintainer, but I'm not sure which package actually contains this bug(s), so I apologize for sending this to the list at large. I see the bug under both R 2.1.1, and R 2.2.1. (I sent a related message a while ago, but this one has more detail.) library(MASS) library(nlme) fit.model <- function(il, model.family) { cs <-

I tried to use LME (on a fairly large dataset, so I am not including it), and I got this error message: Error in lme.formula(formula(paste(c(toString(TargetName), "as.factor(nodeInd)"), : nlminb problem, convergence error code = 1 message = false convergence (8) Is there any way to get more information or to get the potentially wrong estimates from LME? (Also, the page in the

https://bugzilla.mindrot.org/show_bug.cgi?id=1964 Bug #: 1964 Summary: QoS/DSCP names false translated to ToS hex value Classification: Unclassified Product: Portable OpenSSH Version: 5.9p1 Platform: amd64 OS/Version: Linux Status: NEW Severity: normal Priority: P2 Component: ssh

Dear R Community! We analyse the impact of climbing activity on cliff vegetation. During our fieldwork, we recorded 90 Transects in 3 climbing sites. The aim is to see, if the plant cover (response: Cover) is influenced only by crevice availability (predictor: Cracs), or, additional, by the distance to the climbing route (predictor: Distance). Six plots are nested within one Transect

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

Hello splus-users, I am trying to fit a regression model for an ordered response factor. So I am using the function polr in library(MASS). My data is a matrix of 1665 rows and 63 columns (one of the column is the dependent variable). The code I use is polr(as.ordered(q23p)~.,data=newdatap) but I am getting the following warning message singularity encountered in: nlminb.1(temp, p, liv, lv,

Hi all. I am having a problem using color2D.matplot I am trying to visualize several different matrices under two color ranges. One color range corresponds to values less than 1 and the second color range for values greater than 1. However, the minimum value of each matrix differs and is automatically set to have the smallest value in the color range. Similarly, the maximum value of each matrix

As the original coder (in mid 1970s) of BFGS, CG and Nelder-Mead in optim(), I've been pushing for some time for their deprecation. They aren't "bad", but we have better tools, and they are in CRAN packages. Similarly, I believe other optimization tools in the core (optim::L-BFGS-B, nlm, nlminb) can and should be moved to packages (there are already 2 versions at least of LBFGS

How does one coerce predict.gls to incorporate the fitted correlation structure from the gls object into predictions? In the example below the AR(1) process with phi=0.545 is not used with predict.gls. Is there another function that does this? I'm going to want to fit a few dozen models varying in order from AR(1) to AR(3) and would like to look at the fits with the correlation structure

On Mon, Jan 27, 2020 at 4:31 PM Eli Friedman <efriedma at quicinc.com> wrote: > I assume by “drop support”, you mean reject it in the bitcode reader/IR > parser? We can’t reasonably support a complex feature like inalloca if > nobody is testing it. If we can’t reasonably upgrade it, and we don’t think > there are any users other than clang targeting 32-bit Windows, probably

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

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

On Wed, 4 May 2016, Nux! wrote: > Direct links > > https://www.imagemagick.org/discourse-server/viewtopic.php?f=4&t=29588#p132726 > https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2016-3714 > > Mitigation: > > As a workaround the /etc/ImageMagick/policy.xml file can be edited to disable > processing of MVG, HTTPS, EPHEMERAL and MSL commands within image files,

Dear All, I want to analyse treatment effects with time series data: I measured e.g. leaf number (five replicate plants) in relation to two soil pH - after 2,4,6,8 weeks. I used mixed effects models, but some analyses didn?t work. It seems for me as if this is a randomly occurring problem since sometimes the same model works sometimes not. An example: > names(test) [1] "rep"

Dear all I want to compute Monte Carlo p-values in lmer-models based on sampled data sets. To speed up calculations, I've tried to use internal functions from the Matrix package (as suggested ealier on the list by Doug Bates). So I did: fm2 <- lmer(resistance ~ ET + position + (1|Grp), Semiconductor,method='ML') simdata<-simulate(fm2,nsim=1) ynew <- simdata[,1] mer

hi everybody, we have problems using wincvs with a cvsroot on a samba-server. we think that the problem is the renaming of files where the cvs user does not have write-permisson on: this is our configuration: fileserver on a irix6.5 running samba 1.9.18p8 having a share on / named 'coder'. win95 box, running wincvs, setting cvsroot something like

I have a dell precision 380: http://catalog.us.dell.com/CS1/CS1Page2.aspx?br=6&c=us&cs=555&fm=11210&l=en&s=biz with an Intel Matrix Storage Manager ICH7R RAID Controller http://www.intel.com/support/chipsets/imsm/sb/cs-020663.htm I need to get CentOS 3 installed on this thing but can't due to the fact that the controller is not supported in 2.4 kernels. There is a patch

Hi Everybody, My problem is that nlminb doesn't converge, in minimising a logLikelihood function, with 31*6 parameters(2 weibull parameters+29 regressors repeated 6 times). I use nlminb like this : res1<-nlminb(vect, V, lower=c(rep(0.01, 12), rep(0.01, 3), rep(-Inf, n-15)), upper=c(rep(Inf, 12), rep(0.99, 3), rep(Inf, n-15)), control = list(maxit=1000) ) and that's the result :

Hi R-users, I'm attempting to fit a number of mixed models, all with the same structure, across a spatial grid with data points collected at various time points within each grid cell. I'm trying to use a 'for' loop to try the model fit on each grid cell. In some cells lme does not converge, giving me the error: Error message: In lme.formula(logarea ~ year + summ_d, data =