similar to: allowing individual level correlations to differ by cluster in lme in R

Fake Conferences CSCI and WORLDCOMP of Hamid Arabnia Hamid Arabnia from University of Georgia is well known for his fake WORLDCOMP conferences https://sites.google.com/site/dumpconf This website has an open challenge posted sometime in 2012 and it also has comments from several well-known researchers on WORLDCOMP. Hamd Arabnia never responded to these because his conferences are bogus.

Dear R-devel members, While writing a new correlation structure similar to 'corCompSymm' and intended to be used with 'gnls', I got puzzled with the 'Initialize' method. Using 'Initialize' before 'gnls' may be regarded as a mean to set an initial value for the corStruct parameter. However 'gnls' does not work properly with a

Dear R Friends, I need help with an error associated with corCompSymm in an lme fit. I am using a mixed effects model to analyze a split-plot with repeated measures and would like to fit with the compound symmetry correlation structure. This problem doesn't occur when using corAR1 or any of the other structures. I would greatly appreciate help on how to solve this issue. Here's my

We are trying to use the corCompSymm function in nlme The example from the help pages for the corAR1 function gives the following > corAR1(0.2, form = ~ 1 | Mare) Correlation structure of class corAR1 representing Phi 0.2 We are expecting a somewhat similar correlation specification with the help page example for corCompSymm, but just get an error instead > corCompSymm(0.5, form = ~ 1 |

Dear Friends, I am seeking for any help on an error message in lme functions. I use mixed model to analyze a data with compound symmetric correlation structure. But I get an error message: "Error in corMatrix.corCompSymm(object) : NA/NaN/Inf in foreign function call (arg 1)". If I change the correlation structure to corAR1, then no error. I have no clue how to solve this problem.

Hi, I have confirmed temporal correlation problems in my data. Is there a possibility to use corCompSymm for a gamm()? I am an R-beginner. I have very short time series. There are three years and within each year, there are 10 weeks. he 10 weeks are the same every year and have not unique values, I seem not to be able to use AR-1 (I assume that I have too little data for autoregression

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ CLI Output : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ vicidialnow*CLI> == Parsing '/etc/asterisk/manager.conf': Found == Manager 'sendcron' logged on from 127.0.0.1 == Parsing '/etc/asterisk/manager.conf': Found == Manager 'sendcron' logged on from 127.0.0.1 == Manager 'sendcron' logged off from

Dear R-listers, I have been using Snowfall (version 1.84) for parallel computing on a quad-PC running Windows 7 for a month or so without much problem. I started having problems runnnig R with Snowfall over our network when a new Novell client was installed for Windows 7. I experinenced network mapping problem with this new client. Our network colleague provided a fix which worked but a little

Dear R-listers, I am writing a function that will generate a simple plot for the user . The size of the window (that contains the plot) may not be what the user wants. Supposing the user adjusts the window to a size of her/his choice, I would like to be able to save the new window size and specify the plot to be of this new window size when the user calls my function again. My question is

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

Hello list: How to extract the value of "Rho" from a gnls() object. I am using gnls() function similar to res <- gnls(y~SSmicmen(),correlation=corCompSymm(form~1|b),data=dat) Thanks in advance, Mahbub. -- Mahbub Latif School of Mathematical Sciences Queen Mary, University of London United Kingdom [[alternative HTML version deleted]]

I'm trying to learn how to do a repeated measures ANOVA in R using lme(). A data set that comes from the book Design and Analysis has the following structure: Measurements (DV) were taken on 8 subjects (SUB) with two experimental levels (GROUP) at four times (TRIAL). In SAS, I use the code: PROC MIXED DATA=[data set below]; CLASS sub group trial; MODEL dv = group trial group*trial;

Hi folks, I am working with repeated measures data and I ran into issues where the paired t-test results did not match those obtained by employing glht() contrasts on a lme model. While the lme model itself appears to be fine, there seems to be some discrepancy with using glht() on the lme model (unless I am missing something here). I was wondering if someone could help identify the issue. On

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.

Goal: use GEE or GLMM to analyze repeated measures data in R GEE problem: can?t find a way to do GEE with negative binomial family in R GLMM problem: not sure if I?m specifying random effect correctly Study question: Does the interaction of director and recipient group affect rates of a behavior? Data: Animals (n = 38) in one of 3 groups (life stages): B or C. Some individuals (~5)

Dear List, After running a compound symmetric model using gls, I realized that the default contrasts were not the ones that made the most sense given the biological relationships among the factor levels. When I either changed the factor levels to re-arrange the order they occur in the gls model (not shown below) OR specifically change the contrasts I get the exact same estimates for the

Hi All I was wondering how to get the same results with gls and lme. In my lme, the design matrix for the random effects is (should be) a identity matrix and therefore G should add up with R to produce the R matrix that gls would report (V=ZGZ'+R). Added complexity is that I have 3 levels, so I have R, G and say H (V=WHW'+ZGZ'+R). The lme is giving me the correct results, I am

Hello all, I'm attempting to fit a generalized linear mixed-effects model using lmer (R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call: vidusLMER1 <- lmer(jail ~ visit + gender + house + cokefreq + cracfreq + herofreq + borcur + comc + (1 | code), data = vidusGD, family = binomial, correlation = corCompSymm(form = 1 | ID), method = "ML") Although the model fits, the

I am using the gls function of the nlme package to analyze data sets of soil respiration which have the following design: 3 complete blocks x 5 sampling dates (time from fertilization) x 3 fertilization levels. The fertilization dates are equal for all subjects (blocks) but not periodical (-46, 10, 24, 53, 123 days from the event). The code that I've been using is: fit.csnC<- gls(dno.C

I have been using a nlme model and wish to specify the correlation structure. My data is grouped (bas), but I have no time component or adequate spatial description beyond the grouping variable. So I chose the simplest structure and updated my original nlme model by: update(model, corr=corCompSymm(~1|bas)) This is not significant. One of my variables measures area upstream of the sample. Area