similar to: Roaming profiles not roamin :(

Hi I have a problem with samba 3.0.5pre1. Many of my users are disabled by samba and I can't find the reason why. Here is an example of an user disbled by samba: Unix username: christ NT username: christ Account Flags: [DU ] User SID: S-1-5-21-3371203057-3264423045-2392767973-4030 Primary Group SID: S-1-5-21-3371203057-3264423045-2392767973-1213

Guys, I've identified a problem "I think" Config: Samba 2.2.8a on Linux and Solaris WinXP SP1 and Win2kSP4 workstations We have a problem here, when logging out and using roaming profiles on a samba host take significantly longer than logging out when the profile is stored on a Win2k server. Looking closely I've discovered the following. On each logout, numerous files, that

I am running Dovecot 2.0.13 on Fedora 15. I have migrated from a bincimap installation using checklocalpwd. All email is in folders owned by a unprivileged user, popuser. The email users are entirely virtual and have no relationship to actual linux users. My userdb calls return the UID and GID of popuser and all goes well retrieving the mail via POP3 and IMAP. I am trying to implement a

Having built rsync 2.5.5 on IRIX 6.2 with gcc 3.1, I ran into two failures when running the testsuite with make check: both the chgrp and hardlinks tests fail: The failure in the chgrp test occurs here: + rsync -rtgvvv /amnt/callisto/volumes/obj-irix5/local/obj.irix5/rsync-2.5.5/testtmp.chgrp/from/ /amnt/callisto/volumes/obj-irix5/local/obj.irix5/rsync-2.5.5/testtmp.chgrp/to/ rsync: opendir

Hello. I have to fit a mixed model from a repeated measures split-plot experiment in which the response variable is binary. This requires a generalised linear mixed model in which I can specify a binomial distribution. I can’t find the appropriate package in R. I have looked at glmmML, but it doesn’t seem to allow any mixed structure beyond a simple 2-level one. Can anyone point me to the

Hello. I want to plot the distribution of a continuous variable (y) in each of two groups on the same graph as histograms. I suppose one could call this a 2-d histogram? Can this be done in R? Here is a typical data.set: y group 1.2 1 3.3 1 2.4 2 5.7 1 0.2 2 etc. Bill Shipley Subject Matter Editor, Ecology North American Editor, Annals of

Hello. Please excuse this off-topic request, but I know that the question has been debated in summary form on this list a number of times. I would find a paper that lays out the advantages and disadvantages of using different types of SS in the context of unbalanced data in ANOVA, regression and ANCOVA, especially including the use of different types of contrasts and the meaning of the

Hello. I am about to do a rather complicated analysis and am not sure how to do it. The experiment has a split-plot design and also repeated measures. Both of these complications require one to define an error term and it seems that one cannot specify two such terms. The split-plot command is: aov(y~covariates +A*B+Error(C), data=) where A and B are the fixed effects and C is the

Hello. I am trying to fit a non-rectangular hyperbola function to data of photosynthetic rate vs. light intensity. There are 4 parameters that have to be estimated. I find the nls function very difficult to use because it often fails to converge and then gives out cryptic error messages. I have tried playing with the control parameters but this does not always help. Is there another

Hello. I want to estimate the predicted values and standard errors of Y=f(t) and its first derivative at each unique value of t using the smooth.spline function. However, the data (plant growth as a function of time) show substantial heterogeneity of variance since the variance of plant mass increases over time. What is the consequence of such heterogeneity of variance in terms of bias in the

To avoid angry replies, let me first say that I know that the use of Type III sums of squares is controversial, and that some statisticians recommend instead that significance be judged using the non-marginal terms in the ANOVA. However, given that type III SS is also demanded by some… is there a function (equivalent to drop1 for lm) to obtain type III sums of squares for mixed models using the

Hello. Following pages 342-347 of Pinheiro & Bates, I am trying to write a self-starting nonlinear function (a non-rectagular hyperbola) to be used in nonlinear least squares regression (and eventually for a mixed model). When I use the getInitial function for my self-starting function I get the following error message: > getInitial(photo~NRhyperbola(Irr,theta,Am,alpha,Rd),dat) Error

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

Hello. I am sorry for posting a (seemingly) simple question, but I have just spent 2 hours trying to find the answer, without success. I want to make a histogram with conditioning on a factor, using Trellis graphics. However, I do not want any colours (only black and white) either in the histograms or in the strip. There must be some simple argument but I can’t find it. Here is my code so

Hello. I have searched the archives but have not found anything. I need to solve a constrained optimisation problem for a nonlinear function (“maximum entropy formalism”). Specifically, Optimise: -1*SUM(p_ilog(p_i)) for a vector p_i of probabilities, conditional on a series of constraints of the form: SUM(T_i*p_i)=k_i for given values of T_i and k_i (these are constraints on

Hello. I had previously posted a question concerning the optimization of a nonlinear function conditional on equality constraints. I was pointed towards the contrOptim function. However, I do not understand the syntax of this function with respect to specifying the constraints and so I don’t know if it is what I need. The command is: constrOptim(theta, f, grad,ui,ci,…). “theta” is the

Earlier, I had posted the following question to the group : > Hello. I have come across a curious result that I cannot explain. > Hopefully, someone can explain this. I am doing a 1-way ANOVA with 6 > groups (example: summary(aov(y~A)) with A having 6 levels). I get an > F of 0.899 with 5 and 15 df (p=0.51). I then do the same analysis but > using data only

Hello. This is not a technical question but rather an interpretational one, so I apologise if it is not appropriate for this discussion group. I am fitting a mixed model involving two predictor variables: x1 and x2. x1 varies between groups but is constant within groups while x2 varies both between and within groups. I fit two nested models. Model 1: lme(y~x1*x2, random=~1|groups). In this

Hello. I want to specify a diagonal structure for the covariance matrix of random effects in the lme() function. Here is the call before I specify a diagonal structure: > fit2<-lme(Ln.rgr~I(Ln.nar-log(0.0011)),data=meta.analysis, + random=~1+I(Ln.nar-log(0.0011)|STUDY.CODE,na.action=na.omit) and this works fine. Now, I want to fix the covariance between the between-groups slopes

Hello. I am fitting a repeated measures model using the mixed model function of R (lme). However, the hierarchical structure is complicated. Each individual sheep is measured a number of times (once per year) over its life (ranging from once to 12 consecutive years). However, this longitudinal study involves many different cohorts and so many different individuals (of different ages) are