similar to: negative effects of using idmap_tdb in large forest

I've recently upgraded to 3.3.2. I run a small domain using tbdsam. I am getting the following error logged in /var/log/messages: Jul 14 08:34:07 localhost winbindd[1727]: [2009/07/14 08:34:07, 0] winbindd/idmap.c:idmap_alloc_init(587) Jul 14 08:34:07 localhost winbindd[1727]: ERROR: Initialization failed for alloc backend, deferred! and in /var/log/samba/log.winbindd-idmap I

Hi all! I'm trying to setup a linux samba server as a domain member of a SINGLE FOREST MULTI DOMAINS. The forest is a 2 servers acting as a global catalog. Other domains are child domain with implicit trust with forest. I setup a linux server with samba as a domain member to work with squid, authenticating users and verify user's groups membership. I need to allow access to squid only to

I have setup a test PDC with samba 3.4.7 on a fedora core 12 linux machine. I have setup two way interdomain trusts with a Windows 2008 domain. The domain and forest functional levels are Windows 2003. Since the samba machine is not emulating an Active Domain Controller, the Windows 2008 machine should think it is talking to an NT4 server. And since NT4-based domains don't use

As per earlier post, I was having problems getting trusts setup between my Samba domain (3.0.x PDC, 3.4.x BDC on Solaris 10) and two Active Directory domains (each in a separate forest.) One domain is a test Win 2003 PDC in native Win 2003 mode, the other is a Win 2008 system also in native Win 2003 mode. To summarize some of the progess- things work better if the Samba 3.4 is the PDC,

On my test Samba PDC, I updated the krb5.conf file to add realm info for the Windows 2008. This seems to have resolved my "wbinfo" issue. "getent passwd" is still not working (I did update nsswitch.conf) but I suspect this is because of an idmap allocation issue. The syntax for idmap allocation in smb.conf seems to change between 3.0, 3.2, 3.3 and 3.4. I have also tried

Hi, I am trying to add additional columns to a forest plot using the meta package. The study information and subgroup analysis plotting is handled properly. For this output the data is ordered first by subgroup label (rnd.subgroup1 in the example) and second decreasing size of totals ('n' in the example). The proper ordering of this data is confirmed by the color coding (black and red in

Dear R-experts, The forest.rma() function from the metafor package creates nice forest plots for presenting the results of a meta-analysis. These plots can be annotated for e.g. giving names to the columns. E.g. as in the documentation of the package: data(dat.bcg) ### meta-analysis of the log relative risks using a random-effects model res <- rma(ai=tpos, bi=tneg, ci=cpos, di=cneg,

I'm analyzing data using Random Forest Regression. For some of the species I am analyzing, the percent variation explained is negative. Could you please explain to me what that means? If you need more information, please let me know. Thank you. Sincerely, Rachel Unger [[alternative HTML version deleted]]

Take a look at lmekin() in the coxme package. The motivating data set for my development of coxme was the Minnesota Family Breast Cancer project: 24050 subjects in 462 families. The random effect is an intercept per subject with sigma^2 K as its variance where K is the kinship matrix (1 for self-self, .5 for parent-child or sib-sib, .25 for uncle-neice, etc). lmekin is a linear models front

Dear R community, In the excellent nlme package the default diagnostic plot graphs the innermost residuals against innermost fitted values. I recently fit a mixed-effects model in which there was a very clear positive linear trend in this plot. I inferred that this trend occurred because my fixed effect was a two-level factor, and my random effect was a 12-level factor. The negative residuals

Dear All, I carry out negative binomial estimations using the glm.nb command from the MASS package. Is there a command or a simple procedure for computing marginal effects from a glm.nb fitted object? If these are the same as for a Poisson fitted object (glm), my question remains how to compute them. Thanks in advance for your help. Roberto Patuelli ******************** Roberto Patuelli, Ph.D.

Hello, I'm using the rmeta package to perform a meta analysis using summary statistics rather than raw data, and would like to analyze the effects in three different subgroups of my data. Furthermore, I'd like to plot this on one forest plot, with corresponding summary weighted averages of the effects displayed beneath each subgroup. I am able to generate the subgroup analyses by simply

Hello, I have a problem with categorical variables and dummy encoding. I've a factor and for each pair (i,j) with i != j, I'd like to fit res ~ a*x[i] - b*x[j]. A brief example with 3 variables: a - b = 2 b - c = -1 c - a = 0 Thus I fitted the following model: fit <- lm(result ~ X + Y) where Y is just the negative of X, means y[i] = -x[i]. But I don't want to double the

Hello all, I would like to fit a mixed effects model, but my response is of the negative binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or

OK - I've been doing some work on getting a forest plot or two together for a sub-group analaysis. Thats the crucial thing - because its a sub-group analysis rather than a meta-analsysis and all the forest (meta) and forestplot (rmeta) instructions assume you are doing a meta-analysis. I found rmeta didn't play nicely for me so I'm using the meta package with forest. Here's some

Hi list, All the docs I've seen use a combined root forest/regional setup, whereas I'd like to have separate regional trees under an essentially empty root forest. How does this work? If it matters, I'll be migrating a Samba3 NT-style PDC to provision the regional forest. Links to relevant docs or any info appreciated. Thanks! -John

Hi Kim and Others, Can anyone please help me on how can I edit default labels ( "Study 1", "Study 2",...) using forest() - same question as below. Which option I should try? It would be a great help. Thanks in advance, On Sun, Dec 13, 2009 at 7:14 PM, Kim Jung Hwa <kimhwamaillist@gmail.com>wrote: > Hi All, > > I'm using forest() from metafor package to

> > Hello All, > > I have been struggling with this for a long, long time. I came here > looking for answers. So, I have a VM running Debian Lenny. I install > the apt package samba, which installs 3.2.5. I work in a large > university with an extensive Active Directory environment, both forest > and domain running in Win2k native mode. There is a NetApp filer >

now I am learining random forest and using random forest package, I can get the OOB error rates, and test set rate, now I want to get the training set error rate, how can I do? pgp.rf<-randomForest(x.tr,y.tr,x.ts,y.ts,ntree=1e3,keep.forest=FALSE,do.trace=1e2) using the code can get oob and test set error rate, if I replace x.ts and y.ts with x.tr and y.tr,respectively, is the error rate

Hi, We have a test environment with Samba Version 4.18.5 which is set to Forest, Domain and lowest function Level of 2008 R2. I am trying to raise the forest and domain levels. However, when I try to raise the domain level, I get an error - "ERROR: Domain function level can't be higher than the lowest function level of a DC!" and when I try to raise the forest level I get an