similar to: Histogram plot spacing

I have a problem regarding the colors assigned to the lines in the key to an xy plot. I specify the plot like this: xyplot(numbers~sqrt(breaks)|moltype+disttype, groups = type, data = alldata, layout = c(3,2), type = "l" , lwd = 2, col = c("gray", "skyblue"), key = simpleKey(levels(alldata$type), points = FALSE, lines = TRUE, columns = 2, lwd = 2,

Hello all! # dovecot --version 2.1.17 Sometimes I have this in the logfile: May 11 16:55:52 master: Warning: service(imap-login): process_limit (100) reached, client connections are being dropped May 11 17:35:03 master: Warning: service(imap-login): process_limit (100) reached, client connections are being dropped May 11 17:36:27 master: Warning: service(imap-login): process_limit (100)

I am trying to use varcomp to obtain the variance partitioning across different nested levels of random effects (say x,y and z). I get the three variance components (for each of my along with an additional one called 'within' from varcomp output. I am using the 'scale total variance to 1' option and though the within component is small, it does form a part of what explains the

Hello, I am trying to use varcomp to decompose the variance across multiple nested levels on a lme object. I am able to successfully do this and when I view the varcomp object I can see the individual values / estimates for the variance at different levels. However, I want to be able to extract each of them separately, as I need to build a confidence interval using bootstrapping on the sample

Hi, I'm still having problems putting the variance components of my model in to a data frame, it is a continuation of this discussion, http://r.789695.n4.nabble.com/ANOVA-problem-td4609062.html, but now focussed on the problem of extracting variance components. I have got my mixed effects model now /narrow$line<-as.factor(narrow$line) rg.lmer <- lapply(split(narrow,

I am trying to understand better an analysis mean RT in various conditions in a within subjects design with the overall mean RT / subject as one of the factors. LME seems to be the right way to do this. using something like m<- lme(rt~ a *b *subjectRT, random= ~1|subject) and then anova(m,type = "marginal"). My understanding is that lme is an easy interface for dummy coding

Hi try str(varcompobject) to see structure of this object. You can extract parts by standard R means. Cheers Petr > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Sharada > Ramadass > Sent: Tuesday, August 8, 2017 3:33 PM > To: r-help at r-project.org > Subject: [R] how to extract individual values from varcomp? > >

Hello R experts, I haven't found anything like the S function 'varcomp' as described in W.N. Venables & B.D. Ripley's 'Modern Applied Statistics ...' for R; does something comparable exist for R, or is planned for future releases? More generally, are there libraries with post-anova test procedures, like Student-Newman-Keuls? Or do those of you who frequently use

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Hello, I have some data covering contaminant concentrations in fish over a time period of ~35 years. Each year, multiple samples of fish were taken (with varying sample sizes each year). Ultimately, I want an estimation of the variance between years, and the variance within years + random effects. I used a linear mixed model to estimate these variances, but after reading a number of different

Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN

Dear List, Is there a way to extract variance components from lmeObjects or summary.lme objects without using intervals()? For my purposes I don't need the confidence intervals which I'm obtaining using parametric bootstrap. Thanks, Mike [[alternative HTML version deleted]]

Hello I am sorry fuch such a stupid question. Suppose I have a table of data having a lot of NAs and I want to replace those NAs by the mean of the column before NA replacement. How is it possible to do that efficiently ? Thanks in advance, Julie -- Julie Bernauer Yeast Structural Genomics http://www.genomics.eu.org

Hello everybody. I have a question that has stumped me and the usual "apply" tricks don't seem to work. I run a course where each student's performance is marked by one or more assessors. I have a data frame containing students' names, assessors' names and their marks, arranged as follows: ID student assessor Q1A Q1B Q1C Q2A Q2B Q3 1 2152833

Hi, Is it possible to make something like the following code actually work? My goal in this example would be that I'd see results like 1 10000 10100 2 10100 10200 3 10200 10300 4 10300 10400 In real usage the function would obviously do a lot more work, but the question I cannot answer myself yet is whether the apply can return a value from the work on one row and then use

Hai Praveen, > -----Oorspronkelijk bericht----- > Van: Praveen Ghimire [mailto:PGhimire at sundata.com.au] > Verzonden: donderdag 27 juni 2019 13:46 > Aan: samba at lists.samba.org > CC: 'L.P.H. van Belle' > Onderwerp: RE: [Samba] Reverse DNS > > Hi Guys, > > Thank you for your emails. Here is the info > > /etc/apparmor.d/local/usr.sbin.dhcp >

hi all, I hope you can help me with a strange quota/nfs/XFS behavior... I'm using Centos 5.6 on both a nfs server and client. Both are 64-bit, and using a recent kernel: NFS server: Linux fs2.priv 2.6.18-238.12.1.el5 #1 SMP Tue May 31 13:22:04 EDT 2011 x86_64 x86_64 x86_64 GNU/Linux NFS client: Linux nx8.priv 2.6.18-238.12.1.el5 #1 SMP Tue May 31 13:22:04 EDT 2011 x86_64 x86_64 x86_64

Hello everyone, I am sorry if this has already been asked but I can't find it. I want to superpose two lattice plots, namely a levelplot and a contourplot of two different variables with the same x-y scale. I found information about panel.superpose but it does not seem to correspond to what I want (I have two different variables, not groups of the same variable) How can I do this?

Hi folks, I have a vaiable Y and an associated factor Z at several (13) levels. boxplot(Y~Z) produces a nice array of boxplots, one for each level of Z, and each duly labaelled with its level of Z. I would like to superpose on each boxplot the actual data points which it represents, i.e. do something conceptually (though not in real R) expressed as points(Y~Z) or points(Z,Y) It can

Let me present to you my problem : I have a character vector x and I would like to obtain the indices of the elements of this vector that yielded exactly a match. For example, x=nom, pattern="b", I would to obtain 2 because "b" is on the second position. First program : nom <- c("a","b","ab") grep("b",nom) 2 3 Then I try the