similar to: [OT] "plot y against x"

I have 4 DC in samba I can not create users in two of them gives me the following error Please any idea Karel slds root at nbn-2:~# samba-tool user add pepito New Password: Retype Password: params.c:pm_process() - Processing configuration file "/etc/samba/shares.conf" ERROR(ldb): Failed to add user 'pepito': - ../ldb_tdb/ldb_index.c:1216: Failed to re-index objectSid in

First posted at: http://deancollinsblog.blogspot.com/2009/04/australian-nbn-network.html You ripper, http://www.crn.com.au/News/100466,government-announces-nbn-plans.aspx This is exactly how fiber should be - a shared wholesale resource just like water to ensure retail channels can deliver value add in content and services; - Not 'outspending on infrastructure'

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

Hi, I have a dataset where the residual variance decreases with on one of the predictors (population size). Currently, the full model looks like this: prior<-list(R=list(V=1e-16, nu=-2),G1=list(V=diag(2), nu=2)) m<-MCMCglmm(response~poly(population size,2)*poly(other predictor,2)+time, random=~us(1+time):population, data=data, prior=prior) Basically, it's a random regression with

Hi to all the people, I'm working with abundance data of some species, but containing too zero values, and the factors are the ones typical in a BACI experiment (Before-and-After-Control-Impact). Thus, these are two fixed factors. As the data does not holds the normality and homogeneity of variances assumptions of clasiccal ANOVA, I'm trying to fit a zero-altered model using the MCMC glmm

Hello, I am trying to build a generalised linear mixed model.? My dependent variable is ordinal.? I have a random factor (7 individuals), and a repeated measure where the dependent variable was measured three times for each of four attempts (so the repeats are nested).? I also have a few covariates.? I am a complete novice in R, being used to using SPSS.? SPSS lets me build an ordinal model

Hello R-help team, I seek your help (for what is most likely a very simple problem). I'm new to Ubuntu and tried to install R using the Ubuntu Software Center. However, after clicking the install command, I always get prompted with the error " Failed to download package files --- check your internet connection". Details: Failed to fetch

I am getting this message: Failed to authenticate device <sip:2010 at X.X.X.X>;tag=149853321 for INVITE, code = -1 but it does not report the "connecting" address. Who is failing connecting ? I either need to block someone or fix something - I'm thinking block - but I dont know who. How do I found out the connecting IP? Jerry -------------- next part -------------- An HTML

I've scoured the archives but have found no concrete answer to my question. Problem: Two data sets 1st data set(x) = 20,000 rows 2nd data set(y) = 5,000 rows Both have the same column names, the column of interest to me is a variable called strain. For example, a strain named "Chab1405" appears in x 150 times and in y 25 times... strain "Chab1999" only appears 200

Hi, I have a data-frame, r (column names below), that needs subsetting: date, time, strain, gene, deltact When I try to subset r by applying selection criteria on two columns I get an empty data frame. For example I would like to extract all rows that have time == 0h and strain == ROC. So, t <- subset(r, (r$time == "0h" && r$strain == "ROC"), select= c(time,

Dear R user, I have a question on using R to analyze data with repeated measurements. I have 2 species with several strains (12) per species, each of which has been measured twice with for a given trait. No particular covariance, just two measures. Now I want to analyze the data with an ANOVA (aov) considering these repeated measures to get the MSq and SSq for the species and strain level. I

Hello, I am new to R and currently have the following problem: I have successfully loaded my data in R which consists of two numeric columns (LI_F and female) and one character column (Strain). So far I can plot two different set of boxplots for each of the numeric columns plotted by the groups of the character column and the commands look like that: boxplot(LI_F~Strain, ylab="LI_F",

Hello Sir I am getting problem in plotting in CCA . Could you please help me? I wrote the below command but I don't know why it is taking only first 5 env data rather than all 9. > strain.data <- read.xlsx("Dee rhiz.xlsx", sheetName="strain", header = T, row.names = 1) > env.data <- read.xlsx("Dee rhiz.xlsx", sheetName="env", header = T,

Dear list, Has anyone created a version of SSmicmen that allows testing for group differences? The basic Michaelis-Menten equation is: (Bmax * X) / (Kd + X). The nlme package allows modeling of random effects for Bmax and Kd as needed, but I curious how I can build in group differences? I have receptor binding data for strains of mice, and following Pinheiro and Bates' lead in their

In the vcdExtra package on R-Forge, I have functions and generic methods for calculating log odds ratios for R x C x strata tables. I'd like to define methods for fitting weighted lm()s to the resulting loddsratio objects, but I'm having problems figuring out how to do this generally. # install.packages("vcdExtra", repos="http://R-Forge.R-Project.org")

Using the nls function I fit the following model (and some others) to my data. mod1=nls(CLr ~ A-(A-CLi)*exp(-k*d), start = list(A=60,k=0.005)) I would like to rank a set of models using AIC. I calculated AIC as AIC(mod1) However, it appears to use an incorrect number of parameters (3 instead of 2). Why is this? Additionally, if I calculate AIC using the residuals sum of squares instead of the

dfhfsdhf at ghghgr.com I ran a simple lme model: modelrandom=lmer(y~ (1|Test) + (1|strain), data=tempsub) Extracted the BLUPs: blups=ranef(modelrandom)[1] Even wrote myself a nice .csv file....: write.csv(ranef(modelrandom)[1],paste(x,"BLUPs.CSV")) This all works great. I end up with a .csv file with the names of my strains in the first column and the BLUP in the second

Hello, I seem unable to construct a legend which contains a substitution as well as math symbols. I'm trying to do the following: strain2 <- "YJG48" legend.txt <- c( substitute( strain * %==% * "YJG45, rpn10" * %Delta%, list(strain=strain2) ), "Verhulst/Logistic", "Malthus" ) legend( 100,2.5, legend.txt, cex=0.75,

Hi, x <- 0.2*5 is.integer(x) gives me FALSE because R stores it as a float number, right? Is there an elegant way to work around that problem? Right now I'm using x <- 0.2*5 round(x) == x which returns TRUE. But more strictly I should use all.equal(), right? I somehow just don't like the--pardon--ugliness of those pieces of code. Maybe there is a beautiful

Hi, I have some difficulty in figuring out whether I am doing correct or not. A brief introduction about the work: It is a light/dark choice test conducted in insect larvae.  The response is binary (0- present in dark area, 1-present in light area) and the experiment is run for 15 min, so there are 15 repeated measurements per individual larva at 1 min intervals.  The factors which affect