similar to: mixtures as outcome variables

Full_Name: Din Chen Version: 2.4.0 OS: Windows XP Submission from: (NULL) (66.17.122.18) I just updated the R.2.4.0. and got the error message for random effect modelling, which was working on R.2.3.1. library(lme4) mmod <- lmer(bright ~ 1+(1|operator), pulp) summary(mmod) Then when I tried to extract the residuals and random effect using: resid(mmodr) ranef(mmodr) I got error message:

Hello. A quick question about incorporating variation due to study in the metafor package. I'm working with a particular data set for meta-analysis where some studies have multiple measurements. Others do not. So, let's say the effect I'm looking at is response to two different kinds of drug treatment - let's call their effect sizes T1 and T2. Some studies have multiple

I am using R to do a loess normalisation procedure. In 1.5.1 I used the following commands to normalise the variable "logratio", over a 2d surface (defined by coordinates x and y): > array <- read.table("121203B_QCnew.txt", header=T, sep="\t") > array$logs555<-log(array$s555)/log(2) > array$logs647<-log(array$s647)/log(2) >

I am trying to replicate the repeated measures example from Dr.Faraway's book (Extending the linear model with R) as follows: data(vision) vision$npower <- rep(1:4,14) mmod <-lmer(acuity~power+(1|subject)+(1|subject:eye),vision) When I look at the fixed effects p-value, it is missing. Am I missing something here? Thanks. Fixed effects: Estimate Std. Error t value

I have got following error in drawing wavelet fitting. can some one help? > library(faraway) > data(lidar) > newlidar<-lidar[c(1:128),] > library(wavethresh) > wds <- wd(newlidar$logratio) > draw(wds) Error in plot.default(x = x, y = zwr, main = main, sub = sub, xlab = xlab, : formal argument "type" matched by multiple actual arguments [[alternative HTML

HI everyone, I''m trying to assign colors to multiple lines in a graph. Problem is I don''t want to type in as many colors as there are lines....is there a way around this? In brief, I''m plotting the logratio for up to 60 samples and want a different color for each sample. Here is the code I''m using now.. Any help is greatly appreciated.. Best LT data <-

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The compositional data xi=(x_i1,x_i2,...,x_in), for each fixed i , xij>0, and sum(xij)=1; I want to compare the mean( u_i) of several groups i.e. H0: u_1=u_2=...=u_N or H0: u_11=u_21=...=u_N1 Are there any ANOVA tpye tools to do this work in R? Thanks, WEN S Q [[alternative HTML version deleted]]

Dear R users, i am wondering if anyone has some hints for this problem (i have not found a clear answer after searching the R-mailing list archive, 'help.search' in R, and R-Wiki, and the like...): let's assume that i have 4 periods compositional time series data: t=1, A=0.1; B=0.5; C=0.4 t=2, A=0.2; B=0.4; C=0.4 t=3, A=0.5; B=0.3; C=0.2 t=4, A=0.4; B=0.3;

Full_Name: Attila Lengyel Version: 2.8.0 OS: WinXP Submission from: (NULL) (81.182.224.160) > -0.3+0.1+0.1+0.1 [1] 2.775558e-17

Dear R Users, I would like to use R to fit a Weighted Least Square model for a binary outcome, say Y. The model is the one widely used for a binary dependent variable when the logistic model has not been proposed. Does anyone know how to specify the weight as the square root of 1/(E(Y)(1-E(Y)) in lm() or any other regression functions? I know that varPower() in the package of gls() can provide

Hi! I'm pretty raw when working with the R models (linear or not). I'm wondering has anybody worked with the NLME library and dichotomous outcomes. I have a binary outcome variable that I woul like to model in a nested (multilevel) model. I started to fit a logistic model to a NLS function, but could not suceed. I know there are better ways to do it in R with either the LRM or GLM wih

Hi there Within sampleSelection, I'm trying to calculate the marginal effects for variables that are present in both the selection and outcome models. For example, age might have a positive effect on probability of selection, but then a negative effect on the outcome variable. i.e. Model<-selection(participation~age, frequency~age, ...) Documentation elsewhere describes one method for

Hi everyone, I think the following error is slightly odd: > fred <- new.env() > happy <- function() fred > happy() <environment: 0x8a425b8> > attr(happy(), "foo") <- 1:10 Error: invalid (NULL) left side of assignment But naturally this works > attr(fred, "foo") <- 1:10 > fred <environment: 0x8a425b8> attr(,"foo") [1] 1

Hi Folks, I'd be grateful for suggestions about approaching the following kind of data. I'm not sure what general class of models it is best situated in (that's just my ignorance), and in particular if anyone could point me to case studies associated with an R approach that would be most useful. Suppose I have data of the following kind. Each "subject" is observed at say 4

Hello everyone, I want to do a meta-analysis of case-control studies on which an OR was computed based on a continuous exposure. I have found several several packages (metafor, rmeta, meta) but unless I misunderstood their main functions, it seems to me that they focus on two-group comparisons (binary independent variable), and do not have the option of using a continuous independent variable.

Hi, I just completed a really big rsync described earlier. Ie about 13,945 directories transfered about 600GB of data. Of 13,945 directories, 13,9441 directories transfer with matching du -b sizes of the preimage to the size of the destination machine image. of the 4 remaining directories i found source vs destination in bytes --------- a) 20480 vs 34922496 b) 28672 vs

Dear R-nlme expert We need two pieces of information about the fitting of a nlme model which we cannot extract from the R help files and would be most grateful if you could help us. We fit an energy allocation growth model with 4 parameters to individual growth curves using the nlme routine. We thus have repeated age and size measurements of individuals and therefore allow for random

Dear collegues, I have used R statistical program, package 'lmer', several times already. I never encountered major differences in the outcome between SPSS and R. ...untill my last analyses. Would some know were the huge differences come from. Thanks in advance, Ronald In SPSS the Pearson correlation between variable 1 and variable 2 is 31% p<0.001. In SPSS binary logistic

Hi, I am sharing my learning outcomes. Recently I downloaded Kali Linux 64-bit Version 2018.1 and ran it on my HP laptop with the integrated Intel Dual Band Wireless-AC 8260 Wireless Network Card. I wanted to test if I could hack the Wi-Fi WPA password on Ruckus R700 Access Point (AP) and the Aztech DSL8900GR(AC) Wireless Router. So I started using the Reaver WPA cracking tool. I understand