similar to: Multilevel analysis with package lme

Dear all, I am planning to use nlme library for analysis of experiments in semiconductor industry. Currently I am using "lm" but plan to move to "lme" to handle within wafer / wafer-to-wafer and lot-to-lot variation correctly. So far everything is working well, but I have a fundamentel question: NLME offers "maximum likelihood" and "restricted maximum

If anyone has a few extra CPU cycles to spare, I'd appreciate it if you could verify a problem that I have encountered. Run the code below and tell me if it crashes your R before completion. library(lme4) data(bdf) dump<-sapply( 1:50000, function(i) { fm <- lme(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen, data = bdf, random = ~ IQ.ver.cen | schoolNR); cat("

Hi all - (this is posted to r-help and R-SIG-MAC) OSX 10.3.7, R 2.0.1, lme4/Matrix/latticeExtra latest, fresh install of R. MASS loaded (or not). I am getting an error message for the print() and summary() commands with all lme models I try and run in lme4 (GLMM's work fine). Using the example from the lme help, summary and print produce the following errors, despite the model being

Dear all, The following function code fails with errors (see below): RegPlots <- function (data, ContrVar, RespVar){ intNmbrRows<-length(RespVar);intNmbrCols<-lenght(ContrVar) par(mfrow=c(intNmbrRows,intNmbrCols)) For(i in 1:intNmbrRows){ For (j in 1:intNmbrCols){ RegGraf(data,ContrVar[i],RespVar[j]) } }

Hi All I was wondering how to get the same results with gls and lme. In my lme, the design matrix for the random effects is (should be) a identity matrix and therefore G should add up with R to produce the R matrix that gls would report (V=ZGZ'+R). Added complexity is that I have 3 levels, so I have R, G and say H (V=WHW'+ZGZ'+R). The lme is giving me the correct results, I am

Dear list, I asked some questions about multilevelanalysis a couple of months ago. In the meantime I did some reading about the subject. Now I'd like to check, if I understood it all correctly. If you think my questions are not appropriate for this list, please tell me so and i will immediatly stop asking them. I have a dataset with one predicted variable (y), two explanatory variables

I am trying to put 3 dotcharts side-by-side with minimal space between each. Each chart is for a different variable, but the vertical axes are the same. I want to have vertical axis labels on the lefthand chart but no vertical axis labels on the other two. Plus, I would like very little space between charts 1 & 2 and between charts 2 & 3. I have one approach but am not too happy with

Hello R, To analyze multi-level data, I started learning and using lmer. So far so wonderful. I then found some useful functions in package languageR. But then the following problem ocurred: Whenever I load and use the languageR package, then save the workspace - or quit R with saving the workspace - I am unable to reload that workspace in a later session. That is, R doesn't start at all

Dear r-helpers, why do I get an output in the first iteration of the for-loop which contains the string values of the input vector, and how can I avoid that? Here's the output (only line 1 is wrong) latentVariable Indiv Group 1 rPlanning rIterat rTDD 2 rPlanning 0.79 0.84 3 rIterat 0.79 0.83 4 rTDD 0.9 0.96 5 rStandup 0.83 0.82 6

I try to use ?randomForest to find variables that are the most important to divide my dataset (continuous, categorical variables) in two given groups. But when I plot the outliers: plot(outlier(FemMalSex_NAavoid88.rf33, cls=FemMalSex_NAavoid88$Sex), type="h",col=c("red","green")[as.numeric(FemMalSex_NAavoid88$Sex)]) it seems to me that all my values appear as

Hello, When i run the code below from Weibull distribution with 30% censoring by using optim i get an error form R, which states that Error in optim(start, fn = z, data = q, hessian = T) :? ? objective function in optim evaluates to length 25 not 1 can somebody?help me remove this error. Is my censoring approach correct. n=25;rr=1000 p=1.5;b=1.2 for (i in 1:rr){ q<-c(t,cen)

Hi, I'm analyzing a large number of large simulation datasets, and I've isolated one of the bottlenecks. Any help in speeding it up would be appreciated. `dat` is a dataframe of samples from a regular grid. The first two columns are the spatial coordinates of the samples, the remaining 20 columns are the abundances of species in each cell. I need to calculate the species richness in

Dear all, I tried to use "step" function to do model selection, but I got an error massage. What I don't understand is that data as data.frame worked well for my other programs, how come I cannot make it run this time. Could you please tell me how I can fix it? ***************************************************************************************************

hello, I have a graph and i use qgraph package to calculate centrality parameters. Now I want to know the maximum value of shortest path for each vertex with discarding the Inf value in short pathes. For this I use the ShortestPathLengths of centrality function in qgraph. but when I want to get the maximum the result is wrong. here is my code: cen<-centrality(Q)

Hi, there: It's my first time to post question in this forum, so thanks for your tolerance if my question is too naive. I am using a nonparametric smoothing procedure in sm package to generate smoothed survival curves for continuous covariate. I want to truncate the suvival curve and only display the part with covariate value between 0 and 7. The following is the code I wrote:

Hello all, I am trying to use stepwise procedure to select covariates in Cox model and use bootstrap to repeat stepwise selection, then record how many times variables are chosen by step() in bootstrap replications. When I use step() (or stepAIC) to do model selection, I got errors. Here is the part of my code for (j in 1:mm){ #<--mm=10 for (b in 1:nrow(reg.bs)){ #<--bootstrap 10

Hard to follow data analysis without data. Try making your example reproducible [1][2][3] and post in plain text (a setting in your emailer). Read the Posting Guide mentioned in the footer to avoid other posting pitfalls. [1] http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example [2] http://adv-r.had.co.nz/Reproducibility.html [3]

Hi , I am trying to get some estimator based on lognormal distribution when we have left,interval, and right censored data. Since, there is now avalible pakage in R can help me in this, I had to write my own code using Newton Raphson method which requires first and second derivative of log likelihood but my problem after runing the code is the estimators were too high. with this email ,I provide

Hello, I have being trying to estimate the parameters of the?generalized?exponential distribution. The random number generation for the GE distribution is?x<-(-log(1-U^(1/p1))/b), where U stands for uniform dist. The data i have generated to estimate the parameters is right censored and the code is given below; The problem is that, the newton-Raphson approach isnt working and i do not know what

HI I am trying to analyse data which is left-censored (i.e. has values below the detection limit). I have been using the NADA package of R to derive summary statistics and do some regression. I am now trying to carry out regression on paired data where both my X and Y have left-censored data within them. I have tried various commands in R: rega = cenreg(Cen(conc, cens_ind) ~ Gp_ident))? with