similar to: Converting PROC NLMIXED code to NLME

Dear list: working with date/times I have come across a problem that ISOdate and ISOdatetime are too slow on large vectors of data. I was surprised just until I looked at the implementation and the man page: "ISOdatetime and ISOdate are convenience wrappers for strptime". In other terms, they convert data to character representation first in order to create a POSIXlt object that is then

Hi, I'm working with timeseries data. The values are every 5 seconds and each series can last up to 4-5 days. To generate the x-axis labels, I'm doing the following: ========================= # Variable for displaying hours on the x-axis rtime <<- as.POSIXct(round(range(timedata), "hours")) # Variable for displaying days on the x-axis stime <<-

To account for likely differences between families in naturalization rates, we fitted a generalized linear mixed model, using PROC NLMIXED in SAS10, with the naturalization rate per genus (that is, the number of naturalized species in a genus as a proportion of the total number of introduced species in a genus) as the response variable, a variable coding genera as containing at least one native

Dear all, I am using Windows and R 2.9.2 for my analyses. I have a large dataset and I am particularly interested in looking at time to an event for a continuous variable. I would like to produce a plot of log(relative risk) or relative risk (also known as hazard ratio) against the continuous variable. I have spent a long time looking for advice on how to do this but my search has proved

Dear all, I am facing problem with inserting the scheduled day of Observation in the dataset. In the dataset I have only  relative time (table 1) and not scheduled day of observation (day 1, 4, 8, 15, 22, 29, 36, 43). I would appreciate if any one could suggest me how to proceed. Eg: Table 1 The real dataset looks like this with Time, DV ... etc RTime   DV 1        101 4        95 8       

Hi all, I'm new to R and have the following problem: I have a 2 factor design (a has 2 levels, b has 3 levels). I have an object kidney.aov which is an aov(y ~ a*b), and when I ask for model.tables(kidney.avo, se=T) I get the following message along with the table of effects: Design is unbalanced - use se.contrast() for se's but the design is NOT unbalanced... each fator level

Dear R friends A while a go I sent an email to the epi-list and later to the help-list and no answer could fully illuminate my question. So Im trying again with a more specific matter. Im trying to work on a script (function) to analyse data from a diagnostic test meta-analysis with random effects. This was first described by an author using SAS witn PROC NLMIXED. Im not an expert in R and much

I'm trying to run a zero-inflated regression for a continuous response variable in R. I'm aware of a gamlss implementation, but I'd really like to try out this algorithm by Dale McLerran that is conceptually a bit more straightforward. Unfortunately, the code is in SAS and I'm not sure how to re-write it for something like nlme (if at all possible - with conditions etc). Does

Hello! Can anyone help me. I am using the posterior.mode from the result of glmmML. It apears to be different from the BLUe estimate of the RANDOM statement in PROC NLMIXED in SAS. Why is that? Thank you Ronen [[alternative HTML version deleted]]

Dear All, reading an article by Rodolphe Thiebaut and Helene Jacqmin-Gadda ("Mixed models for longitudinal left-censored repeated measures") I have found this program in SAS proc nlmixed data=TEST QTOL=1E-6; parms sigsq1=0.44 ro=0.09 sigsq2=0.07 sigsqe=0.18 alpha=3.08 beta=0.43; bounds $B!](B1< ro < 1, sigsq1 sigsq2 sigsqe >= 0; pi=2*arsin(1); mu=alpha+beta*TIME+a i+b i*TIME;

"btrfs subvolume list" gets a new option "--fields=..." which allows to specify which pieces of information about subvolumes shall be printed. This is necessary because this commit also adds all the so far missing items from the root_item like the received UUID, all generation values and all time values. The parameters to the "--fields" option is a list of items to

hi, everyone: i have a question on the reshape function. i have the following dataset : gene tissue patient1 patient2 patient3............. _________________________________________________ gene1 breast 10 20 50 gene2 breast 20 40 60 gene3 breast 100 200 300 which i hope to convert to the following format: gene patientID

I am one of only 5 or 6 people in my organization making the effort to include R/Splus as an analysis tool in everyday work - the rest of my colleagues use SAS exclusively. Today, one of them made the assertion that he believes the numerical algorithms in SAS are superior to those in Splus and R -- ie, optimization routines are faster in SAS, the SAS Institute has teams of excellent numerical

Dear R users - I have a list of patient identifiers and diagnoses from inpatient admissions. I would like to reorganize the list, presently in a long format to a wide format in reshape, but in the absence of a "time" element, I am uncertain how to do this - any help greatly appreciated. ID Dx A nausea A diabetes A kidney failure A heart attack A fever B fever B

I'd like to estimate an ordinal logistic regression with a random effect for a grouping variable. I do not find a pre-packaged algorithm for this. I've found methods glmmML (package: glmmML) and lmer (package: lme4) both work fine with dichotomous dependent variables. I'd like a model similar to polr (package: MASS) or lrm (package: Design) that allows random effects. I was

Dear List, How do I extract the approximate Wald test for the frailty (in the following example 17.89 value)? What about the P-values, other Chisq, DF, se(coef) and se2? How can they be extracted? ######################################################> kfitm1 Call: coxph(formula = Surv(time, status) ~ age + sex + disease + frailty(id, dist = "gauss"), data = kidney)

Hi everybody: I?m trying to rewrite some routines originally written for SAS?s PROC NLMIXED into LME4's glmer. These examples came from a paper by Nelson et al. (Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Models with Nonnormal Random Effects - 2006). Firstly the authors fit a Poisson model with canonical link and a single normal random effect bi ~ N(0;Sigma^2).The

Hi Folks, I'm seeking confirmation of something which is probably true but which I have not managed to find in the documentation. I have a binary response y={0.1} and a variable x and have fitted a probit response to the data with f <- glm( y~x, family=binomial(link=probit) ) and then, with a specified set of x-value X I have used the predict.glm function as p <- predict( f, X,

Hi Dear R-users I have a database with 18000 observations and 20 variables. I am running cox regression on five variables and trying to use survfit to plot the survival based on a specific variable without success. Lets say I have the following coxph: >library(survival) >fit <- coxph(Surv(futime, fustat) ~ age + rx, data = ovarian) >fit what I am trying to do is plot a survival

I have data I'd like to plot using the formula interface to boxplot. I call boxplot like so: with(mydata, boxplot(count ~ geno * tissue)) I get a boxplot with x axis labels like "wt.kidney". I would like to change the '.' to a newline. Where is this separator configured? Thanks, -Ed