similar to: Sequence for repeated numbers

Dear R-users, I'm running a lmer model using the lme4 package. My dependent variable is dichotomous and I'm using the "binomial" family. The results are slightly different from the HLM results based on a Bernoulli distribution. I read that a Bernoulli distribution is an extension of a binomial distribution. Is that right? If so, how can I adapt my R model to a Bernoulli

Hello all, I want to specify a minimum number of valid arguments for the mean function--I have 5 variables but I want the mean only of cases that have at least 3 valid answers. What is the best way to do that? Thank you very much! Luana [[alternative HTML version deleted]]

Hello, I have a dataframe (var_1, var_2, ..., var_n) and I would like to export summary statistics to Latex in the form of a table. I want specific summary statistics by crossing numerous variables 2x2 AT ONCE. In each cell I would like sometimes to have the median (Q1 - Q3), or frequency and proportion, etc. CrossTable, xtab, etc... do not allow for multiple 2 by 2 crosstabulation. The table

Please, I have a problem with nonlinear quantile regression. My data shows a large variability and the quantile regression seemed perfect to relate two given variables. I got to run the linear quantile regression analysis and to build the graph in the R (with quantreg package). However, the up part of my data dispersion seems a positive exponential curve, while the down part seems a negative

Dear R help list, I am modeling some survival data with coxph and survreg (dist='weibull') using package survival. I have 2 problems: 1) I do not understand how to interpret the regression coefficients in the survreg output and it is not clear, for me, from ?survreg.objects how to. Here is an example of the codes that points out my problem: - data is stc1 - the factor is dichotomous

Thanks for sharing the questions and responses! Is it possible to appreciate how much the coefficients matter in one or the other model? Say, using Biau's example, using coxph, as.factor(grade2 == "high")TRUE gives hazard ratio 1.27 (rounded). As clinician I can grasp this HR as 27% relative increase. I can relate with other published results. With survreg the Weibull model gives a

I am looking for help to analyze an unbalanced AB/BA cross-over design by requesting the type III SS ! # Example 3.1 from S. Senn (1993). Cross-over Trials in Clinical Research outcome<-c(310,310,370,410,250,380,330,270,260,300,390,210,350,365,370,310,380,290,260,90,385,400,410,320,340,220) subject<-as.factor(c(1,4,6,7,10,11,14,1,4,6,7,10,11,14,2,3,5,9,12,13,2,3,5,9,12,13))

1. The weibull is the only distribution that can be written in both a proportional hazazrds for and an accelerated failure time form. Survreg uses the latter. In an ACF model, we model the time to failure. Positive coefficients are good (longer time to death). In a PH model, we model the death rate. Positive coefficients are bad (higher death rate). You are not the first to be confused

Dear R help gurus, I have the following problem and I would be delighted if you could help me. >From a large (1500) cohort of patients we have been taking some measurements (ECG measurements, but its not important). The measurements are ordinal in 4 grades (Grade I-IV, grade IV being the most severe form). Every patients has been measured several times (usually once per year). The

Dear List: I am working to generate graphs for individual students that will be created through a series of loops in Sweave. Before doing so, I am still trying to design the graph. The code for creating the barplot is below with some sample datapoints just made up for now. Ultimately, this chart will take data from an lme object using longitudinal student data. So, the dots represent the

Hello everyone, I want to estimate the reliability *of the level*-*1 random coefficients (the one *reported in the HLM output) using the software R. Does anyone know how to get this statistic from R? I'm using the function "lmer" of the package "lme4" to estimate multilevel models. I tried to use the formula but I can't find specific information such as the sigma

I have a need to find the order of number to get a scaling parameter as a power of 10. I have a function that works *so far*, but it is ugly and probably buggy. In the interest of avoiding code-based outliers in my data, I thought I would ask if anyone here has a better way. > scl <- function(x){ + length(charToRaw(format(trunc(x), scientific = F)))-1} > a <- 123456789 > b <-

Dear R Users, In the expresso function, which combination of these methods for data pre-processing (when using affymetrix oligo arrays) is the best: bgcorrect.metod = rma rma2 mas normalize.method = qspline quantiles loess pmcorrect.method = pmonly subtractmm mas summary.method = liwong avgdiff medianpolish mas There are many options within each method. I would appreciate a hint on the best

I was trying to use memory.size() to determine whether a code loop I am executing created a memory leak, since one replicate of the simulation takes 670.98 seconds according to proc.time(), while 5 replicates takes 170762 seconds. So I set it up as: memA <- memory.size() looping code... memB <- memory.size() memA returns as 9.3, and memB returns 11.3. I'm not familiar with

I was wondering if there is a way to get read in a single keystroke at a time in R as a string, akin to ncurses-style interfaces. I looked into readLines, readChar, etc. using stdin, but these all require the use of an end of line. Has anyone ever had need to do this or have any ideas on how to do this? Thanks, Jon PS I apologize if this double-sends, but I am having mail client issues.

Hello R List, I'm trying to run a linear mixed effects analysis on fairly large datasets with lmer (from the lme4 package) on a 32-bit Windows machine running XP with 3 GB of RAM. It's not working. (details below) I've researched the ff and bigmemory packages, but it appears they won't handle the mixed mode dataset I'm analyzing. It has some character fields for the

Dear Masters, I'm driving crazy with the lowess() function.... my intent is smoothing confidence intervals for predicted y values in a linear model lm() setting since in the predict() function there exists an option for predicting NA values, I instead encounter problems when I fit a missing values x variable to the predicted terms.....,impossible task!!! I've been banging on my head

Is it possible to send data from an executing R script, to external R script files, on linux in this case, get that external R script to execute, and pass results back to the central script? If so what commands should I be looking at? I've googled for R and external, stuff like that but no luck. The reason for the question is that I need to generate simulation data, then perform several

Hi I am new to R and am trying to set up a two-dimensional array/matrix with the elements defined by the function similar to below. Been trying to use outer with apply but can't seem to get the indexing quite right. Is their a simple way of accomplishing this task ?? ----- / | 1 x < 0.5 & y < 0.5 |

Hi, I'm running R 2.11 Does anyone know if it possible to transform one character vector to one character string ? Many thanks Benoit -- Benoit Wastine Laboratoire des Sciences du Climat et de l?Environnement (LSCE/IPSL) CEA-CNRS-UVSQ CE Saclay Orme des merisiers B?t 703 - Pte 13A 91191 Gif sur Yvette Cedex France Tel : 33 (0)1 69 08 21 97 Fax : 33 (0)1 69 08 77 16