search for: outcomment

Why the error is coming? even though the length of outcome.new$compkey and outcome.new$armkey were exactly same. Can anyone help? setwd("D:/AZ") library("RODBC") cdb_cnct <- odbcConnectExcel("AZIF_DC_GVK_NSCLC_MSALL_287papers_02072012_141450_v1_4.xls") outcomes <- sqlFetch(cdb_cnct, "Outcomes_info") odbcClose(cdb_cnct) rm(cdb_cnct)

Hi, Could someone help with coding this in R? I need to select one row per patient i in clinic j. The data is organized similar to that shown below. Two columns - patient i in column j identify each unique patient. There are two columns on outcome. Some patients have multiple rows with each row representing one visit, coded for in the column, visit. Some patients have just one row indicating

Hi, I am simulating an event that has 15 possible outcomes and I have a vector 'pout' that gives me the probability of each outcome - different outcomes have different probabilities. Does anyone know a simple way of simulating the outcome of my event? If my event had only two possible outcomes (0/1) I would pick a uniform random number between 0 and 1 and use it to choose between the two

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the

HI, Dear R community, I am using the following codes to do the som. I tried to label the notes by the majority vote. either through mapping or prediction. I attached my output, the left one dont have any labels in the note, the right one has more than one label in each note. I need to have only one label for each note either by majority vote or prediction. Can anyone give some suggestions or

Dear R-help, I am writing some simulation code to create multiple sets of time-to-event clinical trial data (for use in meta-analysis). Within each trial, I want to apply censoring via simulation of uniform variables (with minimum zero and maximum the median outcome time for that particular trial). I have started by pre-allocating a matrix which has 3 columns; one for trial number, one for

Hi List, I'm hoping to get opinions for enhancing the efficiency of the following code designed to take a vector of probabilities (outcomes) and calculate a union of the probability space. As part of the union calculation, combn() must be used, which returns a matrix, and the parallelized version of lapply() provided in the multicore package requires a list. I've found that

Hello, I'm using a complex survey dataset and my goal is to simply spit out a bunch of probability-weighted outcome variable means for the different levels of covariate. So I first define the structure of the study design (I'm using the CDC's NHANES data): dhanes <- svydesign(id=~PSU, strat=~STRATA, weight=~lab_weight, data=final, nest=TRUE) No problem there. Now I use the

Dear list members, I try to simulate an incomplete block design in which every participants receives 3 out of 4 possible treatment. The outcome in binary. Assigning a binary outcome to the BIB or PBIB dataset of the package SASmixed gives the appropriate output. With the code below, fixed treatment estimates are not given for each of the 4 possible treatments, instead a kind of summary

En el correo anterior se me olvidó mencionar que trabajo con Rstudio El 28 de mayo de 2015, 15:18, MªLuz Morales <mlzmrls en gmail.com> escribió: > Hola, > soy nueva en esta lista y también en R. Yo he realizado un script en R > que carga dos archivos csv, uno de ellos con casi 2 millones de filas. El > programa carga esos archivos a data frame, y se trata simplemente de >

Hola, quiero compartir con vosotros mi problema y la solución que me han planteado. Mi programa carga Outcomes.csv y Set-A.csv (descargados de http://garrickadenbuie.com/blog/2013/04/11/visualize-physionet-data-with-r/, apartado Getting Started --> the code and the data set) de unos 50MB entre los dos. Mi código era: # Transforma csv a data frame seta <- read.csv('Set-A.csv');

Hello, I am using R to analyze a large multilevel data set, using lmer() to model my data, and using anova() to compare the fit of various models. When I run two models, the output of each model is generated correctly as far as I can tell (e.g. summary(f1) and summary(f2) for the multilevel model output look perfectly reasonable), and in this case (see below) predictor.1 explains vastly more

Hola, Si no tienes inconveniente en compartir tu conjunto de datos (puedes dejarlo en un Dropbox y compartir enlace) o incluir una salida de la variables: "seta" y "outcomes" (función "save.image()") con eso podemos darte alguna solución mucho más rápida que la que planteas. En tu código con un bucle estás tratando de rellenar una lista que son los diferentes

Dear All, I have some questions about probit regressions. I saw a nice introduction at http://bit.ly/bU9xL5 and I mainly have two questions. (1) The first is almost about data manipulation. Consider the following snippet ################################################## mydata <- read.csv(url("http://www.ats.ucla.edu/stat/r/dae/binary.csv")) names(mydata) <-

Hi all! I've got this error while running example(Glm) library("rms") > example(Glm) Glm> ## Dobson (1990) Page 93: Randomized Controlled Trial : Glm> counts <- c(18,17,15,20,10,20,25,13,12) Glm> outcome <- gl(3,1,9) Glm> treatment <- gl(3,3) Glm> f <- glm(counts ~ outcome + treatment, family=poisson()) Glm> f Call: glm(formula = counts ~

Dear R users I am analysing data from a group of twins and their siblings. The measures that we are interested in are all correlated within families, with the correlations being stronger between twins than between non-twin siblings. The measures are all calculated from survey answers and by definition have limited ranges (e.g. -5 to +5), though within the range they are approximately normally

Hi dear R-help memebers, When building a CART model (specifically classification tree) using rpart, it is sometimes obvious that there are variables (X's) that are meaningful for predicting some of the outcome (y) variables - while other predictors are relevant for other outcome variables (y's only). *How can it be estimated, which explanatory variable is "used" for which of

I will like to analyse a binary cross over design using the random effects model. The probability of success is assumed to be logistic. Suppose as an example, we have 4 subjects undergoing a crossover design, where the outcome is either success or failure. The first two subjects receive treatment "A" first followed by treatment "B". The remaining two subjects receive

Hello , I am hoping for some advice. I want to run a repeated measures ANOVA. The primary problem is that my attempt to remove missing values created a dataset of missing values. The data set consists of 92 rows (1 row per participant) x 186 variables. The steps of the analysis undertaken are outlined below (#). Any assistance is appreciated in relation to how to remove the missing values so

The concrete problem is that I am refereeing a paper where a confidence interval is presented for the risk ratio and I do not find it credible. I show below my attempts to do this in R. The example is slightly changed from the authors'. I can obtain a confidence interval for the odds ratio from fisher.test of course === fisher.test example === > outcome <- matrix(c(500, 0, 500, 8),