search for: outcomes

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) sink("Dependency checks.log") outcomes[(outcomes$Comparator != 9999), ] -> outcome compkey <- paste(outcome$Comparator, outcome$Comparator_Subarm, sep='_') armkey <- paste(outcome$Arm_ID,...

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 a...

...etermine confidence intervals for the effect sizes derived below and would appreciate tips on that. set.seed(123456) # Make session reproducible # Set up the treatment factor with three levels and 100 observations # each treatment <- factor(c(rep(0, 100), rep(1, 100), rep(2, 100))) # Simulate outcomes outcome <- rep(NA, 300) outcome[treatment==0] <- rnorm(100, 10, 5) # baseline: mean=10, sd=5 outcome[treatment==1] <- rnorm(100, 30, 5) # effect size 4 outcome[treatment==2] <- rnorm(100, 40, 5) # effect size 6 # Check effect sizes (Cohen's d) cohens.d <- function (x, y) {(mean(...

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 parallelization is very necessary for vectors of outcomes...

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

...si R es lento en esta > operación o es que mi código no está optimizado y no estoy haciéndolo de la > forma correcta. > El código de mi programa es el siguiente: > > > #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ > ## Set-A.csv y Outcomes.csv deben estar en el directorio actual > # Transforma csv a data frame > seta <- read.csv('Set-A.csv'); > outcomes <- read.csv('Outcomes-A.csv'); > > ids <- as.character(unique(outcomes$RecordID)); > ## Número de RecordsID distintos > Length_ids <-...

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'); outcomes &lt...

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 agregados y esto se puede hacer mucho más rápido (segundos) con varios paquetes: data.table, dp...

...me mydatanew (see some of its entries below) > mydatanew x1 successes failures 1 220 0 1 2 300 1 2 3 340 1 3 4 360 0 4 5 380 0 8 [...................] where for every value of x1 I count the number of 0 and 1 outcomes (namely number of failures and number of successes). This is equivalent to having a full list of x1 values with an associated 0/1 outcome (I have simply counted them) hence it is all the info I need to again perform a logit regression of the binary outcome on x1, but the data format is now dif...

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

...re relevant for other outcome variables (y's only). *How can it be estimated, which explanatory variable is "used" for which of the predicted value in the outcome variable?* Here is an example code in which x2 is the only important variable for predicting "b" (one of the y outcomes). There is no predicting variable for "c", and x1 is a predictor for "a", assuming that x2 permits it. How can this situation be shown using the an rpart fitted model? N <- 200 set.seed(5123) x1 <- runif(N) x2 <- runif(N) x3 <- runif(N) y <- sample(letters[1...

...tic. 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 treatments in the reverse order. The outcomes for the subjects is the sequence "AB" are as follows: (0,1) and (0,0). While the outcomes for the subjects in the sequence "BA" are (1,1) and (1,0). How can i analyse this using R. I have done the problem with PROC NLMIXED in SAS, I simply want to compare the results from SA...

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),