similar to: Reasons to Use R (no memory limitations :-))

I'm working with a nested model (mixed). I have four factors: Patients, Tissue, sex, and tissue_stage. Totally I have 10 patients, for each patient, there are 2 tissues (Cancer vs. Normal). I think Tissue and sex are fixed. Patient is nested in sex,Tissue is nested in patient, and tissue_stage is nested in Tissue. I tried aov and lme as the following, > aov(gene ~ tissue + gender +

Dear useRs, Am trying to use xtable on the following data.frame and I don't get what I expect: example.table <- data.frame(rbind( c("Gender"," "," "," "), cbind(rep(" ",2),c("Male","Female"),c(3.0,4.0),c(3/7,4/7)) )) colnames(example.table) <- c(" "," ","number of

Hello, I have a data which looks like below: Some of the patients have multiple diagnosis. ID(200 patients)   Diag (100 unique Diag-200 in general)   Proc (50 uniqe Proc)  DOB (200)   Gender (200)    a                           daig1 b                           diag2 c                            diag1 I want to reformat this data to : ID   diag1 diag 2 diag 3..  diagx   proc1   proc2  

Hi All, I have a problem with rbind. I have data that consist of weight height .. etc of 1000 patients. I would like to find the mean and the standard deviation ( for the weight , height etc) for each gender. data<-read.table("data.txt", header=T, sep='\t') fdata=NULL for (i in 1:50){ nn<-names(X)[i] m<-tapply(X[,i],data$gender,mean,na.rm=T) s<-tapply(X[,i],

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Brokers Move On ERMX! EntreMetrix Inc. (ERMX) $0.18 Heavy trading today as ERMX announced its launch of digital support tools for its portfolio companies. Brokers are getting ahead of this steady climb as they grab up large blocks of shares for there clients. Look at the numbers and get on ERMX Friday morning! KNEEguru: Are there any contraindications for the procedure? if the laws of physics

Hello, I am running: R version 2.10.0 (2009-10-26) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 on a RedHat Linux box with 48Gb of memory. I am trying to create a model.matrix for a big model on a moderately large data set. It seems there is a size limitation to this model.matrix. > dim(coll.train) [1] 677236 128 > coll.1st.model.mat <-

Hello all I am a very new R user I am used to using STATA My problem: I want to build a Cox model and validate this. I have a large number of clinical relevant factors and feel the need to reduce these. Meanwhile I have some clinical variables I deem sufficiently important to force into the model regardless of AIC or p value. This is my present log over commands

Hi all, I have this microarray large microarray data set (ALL) from which I would like to subset or extract a set of data based on a factor ($mol.biol). I looked up some example of subsetting in, picked up two commands and tried both but I got error messages as follows > testset <- subset(ALL, ALL$mol.biol %in% c("BCR/ABL","ALL1/AF4")) >> Error in

Hello, Say I have 2 columns, bmi and gender, the first being all the values and the second being male or female. How would I subset this into males only and females only? I have searched these fora and read endlessly about select[] and split() functions but to no avail. Also the table is not ordered. bmi gender -> bmi gender + bmi gender 1 24.78 male

In response to those who asked for a better explanation of what the Vilno software does, here's a simple example that gives some idea of what it does. LABRESULTS is a dataset with multiple rows per patient , with lab sodium measurements. It has columns: PATIENT_ID, VISIT_NUM, and SODIUM. DEMO is a dataset with one row per patient, with demographic data. It has columns: PATIENT_ID, GENDER.

Hello, I need to plot the means of some outcome for two groups (control vs intervention) over time (discrete) on the same plot, for various subsets such as gender and grade level. What I have been doing is creating all possible subsets first, using the aggregate function to create the means over time, then plotting the means over time (as a simple line plot with both control & intervention

Currently in R, constructing a string containing values of variables is done using 'paste' and can be an error-prone and traumatic experience. For example, when constructing a db query we have to write, paste("SELECT " value " FROM table where date ='",cdate,"'") we are getting null result from it, because without (forgotten...)

Dear R contributors, I have a problem in selecting in an efficient way, rows of a data frame according to a condition, which is a part of a row name of the table. The data frame is made of 64 rows and 2 columns, but the row names are very long but I need to select them according to a small part of it and perform calculations on the subsets. This is the example: X Y "Unique to

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

Dear R-user, I have a data like this below, age <- c("young","mid","old") married <- c("no","yes") income <- c("low","high","medium") gender <- c("female","male") I want to make some of combination data like these, age.income.dat <- expand.grid(age,

Dear R people, I am having a problem with modeling the following SAS code in R: Class ID Gr Hemi Region Gender Model Y = Gr Region Hemi Gender Gr*Hemi Gr*Region Hemi*Region Gender*Region Gender*Hemi Gr*Hemi*Region Gender*Hemi*Region Gr*Gender*Hemi*Region Random Intercept Region Hemi /Subject = ID (Gr Gender) I.e., ID is a random effect nested in Gr and Gender, leading to ID-specific

Hi everyone, I'm having some difficulty getting "simple effects" for the ols() function in the rms package. The example below illustrates my difficulty -- I'll be grateful for any help. #make up some data exD <- structure(list(Gender = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L), .Label = c("F", "M"), class = "factor"),

dear all, I'm building new dataframes from bigger one's using e.g. columns F76, F83, F90: JJ<-data.frame( c( as.character(rep( gender,3))) , c( F76,6- F83, F90) ) Looking into JJ one has: c.as.character.rep.gender..8... c.6...F73..F78..F79..F82..6...F84..F94..F106..F109 1 w 2 2 w

Hello, I am trying to get the same values for the adjusted means and standard errors using R that are given in SAS for the following data. The model is Measurement ~ Age + Gender + Group. I can get the adusted means at the mean age by using predict. I do not know how to get the appropriate standard errors at the adjusted means for Gender using values from predict. So I attempted to get them