similar to: variable substitution

Friends I can't quite find a direct answer to this question from the lists, so here goes: I have several dataframes, 200+ columns 2000+ rows. I wish to script some operations to perform on some of the variables (columns) in the data frames not knowing what the column number is, hence have to refer by name. I have variable names in a text file "varlist". So, something like this:

Hi! Would anyone know how to generate a list of variable names from a data frame by the class of the variable? I have large tables with different numbers of columns and am trying to script some rote analyses. There are several categorizing variables (factors) and many response variables (integers and numeric). I want to extract a list of classifier column names in one list and response variable

Hi! I need to conditionally update a dataframe field based on values in other fields and can't find even how to search for this right. Sorry if this has been asked before. But, specifically, I have a 490 X 221 dataframe and need to count, by row, how many fields in Dataframe$field_a...Dataframe$field_zz are non-null and enter this value in Dataset$ABCtaskNum. I have field name

Hello, I have been trying to write a small program to generate automatic plots. What I want is to draw boxplots for some variables in my data frame, contrasting with a variable called 'missing' that has value 1 if some variable in a concrete case has at least one missing value, in order to check if the cases that don't enter in my analysis are biased. The first attempt was to

Hi,I use the 'lmekin' model of the 'kinship' package of R in order to estimate heritability. I want to estimate the confidence interval of the variance coefficient and so I should use a bootstrap simulation. The pedigree file has 1386 subjects so I create a kinship matrix [1386*1386].This is the code of R I use: kfit2 <- lmekin(IT~1+AGE +(1|ID), dati1,

Hello, applying a function to a list of variables I face the following problem: Let's say I want to compute tables for several variables. I could write a command for every single table, like bravo<-c(1,1,2,3,5,5,5,);charly<-c(7,7,4,4,2,1) table(bravo); table(charly) > table(bravo); table(charly) bravo 1 2 3 5 2 1 1 3 charly 1 2 4 7 1 1 2 2 The results are two tables with the

We got some errors and eventually figured out that parallel::clusterExport second argument is "varlist" while in snow::clusterExport it is "list". The user had loaded parallel first, but did something else which inadvertently loaded snow, then clusterExport failed because we had "varlist" and not "list". Are these different on purpose? pj -- Paul E.

I am making the question clear. Please help. > Dear R experts > > I was using kinship package to fit mixed model with kinship matrix. > The package looks like lme4, but I could find a way to extract p-value > out of it. I need to extract is as I need to analyse large number of > variables (> 10000). > > Please help me: > > require(kinship) > > #Generating

Hi, I want to subset a data frame if one of the variables matches any in a list. I could of course do something like this: subset(dataset, var == 1 | var == 2 | var ==3) but that's tedious. I tried varlist = c(1,2,3,4) subset(dataset, any(var == varlist)) but it doesn't work because 'any' doesn't go row-by-row and hence always returns TRUE. Is there any simple way to do this?

Hi! I'm a recent convert from Stata, so forgive my ignorance. In Stata, I can write foreach loops (example below) foreach var of varlist p1-p14 { foreach y of varlist p15-p269 { reg `var' `y' } } It's looping p1-p15, p1-p16...., p1-p269, p2-p15, p2-p16,... p2-p269,... variable pairs. How can I write something similar in R? I 'tried' understanding the

Hello All, I have a toy dataframe like this. It has 8 columns separated by tab. Name SampleID Al1 Al2 X Y R Th rs191191 A1 A B 0.999 0.09 0.78 0.090 abc928291 A1 B J 0.3838 0.3839 0.028 0.888 abcnab A1 H K 0.3939 0.939 0.3939 0.77 rx82922 B1 J K 0.3838 0.393

Hi,? I'm trying to obtain a table of coefficients and confidence intervals from a logistic regression analysis in R. My code is as follows: # read in csv file datafile<-read.csv("file.csv", row.names=1) # read in the variable list varlist<-names(datafile)[66:180] models<-lapply(varlist, function(x) {glm(substitute(outcome ~ i, list(i=as.name(x))), data=datafile, family =

Hello all I have what seems like a simple question but have not been able to find an answer on the forum. I'm trying to define a function which involves regression models and a large number of covariates. I would like the function to accept any number of covariates and, ideally, I would like to be able to enter the covariates in a group (e.g. as a list) rather than individually. Is there any

The survexp function can fail when called from another function. The "why" of this has me baffled, however. Here is a simple test case, using a very stripped down version of survexp: survexp.test <- function(formula, data, weights, subset, na.action, rmap, times, cohort=TRUE, conditional=FALSE, ratetable=survexp.us, scale=1, npoints, se.fit,

I've been fighting a CMD check error for coxme this morning.?? I thought I had it fixed, but instead I had forgotton --as-cran on my last test run.? So the version just submitted to CRAN has warning messages in the log. I think it is an issue with CRAN.?? I've sent a message to R-devel asking for help.?? Since then, as a guess, I renamed my internal "ismat" function to

# I'm new to R and am trying to get the hang of how it handles # dataframes & loops. If anyone can help me with some simple tasks, # I'd be much obliged. # First, i'd like to generate some random data in a dataframe # to efficiently illustrate what I'm up to. # let's say I have six variables as listed below (I really # have hundreds, but a few will illustrate the point). #

Dear R users, I'm using the function coxme of the package coxme in order to build Cox models with complex random effects. Unfortunately, I sometimes get surprising estimations of the variances of the random effects. I ran models with different fixed covariates but always with the same 3 random effects defined by the argument varlist=coxmeMlist(list(mat1,mat2,mat3), rescale = F, pdcheck = F,

Dear all, I am using "coxme" function in Kinship library to fit random treatment effect nested within centre. I got 3 treatments (0,1,2) and 3 centres. I used following commands, but got an error. > ugroup=paste(rep(1:3,each=3),rep(0:2,3),sep='/') > mat1=bdsmatrix(rep(c(1,1,1,1,1,1,1,1,1),3),blocksize=rep(3,3),dimnames=list(ugroup,ugroup)) >

Hello, All, GWAF 1.2 R.Version() is below. system(lme.batch.imputed( phenfile = 'phenfile.csv', genfile = 'CARe_imputed_release.0.fhsR.gz', pedfile='pedfile.csv', phen='phen1', covar=c('covar1','covar2'), kinmat='imputed_fhs.kinship.RData', outfile='imputed.FHS.IBC.GWAF.LME.output.0.txt' )) Gives the error messages: Error in

I really like these Trellis graphics but how do I get this code to skip the missing? logreg<-read.csv("logreg.csv", header=TRUE, sep=",", na.string=" ") attach(logreg) bwplot(yesno~bc_pcb_tot |varlist, data=logreg, main="Box Cox PCB transformation", auto.key=TRUE, fontfamily = "HersheySans" ) Dean Sonneborn M.S. Public Health Sciences *