search for: covar2

...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 way of doing this? Example: #define function involving regression model with several covariates custom <- function(outcome, exposure, covar1, covar2, covar3){ model <- lm(outcome ~ exposure + covar1 + covar2 + covar3) expected <- predict(model) summary(expected) } library(MASS) attach(birthwt) custom(bwt, lwt, low, age, race) #Works when 3 covariates are specified custom(bwt,lwt,low,age) # Does not work with < or > 3 covar...

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 coxme.varcheck(ncluster, varlist, n, gvars, groups, sparse, rescale, : object 'kmat' not found Error in lme.cov.out$theta : $ operator is inv...

Hi, Im using geoR and I'm trying to do some predictions, based on an external trend. I'm having some problems specifying my model.control, specifically how do I define my model, and also the source of the covariate data at the prediction locations? I am assuming that the covariate data at the prediction locations should be imported to a geodata object along with the prediction location

Full_Name: Jerome Asselin Version: 1.6.1 OS: RedHat Linux 7.2 Submission from: (NULL) (142.103.173.179) Hello, I get a segmentation fault when I run the following code. I wouldn't expect meaningful results because my response variable contains only missing values. However, I would expect something like a regular error (not a segmentation fault). library(survival) data <-