getting errors when running this file
http://r.789695.n4.nabble.com/file/n3605311/il1ra_status.txt
il1ra_status.txt
Age.co-c(dat2b$Dr_Age)
Sex.co-as.factor(dat2b$Sex)
casecont.co-as.factor(dat2b$Self_T1D)
stat.co-as.factor(dat2b$status)
m -
the examples work great but it gives me this error on this file
http://r.789695.n4.nabble.com/file/n3603737/il1ra_L.csv il1ra_L.csv
Age.co-c(dat2b$Dr_Age)
Sex.co-as.factor(dat2b$Sex)
casecont.co-as.factor(dat2b$Self_T1D)
stat.co-as.factor(dat2b$status)
m -
Thanks, for pointing out the package e1071, the example for matchControls is
exactly what I am looking for, however how can I add additional factors to
match for.
library(e1071)
Age.case - 40 + 5 * rnorm(50)
Age.cont - 45 + 10 * rnorm(150)
Age - c(Age.case, Age.cont)
Sex.case - sample(c(M, F),
On Jun 3, 2011, at 1:37 PM, 1Rnwb wrote:
Thanks, for pointing out the package e1071, the example for
matchControls is
exactly what I am looking for, however how can I add additional
factors to
match for.
library(e1071)
Age.case - 40 + 5 * rnorm(50)
Age.cont - 45 + 10 * rnorm(150)
Age -
I hope this modified example will work
set.seed(100)
disease-paste(rep(c('y','n'),50))
gender-c(paste(rep(c('m','f'),25)), paste(rep(c('f','m'),25)))
mcp-rnorm(100, mean=1000,sd=600)
age-rnorm(100,mean=32,sd=20)
dat-data.frame(disease=disease,sex=gender,Dr_age=age,MCP=mcp)
On May 30, 2011, at 11:30 AM, 1Rnwb wrote:
I hope this modified example will work
set.seed(100)
disease-paste(rep(c('y','n'),50))
gender-c(paste(rep(c('m','f'),25)), paste(rep(c('f','m'),25)))
mcp-rnorm(100, mean=1000,sd=600)
age-rnorm(100,mean=32,sd=20)
Hello R gurus, I have a data set from which i have to extract the gender and
age matched rows from controls and disease group
disease-paste(rep(c('y','n'),11))
gender-paste(rep(c('m','f'),11))
mcp-rnorm(700,1400)
age-rnorm(32,34)
On May 26, 2011, at 11:19 AM, 1Rnwb wrote:
Hello R gurus, I have a data set from which i have to extract the
gender and
age matched rows from controls and disease group
You need to define what you mean by age-matched. Your example
creates a very narrow age range which further adds
8 matches
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