Displaying 4 results from an estimated 4 matches for "miset".
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misset
2007 May 31
0
Using MIcombine for coxph fits
...39;))
compare.density(data=d, output=am.imp, var='trig')
compare.density(data=d, output=am.imp, var='platelet')
---------------
Since everything looks ok, fit Cox models to each of the 10 imputed
datasets using the functions of the mitools package.
---------------
library(mitools)
miset <- imputationList(list(am.imp[[1]],am.imp[[2]],am.imp[[3]],
am.imp[[4]],am.imp[[5]],am.imp[[6]],am.imp[[7]],am.imp[[8]],
am.imp[[9]],am.imp[[10]]))
mifit <- with(miset, coxph(Surv(time, status) ~ age + sex +
hepmeg + platelet + trt + trig))
mifit
---------------
The "mifit" o...
2007 Jun 07
1
MITOOLS: Error in eval(expr, envir, enclos) : invalid 'envir' argument
...#39;),
+ sqrts=c('futime'))
-- Imputation 1 --
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
<snip>
-- Imputation 50 --
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
21 22 23 24 25 26
> MIset <- imputationList(am.imp[1:M])
> mifit <- with(MIset,
+ coxph(Surv(futime, os) ~ age + symptoms + ctnm + smk))
Error in eval(expr, envir, enclos) : invalid 'envir' argument
2008 May 30
0
imputationlist, update, and recode
...mputationList. I run into trouble,
though, when I try to construct a scale across all the data sets. Is there
a simple way to do this?
(here's what I've been trying)
vars_to_impute = c("var1", ... "var50")
imputed <- amelia(data=vars_to_impute, m=5, outname="miset")
files.allmisets <- list.files(getwd(),pattern="miset*",full=TRUE)
allmis <- imputationList(lapply(files.allmisets, read.csv))
scale1_vars <- c("var1", "var2", "var3", ... "var20")
scale2_vars <- c("var21", "var22&q...
2008 May 28
1
manipulating multiply imputed data sets
...using MIM, but I've decided to go cold turkey on other
stats packages as a incentive for learning more about R. Most of the
recoding is for nominal variables, like race, religion, urbanicity,
and the like. So, for example, to recode race for my first dataset,
inmi1, I would do the following:
miset1$white <- recode(miset1$RACE, '1=1; else=0; ')
miset1$black <- recode(miset1$RACE, '2=1; else=0; ')
miset1$asian <- recode(miset1$RACE, '3=1; else=0; ')
miset1$hispanic <- recode(miset1$RACE, '4=1; else=0; ')
miset1$raceother <- recode(miset1$RACE,...