[R] 'fractal' package

ze the 'fractal' package. Many Thanks in Advance, David Paul [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do r

Re: [R] propensity scores & imputation

ley Breathed in his "Bloom County" comic strip ) On Thu, Mar 16, 2017 at 10:42 AM, David Paul wrote: > Hi, > > > > Many thanks in advance for whatever advice / input I may receive. > > > > I have a propensity score matching / data imputation question. The

[R] propensity scores & imputation

Hi, Many thanks in advance for whatever advice / input I may receive. I have a propensity score matching / data imputation question. The purpose of the propensity score modeling is to put subjects from two different clinical trials on a similar footing so that a key clinical measurement

[R] plotting varclus( ) results

dimension reduction. Is it possible to modify the plot so variable names are color-coded? For example, half of the variable names in red and half in blue. Kind Regards, David Paul PGP.sig Description: PGP signature __ R-help@r

[R] obtaining prediction intervals from lrm() in rms library

Hi, Apologies if this is a silly question -- I am just now learning how to use some of the basic functions in the rms library. I have been using foo.dist <- datadist(foo.frame) options(datadist='foo.dist') lrm.model <- lrm(binary.outcome ~ rcs(contin.var,5)+categ.var, data = foo.f

[R] transcan() imputation of categorical (factor) variables

Hi, and thanks in advance. I have used the following to try to obtain singly-imputed values for missing data comprising no more than 15% of any variable in the data: > library(Hmisc) > some.df = read.csv("N:/.../some.csv", header = TRUE, stringsAsFactors = TRUE) > some.trans <- transcan(~ contin