similar to: PROC DISCRIM vs. lda( ) in MASS

Hello: Please help me through my confusion. I am having trouble reconciling the difference between what I believe is the conventional definition of an odds ratio for a 2-by-2 table and the output produced by fisher.test() in R. Consider the following example: > Discrim <- matrix(c(1,10,24,17), + nr = 2, + dimnames = list(AGE = c('young', 'old'), +

Pass to --commands-from-file the name of a file containing customization commands in each line, as if they were specified as command line arguments. This eases the reuse of commands among different builder/customize/sysprep invocations. --- builder/cmdline.ml | 3 +- customize/customize_run.ml | 5 +++ generator/customize.ml | 98 ++++++++++++++++++++++++++++++++++++++++++++-- 3

I am trying to write the following code in R. The code works in S+ and i am trying to do the program in R. x=discrim(admit~gpa+gmat,prior=c("uniform"),data=data.mm) i wrote the following in R: x=lda(admit~gpa+gmat,data=data.mm) i could not figure out how to write prior=c("uniform") in R. I would get an error every time. I think that it has something to do with

Hello R people I now know how to run my discriminant analysis with the lda function in MASS: lda.alain=lda(Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, gr, CV = FALSE) and it works fine. But I am missing a test and cannot find any help on how to get it, if it exist. The "S" equivalent: discrim(structure(.Data = Groupes ~ Ht.D0 + Lc.Dc + Ram + IDF, class = "formula"), data = gr,

Add a new Getopt module to mllib, to parse command line arguments with handlers close to the ones used with Arg, but using getopt(3) (actually getopt_long_only) to do the real parsing. This allow us to provide options for OCaml tools with a syntax similar to the C tools, and use the additional features getopt offers and Arg does not. Do a single-step conversion of Common_utils and all the OCaml

Hi! Will you please ask a newbie's question? Need linear discriminant analysis, need to capture discrim. function coefficiemts for further processing. Using R ver 1.4.0 package MASS function lda() Help says resulting object should have "components" (among others): means, scaling, svd, but program returns NULL for all of them Thank you!

Hopefully a quick question: is there a package/routine to perform Box's M (like proc discrim in SAS) or some other test for whether two multivariate samples (presumed multivariate normal) have the same v-c matrix? I've looked, but can't find it. If not, I can certainly code it up myself (and perhaps submit it for inclusion in the mvtnormtest package), but I wanted to check if it

Hi I am trying to understand how to get the validate() function in Design to work with the subset option. I tried this: ovarian.cph=cph(Surv(futime, fustat) ~ age+factor(ecog.ps)+strat(rx), time.inc=1000, x=T, y=T, data=ovarian) validate(ovarian.cph) #fine when no subset is used, but the following two don't work: > validate(ovarian.cph, subset=ovarian$ecog.ps==2) Error in

Dear All, I have just estimated this model: ----------------------------------------------------------- Logistic Regression Model lrm(formula = Y ~ X16, x = T, y = T) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 82 LR chi2 5.58 R2 0.088 C 0.607 0

Hi, I am trying to run a simple logistic regression using lrm() to calculate a odds ratio. I found a confusing output when I use summary() on the fit object which gave some OR that is totally different from simply taking exp(coefficient), see below: > dat<-read.table("dat.txt",sep='\t',header=T,row.names=NULL) > d<-datadist(dat) > options(datadist='d')

Dear r-help I am trying to run LDA on a training data set, and test it on another data set with the same variables. I found examples using crossvalidation, and using training and testing data sets set up with sample, but not when they are preassigned. Here is what I tried # FIRST SET UP A DATAFRAME WITH ALL THE DATA AND CREATE NEW VARIABLES traintest1 <-

Dear Sir or Madam? I am a doctor of urology,and I am engaged in developing a nomogram of bladder cancer. May I ask for your help on below issue? I set up a dataset which include 317 cases. I got the Binary Logistic Regression model by SPSS.And then I try to reconstruct the model ?lrm(RECU~Complication+T.Num+T.Grade+Year+TS)? by R-Project,and try to internal validate the model through

Add simple operations for RHEL guests using subscription-manager, so it is possible to e.g. install software on them. --- builder/Makefile.am | 1 + builder/virt-builder.pod | 47 ++++++++++++++++++ customize/Makefile.am | 2 + customize/customize_run.ml | 34 +++++++++++++ customize/subscription_manager.ml | 53 ++++++++++++++++++++

Hi, this is the v2 of a series introducing basic support for registering/attaching/unregistering RHEL guests using subscription-manager, so it is possible to do for example: $ virt-customize -a rhel-guest.qcow2 \ --sm-credentials user:file:/path/to/password-file --sm-register \ --sm-attach file:/path/to/pool-file \ --install pkg1 --install pkg2 .. \ --sm-remove --sm-unregister

lrm does some binning to make the calculations faster. The exact calculation is obtained by running f <- lrm(...) rcorr.cens(predict(f), DA), which results in: C Index Dxy S.D. n missing 0.96814404 0.93628809 0.03808336 32.00000000 0.00000000 uncensored Relevant Pairs Concordant Uncertain 32.00000000

The second patch is obviously not complete yet - for discussion only. Rich.

On Fri, Jun 24, 2016 at 05:42:37PM +0200, Pino Toscano wrote: > Add a new Getopt module to mllib, to parse command line arguments with > handlers close to the ones used with Arg, but using getopt(3) (actually > getopt_long_only) to do the real parsing. This allow us to provide > options for OCaml tools with a syntax similar to the C tools, and use > the additional features getopt

Dear list, I'm currently trying to use the rms package to get predicted ordinal responses from a conditional ratio model. As you will see below, my model seems to fit well to the data, however, I'm having trouble getting predicted mean (or fitted) ordinal response values using the predict function. I have a feeling I'm missing something simple, however I haven't been able to

Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <-

v2 -> v3: - Changes to the Curl API suggested by Pino.