similar to: Re: [S] Labels wrong with lrm

Hi all, I have another question about the lrm function (from the rms library) that I cannot find the answer to. I get an error message when I try to fit a model, and I don't know what to make of it. Please forgive me for not having a toy example, but it appears the size and complexity of my data is somehow causing the error. The best I can do is show you what I type and what errors I get.

Dear All, I'm using apply to do some genetic association analysis along a chromosome, with many thousands markers. For each marker the analysis is the same, so I was planning to use apply(chrom, 2, somefunction) In the specific case I do: my.results = apply(chr, 2, function(x){anova(lrm( cpstc.f ~ x + time.cpstc + age + sex + mri))[1,3]}) This is all good and well in theory, but in

Hello again R users, I have a devilishly hard problem, which should be very simple. I hope someone out there will have the answer to this on the tip of their tongue. Please consider the following toy example: x <- read.table(textConnection("y x1 x2 indv.1 bagels 4 6 indv.2 donuts 5 1 indv.3 donuts 1 10 indv.4 donuts 10 9 indv.5 bagels 0 2 indv.6 bagels 2 9 indv.7 bagels 8 5 indv.8

Please forgive me if you already received this. I had an e-mail sending glitch this morning. http://cran.r-project.org/src/contrib/checkSummary.html reported an error in Design.trans.Rd * checking Rd files ... ERROR Rd files with syntax errors: /var/mnt/hda3/R.check/r-devel/PKGS/Design/man/Design.trans.Rd: unterminated section 'alias' The .Rd file is attached. It begins

My laboratory is measuring the abundance of various proteins in the blood from either healthy individuals or from individuals with various diseases. I would like to determine which proteins, if any, have significantly different abundances between the healthy and diseased individuals. Currently, one of my colleagues is performing an ANOVA on each protein with MS Excel. I would like to analyze

Hello, I have a data base with variables pasthma,kasthma,sex,etc. I wrote a program to compute the Kappa statistic between pasthma and kasthma. Actually "did the child had asthma" answered by the parents and by the child. kappa <- function(x) { r <- paste("p",evaluate(x),sep="") c <- paste("k",evaluate(x),sep="") a <- table(r,c) ...

Hi Group, I have a question about obtaining the bias-corrected c-index using validate from the Design library. As an example, consider the example from help page: library(Design) ?validate.lrm n <- 1000 age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'),

Hi R sages, Here is my latest problem. Consider the following toy example: x <- read.table(textConnection("y1 y2 y3 x1 x2 indv.1 bagels donuts bagels 4 6 indv.2 donuts donuts donuts 5 1 indv.3 donuts donuts donuts 1 10 indv.4 donuts donuts donuts 10 9 indv.5 bagels donuts bagels 0 2 indv.6 bagels donuts bagels 2 9 indv.7 bagels donuts bagels 8 5 indv.8 bagels donuts bagels 4 1 indv.9

I am cleaning up the rms package to not export functions not to be called directly by users. rms uses generic functions defined in other packages. For example there is a latex method in the Hmisc package, and rms has a latex method for objects of class "anova.rms" so there are anova.rms and latex.anova.rms functions in rms. I use:

I am interested in configuring Dovecot's TLS so as to retain forward secrecy, but eliminate all of NIST's elliptic curves. Besides being subject to side channel attacks [1], in some quarters there is a general distrust of NIST's curves and any of their other cryptographic primitives after the Dual EC DRBG debacle. >From what I can tell, the following will prevent the use of

Hello I'm using logistic regression from the Design library (lrm), then fastbw to undertake a backward selection and create a reduced model, before trying to make predictions against an independent set of data using predict.lrm with the reduced model. I wouldn't normally use this method, but I'm contrasting the results with an AIC/MMI approach. The script contains: # Determine full

I really want to do this: abline( a=tan(-kT*pi/180), b=kY-tan(-kT*pi/180)*kX ) where kX,kY and kT are vectors of equal length. But I can't do that with abline unless I use a loop, and I haven't figured out the least unelegant way of writing the loop yet. So is there a way to do this without a loop? Or if I am to resort to the loop, what's the best way of doing it considering that I

Dear Harry, Thx for the explanation. My team manage building's PBX that use Asterisk 13.x. We use Asterisk PBX for this buildings that have apartment and office customer. >From my Asterisk PBX, we connect to IPPhone (yealink) or ATA Converter (cisco SPA112). Others are using PBX like panasonic analog, audiocodes SBC, etc, and we use ATA Converter to convert from SIP to Analog (CO Line)

dear professor: I have a problem about the nomogram.I have got the result through analysing the dataset "exp2.sav" through multinominal logistic regression by SPSS 17.0. and I want to deveop the nomogram through R-Projject,just like this : > n<-100 > set.seed(10) > T.Grade<-factor(0:3,labels=c("G0", "G1", "G2","G3")) >

Dear R helpers, >From the lrm( ) model used for binary logistic regression, we used the L.R. model value (or the G2 value, likelihood-ratio chi-squared statistic) to evaluate the goodness-of-fit of the models. The model with the lowest G2 value consequently, has the best performance and the highest accuracy. However our model includes rsc() functions to account for non-linearity. We

> On Sep 14, 2017, at 12:30 AM, Bonnett, Laura <L.J.Bonnett at liverpool.ac.uk> wrote: > > 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

Hello, I am an R newbie, trying to use lsoda to solve standard Lotka-Volterra competition equations. My question is: how do I pass a parameter that varies with time, like say, phix <- 0.7 + runif(tmax) in the example below. # defining function lotvol <- function(t,n,p){ x <- n[1]; y <- n[2] rx <- p["rx"]; ry <- p["ry"] Kx <-

Fixed 'maxiter' in the help file. Thanks. Please give the original source of that dataset. That dataset is a tiny sample of GUSTO-I and not large enough to fit this model very reliably. A nomogram using the full dataset (not publicly available to my knowledge) is already available in http://biostat.mc.vanderbilt.edu/tmp/bbr.pdf Use lrm, not lrm.fit for this. Adding maxit=20 will

Hi all I have run into a case where I don't understand why predict.lrm and predict.glm don't yield the same results. My data look like this: set.seed(1) library(Design); ilogit <- function(x) { 1/(1+exp(-x)) } ORDER <- factor(sample(c("mc-sc", "sc-mc"), 403, TRUE)) CONJ <- factor(sample(c("als", "bevor", "nachdem",

Hi R-helpers I have a dataframe DF (lets say with the variables, y, x1, x2, x3, ..., clust) containing relatively many NAs. When I fit an ordinal regression model with the function lrm from the Design library: model.lrm <- lrm(y ~ x1 + x2, data=DF, x=TRUE, y=TRUE) it will by default delete missing values in the variables y, x1, x2. Based on model.lrm, I want to apply the robust covariance