similar to: logistic model diagnostics residuals.lrm {design}, residuals()

Is there a predict method/syntax which I could use to generate predictions (and other output from predict() methods) if I have the model parameter estimates from a training dataset but not the data used to generate the original model (the models were generated by a collaborator using STATA and for IRB reasons I am not allowed independent access to the original data)? I have the new/testing data

Suppose I have a binary outcome (disease/no disease and all subjects had the same period of exposure) and 2 or 3 (categorical) predictors. I can obviously build a logistic regression model which describes the data, possibly including interaction terms, on a relative scale: model<-glm(disease~sex*race*prematurity,family=binomial) 1) Is there any way to extract information on the absolute

Hi R, I am getting this error while trying to use 'lrm' function with nine independent variables: > res = lrm(y1994~WC08301+WC08376+WC08316+WC08311+WC01001+WC08221+WC08106+WC0810 1+WC08231,data=y) singular information matrix in lrm.fit (rank= 8 ). Offending variable(s): WC08101 WC08221 Error in j:(j + params[i] - 1) : NA/NaN argument Now, if I take choose only four

Hi, I am wondering if there is a way to specify the prevalence of events in logistic regression using lrm() from Design package? Linear Discriminant Analysis using lda() from MASS library has an argument "prior=" that we can use to specify the prevalent of events when the actual dataset being analyzed does not have a representative prevalence. How can we incorporate this information in

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')

Hello all, Could someone tell me what I am doing wrong here? I am trying to fit a binary logistic model using the lrm function in Design. The dataset I am using has a dichotomous response variable, 'covered' (1-yes, 0-no) with explanatory variables, 'nepall', 'title', 'abstract', 'series', and 'author1.' I am running the following script and

Hallo! I want to understand / recalculate what is done to get the CI of the logistic regression evaluated with lrm. As far as I came back, my problem is the variance-covariance matrix fit$var of the fit (fit<-lrm(...), fit$var). Here what I found and where I stucked: ----------------- library(Design) # data D<-c(rep("a", 20), rep("b", 20)) V<-0.25*(1:40) V[1]<-25

Greetings, I am using both the lrm() {Design} and glm( , family=binomial()) to perform a a logisitic regression in R. Apart from the typical summary() methods, what other methods of diagnosing logistic regression models does R provide? i.e. plotting an 'lm' object, etc. Secondly, is there any facility to calculate the R^{2)_{L} as suggested by Menard in "Applied Logistic

To all moderators i guess, my question was probably not clear this is not a homework, i am trying to understand R by doing some exercise in my book. I will however participate a course in R in august and thought it could be good to have some knowledge before. I hoped for help from you since i have no instructor to ask, that would have been my first choice. thanks anyway Lotta

Hi, I have a small Linux server PC at home (Intel Core2 Q9300, 4 GB RAM), and I''m seriously considering switching to OpenSolaris (Indiana, 2008.11) in the near future, mainly because of ZFS. The idea is to run the existing CentOS 4.7 system inside a VM and let it NFS mount home directories and other filesystems from OpenSolaris. I might migrate more services from Linux over time, but for

1. Emma is performing an experiment that requires individual handling of some animals. The sizes of the animals are lognormally distributed: The natural logarithms of their sizes has a normal distribution with mean 3 and standard deviation 0.4. The time (in minutes) it takes to handle each animal is given by 10 + s · 1.5 + eε for animals with s ≤ 20 20 + s · 0.8 + eε for animals with s > 20

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

Hi, I have an Asterisk behind an OpenSIPS proxy. The proxy handles registrations and also SIP SUBSCRIBE for MWI. The Asterisk are configured to send NOTIFY to the proxy even when the SUBSCRIBE haven't been received. I can configure a user in sip.conf that works: [az5134939706] type=friend host=xxx.xxx.xxx.xxx (IP of proxy) port=5060 nat=no mailbox=1234 at customer subscribemwi=no

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

Hi, I am running a logistic regression model using lrm library and I get the following error when I run the command: mod1 <- lrm(death ~ factor(score), x=T, y=T, data = env1) Unable to fit model using ?lrm.fit? where score is a numeric variable from 0 to 6. LRM executes fine for the following commands: mod1 <- lrm(death ~ score, x=T, y=T, data = env1) mod1<- lrm(death ~

> 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

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

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 <-

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",

Hello everybody, I am trying to do a logistic regression model with lrm() from the design package. I am comparing to groups with different medical outcome which can either be "good" or "bad". In the help file it says that lrm codes al responses to 0,1,2,3, etc. internally and does so in alphabetical order. I would guess this means bad=0 and good=1. My question: I am trying to