search for: hypertensive

Hi all , I have a dataframe (Hypertension) with following headers :- > Hypertension ID Hypertension(before drug A) Hypertension(On drug A) On drug B? Healthy diet? 1 160 90 True True 2 190

Hi, I am trying a linear regression model where the dependent variable is the size of the heart corrected for the patient's height and weight. This is labelled as LAVI. The independent variables are race (european or non-eurpoean), age, sex (male or female) of the patient and whether they have diabetes and high blood pressure. sample size 2000 patients selected from a community. when I

Hi everyone, I've only been using R for a week or so now, and am now required to work with some pretty mixed data at my hospital. Basically, we're looking to see if hypertension status (nominal) affects cardiac function (multiple & continuous). In order to do this, we've collected multiple echocardiograph data reports on each patient. 1. Each patient is coded either as 0 or 1

Hello, This is the fitted model: > fit Call: multinom(formula = resp ~ pred$cls + pred$smoke) Coefficients: (Intercept) pred$cls2 pred$cls3 pred$cls4 pred$cls5 pred$smoke2 pred$smoke3 Proteinuria -1.140520 0.1616644 0.05554898 -0.01584927 0.02574805 -0.4057245 -0.2898425 Hypertension -2.691215 -0.3699690 -0.22582107 0.01615898 0.26318005 0.1239051 0.2413282

Could someone kindly help me with the following question: when I analyze microarray data I need to fit multiple linear regression models between genes and clinical patameters followed by estimation of the p-values. What's the solution to implement Westfall and Young's algorithm + resampling into the scheme: lm -> stepAIC -> anova. Actually permcor works fine for me in the case of

Dear R users, I would like to know if there is a way in R to execute a post-hoc test (factor levels comparison, like Tukey for ANOVA) of a non-parametric analysis of variance with kruskal.test() function. I am comparing three different groups. The preliminary analysis using the kruskal-wallis-test show significance, but I still don''t know the relationship and the significance level

Dear R-list members, I would like to pose a question about the use and results of the glm() function for logistic regression calculations. The question is based on an example provided on p. 229 in P. Dalgaard, Introductory Statistics with R, 2nd. edition, Springer, 2008. By means of this example, I was trying to practice the different ways of entering data in glm(). In his book, Dalgaard

We are trying to read a sas export file into R. (Works fine on Mac by ftping the export file from solaris box and then importing to R with read.xport, this gives rational numbers etc.) Trying to do this on an IBM Power p655 running linux, reading the same sas export file as used on MAC, in this case the results are not rational. Many of the numbers are interpreted as INF etc. Is it possible

Dear R users, Please can you help me with a relatively straightforward problem that I am struggling with? I am simply trying to plot a baseline survivor and hazard function for a simple data set of lung cancer survival where `futime' is follow up time in months and status is 1=dead and 0=alive. Using the survival package: lung.wbs <- survreg( Surv(futime, status)~ 1, data=lung,

Hello everybody! I have problems with integrating my function. My primary function is a “survival function” of the following type: surviv <- exp(-k*x)/(1+exp(alpha*(x-tau))) I would like to integrate this function over a defined range and obtain a vector with all the values from integrate(surviv, 0, x) for x <- 1:N. I unfortunately obtain just a scalar value corresponding to the last

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 <-

If there are any other users who use AHRQ's SAS code comoanaly2010 and comformat2010 to create comorbidity variables, I thought you might be interested in the following PRELIM code we wrote to mimic its functionality in R. It seems to yield similar results, but may contain errors. Please feel free to comment (kindly) or enhance. I'm sure there are better ways to skin this cat, but we at

Following is a function that I wrote (It is working well). It's a simple one, nothing complicated. The only question that I have is a qutation mark issue, I guess. ############################################# funcname <- function(trait.file){ #line1 setwd('/root/subroot') # line 2 load('imge.RData')

> 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

emigrate tough sprightly torque tallyho alizarin dispersible chipmunk hypertensive rhine tincture creating cryptanalyze conjoint odious also downstream brew footage

Hi, all. It seems like everybody ignore my letters about audit in samba. I wanna ask one simple question- have anyone working audit in samba ? WBR -- Origin:...and your cities will become your tombs(2:5054/63@Fidonet)

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, I would like to see if a matching variable is an effect-modifier in a conditional logistic regression. Naturally, the matching variable can't enter directly in the model but as an interaction with terms that are in. However, I have problems in formulating the correct model the term that's already in the model is a factor. I am using treatment contrasts and the problem is that if I

bRotheRs & sisteRs, I am once again attempting to learn enough Latex voodoo to get something done, and failing comically. The document "RJAuthorguide.pdf" mentions that one can get page width figures through the use of the "figure*" or "table* environments, but despite considerable searching through the mail archives and reading Frank Harrell's discussion of

Some time ago I was allowed to discuss "Diamond Graphs", and whether they would be useful in R, in this mailing list. The August 2003 issue of The American Statistician has finally arrived here and I have been able to read the article. A number of points of interest arise. 1. The article is "A Diamond-Shaped Equiponderant Graphical Display of the Effects of Two