search for: smokes

Dear all, Apologies if this question is bit theoretical and for the longish email. I am meta-analyzing the coefficients and standard errors from multiple studies where the raw data is not available. Each study analyst runs a model that includes an interaction term for, say, between sex and smoking and age. Here is an illustrative example example for one study: set.seed(1066) status

Hi, I'm sure this is simple, but I haven't been able to find this in TFM, say I have some data in R like this (pasted here: http://pastebin.com/raw.php?i=sjS9Zkup): > head(df) gender age smokes disease Y 1 female 65 ever control 0.18 2 female 77 never control 0.12 3 male 40 state1 0.11 4 female 67 ever control 0.20 5 male 63 ever state1 0.16 6 female 26 never state1 0.13 where unique(disease) == c("control", "state1", "s...

Hola: Si aún hay alguien que no está de vacaciones, igual me pueden ayudar. Quiero ajustar unos modelos: REG_LOG <- glm (low ~ X, family = "binomial", data = DATOS) Ejemplo: library(MASS) data(birthwt, package="MASS") birthwt$low <- factor(birthwt$low) birthwt$race <- factor(birthwt$smoke) REG_LOG <- glm (low ~ smoke, family =

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 professor: thank you for your help,witn your help i develop the nomogram successfully. after that i want to do the internal validation to the model.i ues the bootpred to do it,and then i encounter problem again,just like that.(´íÎóÓÚerror to :complete.cases(x, y, wt) : ²»ÊÇËùÓеIJÎÊý¶¼Ò»Ñù³¤(the length of the augment was different)) i hope you tell me where is the mistake,and maybe i have

Hello, I want to fit a GEE with a user-defined link function. For the user-defined link-function I still read http://finzi.psych.upenn.edu/R/Rhelp01/archive/6555.html and http://finzi.psych.upenn.edu/R/Rhelp02a/archive/25727.html. Only for testing purposes I added a new link function (corlogit) in make.link (as well as in binomial) with exactly the same code as logit before using my code.

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

Hello there, I have two groups (men and women) and I know per group how many of them smoke or don't smoke (women 40 of 200; men 100 of 300). I would like to know how I can compare in R if men and women differ significantly in their smoking. However, because there are more men in the sample than women I cannot just compare the number of smokers and non-smokers in both groups, right?! (I would

Hello, I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway). Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not

On Mon, Sep 21, 2015 at 11:10:06AM +0300, Pasi K?rkk?inen wrote: > Hi, > > On Wed, Sep 09, 2015 at 02:24:15PM +0100, George Dunlap wrote: > > Updated Xen 4.4.3 packages have passed my local smoke tests, and are > > now available. > > > > Full C7 release is still waiting on suitable testing for the libvirt packages. > > > > If someone wants to step up

Hi I am a new R user and I did try elrm package to do Exact Logistic regression for my dataset ( about 170 patients with a binary dependent variable against age and 6 other binary covariates), I did not have n column because it was about individuals, I made n column with 1 for each individual, here is my code and result: > > #### Libraries needed #### > >

Hi, Does anyone know if: with R can you take a set of numbers and aggregate them like you can in SPSS? For example, could you calculate the percentage of people who smoke based on a dataset like the following: smoke = 1 non-smoke = 2 variable 1 1 1 2 2 1 1 1 2 2 2 2 2 2 When aggregated, SPSS can tell you what percentage of persons are smokers based on the frequency of 1's and 2's. Can

greetings. my problem is i can't print from windows 2000 or windows xp. my solaris box can print fine. on smoking (solaris 10, samba host) smbclient '//smoking/hp2605' -U jgs%password put /etc/motd i get the printout. on windows, this works: echo hiya^L > \\smoking\hp2605 on windows, from M$ word, the print SEEMS to go into the queue. i click on the printer, get the box that

Dear UseRs, Suppose I have data regarding smoking habits of a prospective cohort and wish to determine the risk ratio of colorectal cancer in the smokers compared to the non-smokers. What do I do at the end of the study with people who die of heart disease? Can I just censor them exactly the same as people who become uncontactable or who die in a plane crash? If not, why not? I'm thinking

Bother! This cold has made me accident-prone. I meant to hit Reply-all. Clarification below. -------- Original Message -------- Subject: Re: [R] Parameterization puzzle Date: Fri, 21 Jul 2006 19:10:03 +1200 From: Murray Jorgensen <maj at waikato.ac.nz> To: Prof Brian Ripley <ripley at stats.ox.ac.uk> References: <44C063E5.3020703 at waikato.ac.nz>

I apologize in advance if this is posted all ready I have not been able to find any information about it. I have this data frame and I want to sort smoking by retlevel. Age Gender BMI Calories Fat Fiber Alc retlevel Smoking 1 64 Female 18.87834 1828.0 63.4 14.7 0.0 Normal Non-Smoker 2 25 Female 20.64102 1517.4 59.1 5.9 0.0 Normal Smoker 3

Consider the following example (based on an example in Pat Altham's GLM notes) pyears <- scan() 18793 52407 10673 43248 5710 28612 2585 12663 1462 5317 deaths <- scan() 2 32 12 104 28 206 28 186 31 102 Smoke <- gl(2,1,10,labels=c("No","Yes")) Age <- gl(5,2,10,labels=c("35-44","45-54","55-64","65-74","75-84"),

Hi all, A better documentation about the feature, and also information about how to use the features are one of the major ask of the community when they want to use glusterfs, or want to contribute by helping get the features, bug fixes for features, etc. Finally, we have taken some baby steps to get that ask of having better design and documentation resolved. We had discussed this in our

I've got a dataset which looks like this in the beginning: cbr dust smoking expo 1 0 0.20 1 5 2 0 0.25 1 4 3 0 0.25 1 8 4 0 0.25 1 4 5 0 0.25 1 4 (till no. 1240, anyway, a huge set) I have to analyse cbr and smoking, I know it works with chisq.test() for the whole set, but I only need cbr and smoking, and I

Hi, Suppose I have: # Fit a base model d1.ph <- coxph(Surv(start, stop, event)~ ejec + diavol + score + smoking + beta + surg.done, data = data.frame(foo)) summary(update(d1.ph, . ~ . + td1)) summary(update(d1.ph, . ~ . + td2)) As I have many columns in my data frame, foo, called td's. e.g. td1, td2, td3, .... And I'd like to