search for: confounder

...p<0.25) in my full model. I have three confusions. Firstly, I am looking for confounding variables by using formula "(crude beta-cofficient - adjusted beta-cofficient)/ crude beta-cofficient x 100" as per rule if the percentage of any variable is >10% than I have considered that as confounder. I wanted to know that from initial model i have deducted one variable with insignificant p-value to form adjusted model. Now how will i know if the variable that i deducted from initial model was confounder or not? Secondly, I wanted to know if the percentage comes in negative like (-17.84%) than...

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to treatment in the trial, but Covariate is something that is measur...

Hi, I was wondering if there is a way, or function in R to find confounders. For istance, > a = sample( c(1:3), size=10,replace=T) > X1 = factor( c('A','B','C')[a] ) > X2 = factor( c('Aa','Bb','Cc')[a] ) > Xmat = data.frame(X1,X2,rnorm(10),rnorm(10)) > dimnames(Xmat)[[2]] = c('z1','z2','z...

...works with complete datasets, but I cannot seem to get the na.omit function to work. For example assuming DataMiss contains 3 columns, each of which has missing observations, and an id column with no missing info then identifies the clusters. Outcome: 2 level integer Predictor: numeric variable Confounder: 3 level integer If I "manually" remove the missing values then run the model, there is no problem. #remove missing values data<-subset(DataMiss, !is.na(outcome) & !is.na(predictor) & !is.na(confounder)) #run the model model<-geeglm(outcome~predictor+confounder, family=...

The following R commands were written: >help.search("factorial") >data(npk) >npk >coef(npk.aov) In the output of coef command, please explain me the interpretation of coefficients of block1 to block 6 in this 2^3 confounded factorial experiment. Thanks. [[alternative HTML version deleted]]

Hello all, long time lurker, first time poster. I have a situation that has been confounding me for the better part of a week. I run an internal mirror of a Centos mirror, mainly because I have lots of hosts and low bandwidth. For my 7.x hosts, since 7.5.1804 was released, when I do a ?yum update?, I get the following errors on a number of files. "Package does not match intended download.

...with mortality. Now I would also want that this analysis to be adjusted for a number of variables (that I don't want to incorporate in the decision tree, just to take them into consideration in the relationship between the 3 variables and the outcome; I would also want to mention that for this confounders I have missing values - how should this be deal with?), this survival analysis to be stratified and also to use clusters. I have tried party and rpart packages, but I don't seem to get how to properly do what I want. Thank you [[alternative HTML version deleted]]

This is probably a know problem (problem for me anyway) in R but I don't quite know what to search for in help archives. When I name a column "x11" in a data frame R thinks a column named "x1" exists. In my application I am trying to test for the existence of a column, then add it if it's not there. Here is a simple example: > temd <- data.frame(x11=c(0:10)) >

Hello, I am trying to find an R function to compute 'direct adjusted survival' with standard errors. A SAS-macro to do this is presented in Zhang X, Loberiza FR, Klein JP, Zhang MJ. A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model. Comput Methods Programs Biomed 2007;88:95-101. It appears that this method is not implemented in R.

I don't know if this is the best place to post this question but I will try anyway. I have two experiements for which I use one-way matched-randomized ANOVA for the analysis and I would like to compare different treatments in the two experiments. The only common group in the two experiments are the controls. Is there any ANOVA design that allows me to make this comparison taking into

Hi all, Does anybody know if it is possible to build a fractional factorial design in R? That is, suppose that we want do design an experiment with 3 factors with 2, 3 and 3 levels, respectivly. However we want to consider, let's say, only 6 from all possible level combinations. Does R design such experiment? Thanks in advance, Caio [[alternative HTML version deleted]]

Windows Vista R 2.10 - I know it is old, I will update later today. How might I perform a piece-wise linear regression where two linear segments are separated by a single knot? In addition to estimating the slopes of the two segments (or the slope in one segment and the difference between the slope of the first and second segment), I would like the analysis to select the optimum knot. My first

oops, Forgot to cc to the list. Regards, Mike -----Original Message----- From: dr mike [mailto:dr.mike at ntlworld.com] Sent: 24 February 2005 19:21 To: 'Spencer Graves' Subject: RE: [R] Bayesian stepwise (was: Forward Stepwise regression based onpartial F test) Spencer, Obviously the problem is one of supersaturation. In view of that, are you aware of the following? A Two-Stage

Dear All, My question is more a statistical question than a R question. The reason I am posting here is that there are lots of excellent statistician on this list, who can always give me good advices. Per my understanding, the purpose of propensity score is to reduce the bias while estimating the treatment effect and its implementation is a 2-stage model. 1) First of all, if we assume that T =

Hello, This is a problem for which there seem to be several solutions online, but not really. My question was about plotting a curve over the histogram. All the previous posts and messages talk about generating a *density histogram*using (freq=F) and then plotting the density curve. However, I find that that seriously distorts my data and the plot becomes confounding to the viewer. I was

Hi everyone, I have a question that seems simple, but has been confounding me for several hours. I'd like to debug a binary produced with LLVM. For the life of me, I can't get any symbols into gdb and llvm-db won't even start the program nor load any useful information about it. Here's my current strategy (which isn't working): llvm-gcc -g -O0 -c -emit-llvm

Dear r-bugs, When I create EPS files, they sometimes appear rotated in my LaTeX PDF document and sometimes they don't. Two examples: ## x1.eps is not rotated in LaTeX x <- seq(-1, 1, length=100) postscript("c:/x1.eps", height=3, width=4, horizontal=FALSE, onefile=FALSE, paper="special") plot(x, dnorm(x), type="l") dev.off() ## x2.eps is not

Dear List, I've just tried to specify a GAM without an intercept -- I've got one of the (rare) cases where it is appropriate for E(y) -> 0 as X ->0. Naively running a GAM with the "-1" appended to the formula and the calling "predict.gam", I see that the model isn't behaving as expected. I don't understand why this would be. Google turns up this old

Hi, i need to create a fractional factorial design sufficient to estimate the main effects. The factors may have any number of levels, let's say any number from 2 to 6. I've tried to use the library conf.design , but i cannot figure out how to write the code. For example, what is the code for a design with 5 factors (2x3x3x5x2) and only main effects not confounded? thanks in advance!

Hi strange behaviour of iptables on a centos 7.0 machine: The following rule is in the iptables of said machine: [root at myserver ~]# iptables -L -v -n --line-numbers |grep 175\. 9 9 456 DROP all -- * * 175.44.0.0/16 0.0.0.0/0 [root at myserver ~]# The corresponding enty in /etc/sysconfig/iptables looks like: [root at myserver ~]# grep 175 /etc/sysconfig/iptables