search for: confounding

...my thesis in which i have couple of independent variables that are categorical in nature and the depndent variable is dichotomus. Initially I run univariate analysis and added the variables with significant p-values (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 insig...

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

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','z3','y') Now,

Dear all I am struggling with how to deal with missing values using geeglm. I know that geeglm only 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

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 do...

I am trying to use the CART in a survival analysis. I have three variables of interest (all 3 ordinal - x, y and z, each of them with 5 categories) from which I want to make smaller groups (just an example 1st category from X variable with the 2nd and 3rd categories from the Y category and 2, 3 and 4 categories from the Z category etc) based on their, let's say, association with mortality. Now

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.

...hich 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 consideration the confounding effect? Any help would be greatly appreciated. Isaac A representation of the experiments follows: Experiment 1 Control1 Treat1 Treat2 Blk1 s1 s2 s3 Blk2 s4 s5 s6 Blk3 s7 s8 s9 Experiment 2...

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]]

...the first and second segment), I would like the analysis to select the optimum knot. My first analysis will contain a single dependent and a single independent variables. Subsequent analyses will add additional independent variables to determine the effect on the results of adjusting for potential confounding. Thank you, John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone) 410-605-7119 (Fax) 410-605-7913 (Please call p...

...egression based onpartial F test) Does anyone know of research fully Bayesian stepwise procedures assuming that models not considered by the stepwise would essentially have zero posterior probability? I need to analyze the results of ad hoc experiments run in manufacturing with crazy confounding and possible supersaturation (i.e., more potentially explanatory variables than runs), when each run is very expensive in both time and money. There have to be ways to summarize concisely and intelligently what the data can tell us and what remains uncertain, including the level of partial conf...

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 =

...l 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 wondering if there's a way to do the following 2 things: 1) Plot both histogram and the overlying frequency curve in one plot 2) Plot multiple frequency curves in a single plot I have been using the "hist" function for my job. I'd appreciate if anyone could...

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...

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

...rstand why this would be. Google turns up this old R help thread: http://r.789695.n4.nabble.com/GAM-without-intercept-td4645786.html Simon writes: *Smooth terms are constrained to sum to zero over the covariate values. ** **This is an identifiability constraint designed to avoid confounding with ** **the intercept (particularly important if you have more than one smooth). * If you remove the intercept from you model altogether (m2) then the smooth will still sum to zero over the covariate values, which in your case will mean that the smooth is quite a long way from...

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