search for: confound

...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 in...

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 meas...

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','...

...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, famil...

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...

...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.

...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 cal...

...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 c...

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 co...

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...

...uot;special") plot(x, dnorm(x), type="l") dev.off() By LaTeX PDF, I am referring to the dvips->ghostscript pathway, but x2.eps also rotates when distilled into PDF outside LaTeX. I have tried ghostscript and acrobat, placed on a page or cropped, so the problem does not seem to be confounded with particular PDF distillation software. I'm aware of the pdf() device in R, but EPS files are a widely used format to share scientific graphics, often winding up embedded in a PDF document at a later point in the hands of an editor. If at all possible, a more predictable postscript gener...

...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 f...

...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! Roberto Furlan University of Turin, Italy PS: i've already tried to send this email, sorry if you 've received it again! ---------------------------------------- La mia Cartella di Posta in Arrivo ?? protetta con SPAMfighter 188 messaggi contenenti spam sono stati b...

...n /etc/sysconfig/iptables looks like: [root at myserver ~]# grep 175 /etc/sysconfig/iptables -A INPUT -s 175.44.0.0/16 -j DROP [root at myserver ~]# The rule must be there since ages, because it has number 9 out of 76 similar rules. Today, on the same machine (I rechecked it to make sure not to confound machines), I see the following extract of the ftplog: <snip> 175.44.4.127 2915 175.44.26.128 2021 175.44.26.138 1322 175.44.6.186 1290 175.44.24.88 1219 175.44.4.199 1212 </snip> saying that from this IP addresse there have been this many connections to the ftp server on that machin...