similar to: qgraph: correlation matrix variable names

hello, I have a graph and i use qgraph package to calculate centrality parameters. Now I want to know the maximum value of shortest path for each vertex with discarding the Inf value in short pathes. For this I use the ShortestPathLengths of centrality function in qgraph. but when I want to get the maximum the result is wrong. here is my code: cen<-centrality(Q)

Hard to follow data analysis without data. Try making your example reproducible [1][2][3] and post in plain text (a setting in your emailer). Read the Posting Guide mentioned in the footer to avoid other posting pitfalls. [1] http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example [2] http://adv-r.had.co.nz/Reproducibility.html [3]

I would like to obtain Confidence Intervals for the estimates (unstandardized beta weights) of each predictor in a WLS regression: m1 = lm(x~ x1+x2+x3, weights=W, data=D) SPSS offers that output by default, and I am not able to find a way to do this in R. I read through predict.lm, but I do not find a way to get the CIs for multiple independent variables. Thank you Torvon [[alternative HTML

I have two regressions to perform - one with a metric DV (-3 to 3), the other with an ordered DV (0,1,2,3). Neither normal distribution not homoscedasticity is given. I have a two questions: (1) Some sources say robust regression take care of both lack of normal distribution and heteroscedasticity, while others say only of normal distribution. What is true? (2) Are there ways of using robust

I need to report exact p-values in my dissertation. Looking at my lm() results of many regressions with huge datasets I have the feeling that p-values are rounded to the smallest value of "2e-16", because this p-value is very common. Is that true or just chance? If it is true, how do I obtain the "true" unrounded p-values for these regressors? m1 <- lm(y ~ x1+x2+x3+4+x5,

I am running 9 negative binomial regressions with count data. The nine models use 9 different dependent variables - items of a clinical screening instrument - and use the same set of 5 predictors. Goal is to find out whether these predictors have differential effects on the items. Due to various reasons, one being that I want to avoid overfitting models, I need to employ identical types of

Dear sir, I have a matrix like a<-matrix(c(0,2,0,4,0,6,5,8,0),nrow=3) colnames(a)<-c("F1","F2","F3") rownames(a)<-c("A1","A2","A3") a F1 F2 F3 A1 0 4 5 A2 2 0 8 A3 0 6 0 I want to extract all pairs (rownames, columnames) from which the value in the matrix is 0 The result should be something like this A1, F1 A2,

Hello, Can you say me which is the key to add in the registry for Windows XP to Enable Plain Text Password? Best Regards

Hello! I am doing an analysis on a questionnaire of hunters taken in 4 different districts of some mysterious foreign country. The aim of the study was to gather info on the factors that determine the hunting success of a peculiarly beautiful bird in that area. All variables are factors, i.e. they are variables such as "Use of Guns - yes / no", "Use of Dogs - yes / no" and

Hello, I noticed a strange thing when attempting to upgrade a specific package (lavaan) from c2d4u and wondered if someone could confirm the problem. In short, apt seems to think that an older version from the "universe" repo is higher than the newer version from c2d4u. When I do apt-cache showpkg r-cran-lavaan I get the output at the bottom of this email. Then, I tried to force

Dear R Mailinglist, I want to understand how predictors are associated with a dependent variable in a regression. I have 3 measurement points. I'm not interested in understanding the associations of regressors and the predictor at each measurement separately, instead I would like to use the whole sample in one regression, "pooling" the measurement points. I cannot simply throw them

I run 9 WLS regressions in R, with 7 predictors each. What I want to do now is compare: (1) The strength of predictors within each model (assuming all predictors are significant). That is, I want to say whether x1 is stronger than x2, and also say whether it is significantly stronger. I compare strength by simply comparing standardized beta weights, correct? How do I compare if one predictor is

Hi R-users I would like to extract the random effects ("1|SITE", "1+SPECIES|SITE" and "BA|SITE") from this model formula: Full_model <- formula (VAR ~ (1|SITE) + (1+SPECIES|SITE) + (BA|SITE) + HEIGHT + COND + NN_DIST) I tried: terms(Full_model) labels(terms(Full_model)) but I could not distinguish between random and fixed effects. thanks

Hello. Using the polychor function > polychor(data[c(s1,s2)] ) for polychoric correlations of two ordinal variables in R takes a long time for N=7000 (20 minutes+) and significantly slows down my computer. Now, I have a pretty old computer, but it takes about 20 seconds for MPLUS to print out the complete polychoric correlation matrix for all 16 variables, while I am running the R function

Dear Sacha, use glm() in this case. I'd rather code the covariable as TRUE / FALSE or as a factor. Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance thierry.onkelinx at inbo.be

Dear R-experts, I am trying to fit a GAM with 2 binary predictors (variables coded 0,1). I guess I cannot just smooth binary variables. By the way I code them as 0=no,1=yes, then mgcv will think those variables are numeric.? I have tried to change 0 and 1 in no and yes. It does not work. How to solve my problem. Here below my toy example. Many thanks. Best, Sacha ? ########################

Hi, I'm new to R, and I have searched helpfiles and this forum on my 2 questions. Hope you guys can help me out! :-) Many thanks in advance! Cheers, Lars Q1: I imported a csv file with columnames subject and class. There are about 1000 different classes... It looks like this: subject1, class1 subject1, class2 subject2, class1 subject2, class3 ... subject999, class1 subject999, class2

I would like to develop a new mailbox format for a very specialized application. Can anyone point me in the appropriate direction to get started writing a plugin? More detail for anyone interested: I need to store mail in a format that's quickly accessible and modifiable to an external process. This is for a very low-volume mail system (averaging less than 50 messages stored at any one

hello list! I'm sorry, I just stumbled over this strange behaviour (at least I am not able to explain the behaviour, therefore I assume it to be a strange behaviour): attach(water) # I know, this is not recommended names(water[3:10]) [1] "temp" "pH" "DO" "BOD" "COD" "no3" "no2" "po4" for (i in

R-experts, I have fitted many different lines. The fast-tau estimator (yellow line) seems strange to me?because this yellow line is not at all in agreement with the other lines (reverse slope, I mean the yellow line has a positive slope and the other ones have negative slope). Is there something wrong in my R code ? Is it because the Y variable is 1 vector and should be a matrix ? Here is the