search for: intercorrelations

Hello! I am doing Principle Componenets Analysis using "psych" package: mypc<-principal(mydata,5,scores=TRUE) However, I was asked to run a case-weighted PCA - using an individual weight for each case. I could use "corr" from "boot" package to calculate the case-weighed intercorrelation matrix. But if I use the intercorrelation matrix as input (instead of the

...ctor2 Factor3 Factor1 1.000000000 0.001624383 0.002862785 Factor2 0.001624383 1.000000000 0.001956953 Factor3 0.002862785 0.001956953 1.000000000 which means that factor correlations are indeed quite low with regard to interpretation issues, but an analysis of a larger dataset yielded factor intercorrelations up to .10. I guess this is an optimization issue because a lower setting of "lower" tends to lower factor intercorrelations, but I'm still confused because I (misleadingly?) thought that factor scores are (completely) independent by definition? Any hints would be greatly apprecia...

...different from what SPSS spits out... Thank you! Dimitri ############################################ ### Run the whole code below to create mysample: intercor<-0.3 # intercorrelation among all predictors k<-10 # number of predictors sigma<-matrix(intercor,nrow=k,ncol=k) # matrix of intercorrelations among predictors diag(sigma)<-1 require(mvtnorm) set.seed(123) mypop<-as.data.frame(rmvnorm(n=100000, mean=rep(0,k), sigma=sigma, method="chol")) names(mypop)<-paste("x",1:k,sep="") set.seed(123) mypop$x11<-sample(c(0,1),100000,replace=T) set.seed(123) be...

Hi I am struggling with nested random effects and hope someone can help. I have individuals (ID) who are nested within families (FAM). I want to model an outcome variable, and take account of the intercorrelation of individuals within each family. I think this amounts to two random effects, one nested within the other. How can I model this in R? So far I have tried using the

I'm running some GEE models but when I request the summary(pcb.gee) all I get are rows and rows of intercorelations and they fill up the screen buffer so I can not even scroll back to see what else might be in the summary. How do I get the summary function to NOT print the intercorrelations? Thanks, -- Dean Sonneborn Programmer Analyst Department of Public Health Sciences University of California, Davis (916) 734-6656

...variables in the columns (vars1) and another set of variables in the rows (vars2). The reason I want to keep one set of variables in the columns and another set of variables in the rows is because I have many variables and am only interested in the correlations of vars1 with vars2 (and not of the intercorrelations of variables among vars1 or among vars2). Thus, the correlogram with separate variables in the rows and columns would be more condensed and easier to examine visually. Can I address these issues in corrgram, or is there another package that will allow me to do that? Thanks in advance. -- View t...

Hello, I finally run my measurement model in sem - successfully. Now, I am trying to print out the path diagram that is based on the results - but for some reason it's not working. Below is my script - but the problem is probably in my very last line: # ANALYSIS OF ANXIETY, DEPRESSION, AND FEAR - LISREL P.31 library(sem) # Creating the ANXIETY, DEPRESSION, AND FEAR intercorrelation matrix

Dear all, I am quite new to R; facing certain problems: Say, I have a text file( named as "try"): Year C1 C2 C3 C4 C5 C6 Y1 3.5 13.8 9.5 6.8 0.4 24.2 Y2 3.8 13.9 9.9 7.6 0.7 12.8 Y3 4.5 14.5 14.2 9.2 0.6 14.5 Y4 5.9 16.2 24.6 12.7 0.2 24.3 Y5 7.2 20.4 40.6 18.2 0.8 28.2 Y6 5.9 18.6 37.4 14.5 0.3 36.9 Y7 8.0 16.1 88.6 24.1 0.1 34.6 Y8 13.6 21.1 56.3 19.0 0.7 33.3 I wish to import the

I am new to R. I just tried to recreate in R (using sem package and the identical input data) a solution for a simple measurment model I have found before in LISREL. LISREL had no problems and converged in just 3 iterations. In sem, I got no solution, just the warning message: "Could not compute QR decomposition of Hessian. Optimization probably did not converge. in: sem.default(ram =

Hello, Im reading through some manuals, but I cannot find my answer. I have a file containing many data: Vpn Code Family Age F1 F2 ... F17 1 1 M 46 1 2 ... 1 2 1 D 18 3 2 ... 4 3 2 M 50 3 3 ... 3 ... and so on. Now I can read it by: F = read.table("file", header=T) but now I want to seperate the mothers (M) and daugthers (D) of the family with all the data in all other fields.

I am not sure if this is an R-users question, but since most of you here are statisticians, I decided to give it a shot. I am using the lm() function in R to fit a dependent variable to a set of 3 to 5 independent variables. For this, I used the following commands: >model1<-lm(function=PBW~SO4+NO3+NH4) Coefficients: (Intercept) SO4 NO3 NH4 0.01323 0.01968

R-users At the moment I am teaching a course on the use of R for data analysis. Part of course requirements involves the transfer of results from R to something that resembles the APA (American Psychological Associations) type tables to MS Word. Until now I have used the HTML function in the R2HTML library, with a call like this: HTML(summary(model1), file("clipboard",