similar to: Strange Inheritance of folder rights in Version 3.026a

I'm trying to build a SEM using the sem package. I'll attach my model specification below. When I turn debug=TRUE, it seems as if I'm getting to convergence because I get this message: Successive iterates within tolerance. Current iterate is probably solution. However, at the end of the process I get this message: Warning message: In sem.default(ram = ram, S = S, N = N,

Dear all, I'm attaching a patch that should fix the issue mentioned above. It simply makes the same check seen in the same file for global variables: emitPTXAddressSpace(PTy->getAddressSpace(), O); if (GVar->getAlignment() == 0) O << " .align " << (int) TD->getPrefTypeAlignment(ETy); else O << " .align " <<

https://bugzilla.netfilter.org/show_bug.cgi?id=1432 Bug ID: 1432 Summary: ebtables ebtables-2.0.11 buffer overflow on getting kernel data ( ebtables compiled with address sanitizer) Product: netfilter/iptables Version: unspecified Hardware: x86_64 OS: Debian GNU/Linux Status:

Dear R users, Version 2.1-1 of the sem package, for structural equation modeling, is now on CRAN. Unlike version 2.0-0, which was a major overhaul of the package, version 2.1-1 just sprinkles some syntactic sugar on it, introducing the specifyEquations() and cfa() functions; specifyEquations() supports model specification in equation (rather than path) format, and cfa() facilitates compact

Dear R users, Version 2.1-1 of the sem package, for structural equation modeling, is now on CRAN. Unlike version 2.0-0, which was a major overhaul of the package, version 2.1-1 just sprinkles some syntactic sugar on it, introducing the specifyEquations() and cfa() functions; specifyEquations() supports model specification in equation (rather than path) format, and cfa() facilitates compact

Hi list, Sorry for the vague title, but here is the scenario. I?m writing an R package, let?s say, ?pkg1?, which contains 3 functions: f1, f2, f3. f2 and f3 are helper functions for f1, i.e. f1 calls f2 which in turn calls f3. f1 <- function(?) { ?. f2() ? } f2 <- function(?){ ? f3(?) ? } f3 <- function(...){ .... } Then, I wrote a new version of f3 and I want to

Hi everyone check this out [R-1.7.0]: R> f1 <- function(x){x^2} R> f1 -> f2 R> f2(4) [1] 16 R> R> function(x){x^2} -> f3 function(x){x^2} -> f3 R> f3(4) Error: couldn't find function "f3" Why does right assignment "->" work in the first but not the second case? Can anyone else reproduce this? -- Robin Hankin, Lecturer, School of

Hi, I am doing a hierarchical CFA using the sem package. I have 20 items, and I have 2 factors (F3 and F4), and also F1 and F2 are nested within F3. Here is the code that I have, but it is giving me an error message "Warning message: In eval(expr, envir, enclos) : Negative parameter variances. Model may be underidentified." and a further error "Error in summary.objectiveML(cfa,

I've written a patch to add option-key submenus to menu.c32 and vesamenu.c32 for doing things like changing the video mode. This was inspired the the patch that SuSE applies to the syslinux family and Grub, without the ugly pseudo-PostScript spaghetti. The patch may be found at http://www.valleyhold.org/~gordons/syslinux-optmenus.diff (it's over 30K, so posting it here didn't seem

Dear R-List, (I am not sure whether this list is the right place for my question...) I have a dataframe df.cfa

Hi, I would like to eliminate all the load instructions and replace their uses with the stored values in the following program. The stores and loads are in the same basic block. Is there an optimization pass in LLVM 3.0 that can do this? define void @testFunc() { entry: %sVal = alloca %sTy %f1 = getelementptr %sTy* %sVal, i32 0, i32 0 store i32 789, i32* %f1 %f2 = getelementptr %sTy*

First of all, I do not understand why conversion to characters are not allowed. That's what Sundar's solution is doing implicitly (but more elegantly). Here's a test of all three. See the function definitions below. > f1 <- factor(sample(letters[1:3], 1e4, replace=TRUE)) > f2 <- factor(sample(letters[3:5], 1e4, replace=TRUE)) > f3 <- factor(sample(letters[5:7],

Someone can help me? I tried several things and always don't converge # Model library(sem) dados40.cov <- cov(dados40,method="spearman") model.dados40 <- specify.model() F1 -> Item11, lam11, NA F1 -> Item31, lam31, NA F1 -> Item36, lam36, NA F1 -> Item54, lam54, NA F1 -> Item63, lam63, NA F1 -> Item65, lam55, NA F1 -> Item67, lam67, NA F1 ->

Hello, FYI, this is a bug http://llvm.org/bugs/show_bug.cgi?id=13324 When compiling the following code for sm_20, func params are by some reason given with .align 0, which is invalid. Problem does not occur if compiled for sm_10. > cat test.ll ; ModuleID = '__kernelgen_main_module' target datalayout = "e-p:64:64-i64:64:64-f64:64:64-n1:8:16:32:64" target triple =

Dear all I would like to ask you about possible bug in using bwplot (from lattice) together with Hmisc library attached. I found it in my actual data, but here is a toy example. It appears only when some levels are missing. library(lattice) library(Hmisc) # preparing data x1<-rnorm(10,5,1) x2<-rnorm(10,5,5) x3<-rnorm(10,1,1) x4<-rnorm(10,1,5) x<-c(x1,x2,x3,x4) x<-c(x,x+5)

Hi, I have a data frame where one column is a list of lists. I would like to subset the data frame based on membership of the lists in that column and be able to 'denormalise' the data frame so that a row is duplicated for each of its list elements. Example code follows: # The data is read in in this form with the c2 list values in single strings which I then split to give lists: > f1

R-experts: In 2002, Hans Van Houwelingen et al. published a tutorial on how to do meta-regression in Statistics in Medicine. They used the classic BCG dataset of Colditz to demonstrate correct methodology and computed the results using PROC MIXED in SAS. In trying to duplicate the results presented in this paper, I have discovered that I can reproduce certain items with lmer but not

Dear R-help list, I am trying to do a mixed ANOVA on a 8960 x 5 dataframe. I have 3 factors for which I want to test all main effects and interactions : f1 (40 levels), f2 (7 levels), and f3 (4 levels). I also have a subject factor, subject, and a dependent variable, dv. Some more information about the factors: f2 is a between-subject factor. That is, for each level of f2 there are 8 nested

Hi, when I use use subset in a data.frame, all empty levels are maintained in the new table. > test <- data.frame(a=as.factor(rep(c("f1","f2","f3"),10)),b=rep(c(1,2,3),10)) > summary(test) a b f1:10 Min. :1 f2:10 1st Qu.:1 f3:10 Median :2 Mean :2 3rd Qu.:3 Max. :3 > test2 <-

Dear users, i'm using the sem package in R, because i need to improve a confermative factor analisys. I have so many questions in my survey, and i suppose, for example, that Question 1 (Q1) Q2 and Q3 explain the same thing (factor F1), Q4,Q5 and Q6 explain F2 and Q7 and Q8 explain F3... For check that what i supposed is true, i run this code to see if the values of loadings are big or not.