similar to: Iterate by Factor - Newbie Question

Hello, I'm using the "sem" package to do a confirmatory factor analysis on data collected with a questionnaire. In the model, there is a unique factor G and 23 items. I would like to calculate the standardized residual variance of the observed variables. "Sem" only gives the residual variance with the "summary" function, or the standardized loadings with the

I currently have a data frame whose rows correspond to each student and whose columns are different variables for the student, as shown below: Lastname Firstname CATALOG_NBR Email StudentID EMPLID Start 1 alastname afirstname 1213 *@uark.edu 10295236 # 12/2/2008 2 anotherlastname anotherfirstname 1213 **@uark.edu ## 10295236 9/3/2008 Xattempts Q1

On 14 October 2013 19:31, Arnold Schwaighofer <aschwaighofer at apple.com>wrote: > Renato, can you post the c code for the function and the assembly that gcc > produces? > Attached. Your initial example could be well handled by vectorization of strided > loops (and the mentioning of VLD3(.8?)/VST3(.8?) lead me to assume that > this is what happened). But the LLVM-IR you

Hello R users! I have several data frames where some of the variables have many missing observations. For example, Q1 in one of my data frames has over 66% of its observations missing. I have tried imputation with mice but it does not work for all the data frames and I get the following message or a similar message to this: iter imp variable 1 1 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11

Renato, can you post the c code for the function and the assembly that gcc produces? Your initial example could be well handled by vectorization of strided loops (and the mentioning of VLD3(.8?)/VST3(.8?) lead me to assume that this is what happened). But the LLVM-IR you sent has a store of 0 in there ;) and strides by 4. Thanks, Arnold Vectorization of strided loops: I am using float as the

On 21 September 2012 09:28, Sebastien DELDON-GNB <sebastien.deldon at st.com> wrote: > declare <16 x float> @llvm.arm.neon.vmaxs.v16f32(<16 x float>, <16 x float>) nounwind readnone > > llc fails with following message: > > SplitVectorResult #0: 0x2258350: v16f32 = llvm.arm.neon.vmaxs 0x2258250, 0x2258050, 0x2258150 [ORD=3] [ID=0] > > LLVM ERROR: Do not

I came across this problem moving from the standard jessie package to the backport: $ sudo dpkg --configure r-base-core Setting up r-base-core (3.3.2-1~jessiecran.0) ... Preserving user changes to /usr/share/bash-completion/completions/R (renamed from /etc/bash_completion.d/R)... mv: cannot stat ?/usr/share/bash-completion/completions/R?: No such file or directory dpkg:

R Experts, I have a dataframe made up of character vectors--these are results from survey questions. I need to convert them to factors. I tried the following which did not work: scs2<-sapply(scs2,as.factor) also this didn't work: scs2<-sapply(scs2,function(x) as.factor(x)) After doing either of above I end up with >str(scs2) chr [1:10, 1:10] "very important" "very

Hello Renato, You're pointing me at ARM intrinsics related to loads, problem that I've reported in original e-mail, is not support for vector loads, but support for 'vmaxs'. For instance, there is no vector loads of 16 floats in ARM ISA but it is legal to write in LLVM: ; ModuleID = 'vadd.ll' target datalayout =

This is almost ideal for SLP vectorization, except for two problems: 1. We have 4 stores to consecutive locations, but the last element is the constant zero, and not an additional SUB. At the moment we don’t have support for idempotence operations, but this is something that we should add. 2. The values that we are subtracting come from 3 loads. We usually load 4 elements from memory, or

Created corresponding unit test, and the optimization is bit exact with C function. This optimization speeds up SILK encoder on NEON as following. Fixed-point: Complexity 0-5: 0% Complexity 6-7: 6% Complexity 8-9: 10% Complexity 10: 8% Got similar results on floating-point. --- silk/NSQ_del_dec.c | 6 +- silk/SigProc_FIX.h | 4

I am doing some bibliometric analysis of interdisciplinarity using cluster analysis of co-authorship. I'd like to be able to specify the colour of individual authors in the labels to show a prior grouping by discipline (red for sociology authors, blue for economics authors, ..., that sort of thing). Is there any way of doing this sort of thing? I'm hoping for something like: h <-

NSQ_LPC_BUF_LENGTH is independent of DECISION_DELAY. --- silk/define.h | 4 ---- 1 file changed, 4 deletions(-) diff --git a/silk/define.h b/silk/define.h index 781cfdc..1286048 100644 --- a/silk/define.h +++ b/silk/define.h @@ -173,11 +173,7 @@ extern "C" #define MAX_MATRIX_SIZE MAX_LPC_ORDER /* Max of LPC Order and LTP order */ -#if( MAX_LPC_ORDER >

Hi all, I would like to know if LLVM Neon intrinsics are designed to support only 'Legal' types for NEON units. Using llc -march=arm -mcpu=cortex-a9 vmax4.ll -o vmax4.s on following ll code: ; ModuleID = 'vmax.ll' target datalayout = "e-p:32:32:32-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v64:64:64-v128:128:128-a0:0:64-n32" target triple =

Does something like this get you close: x <- list() keys <- LETTERS[1:6] # create for (i in keys){ x[[i]] <- data.frame(a=1:5, b=1:5, c=1:5) } # output output <- file('tempxx.txt', 'w') for (i in keys){ write.table(i, row.names=FALSE, col.names=FALSE, file=output, quote=FALSE) write.table(x[[i]], file=output, quote=FALSE) } close(output) On Wed, Jul 2,

Hi, I have reconstructed ancestral character states on a phylogeny using MuSSE in the diversitree package and plotted the character state probabilities as pie charts on the nodes. I would, however, like to colour the character states of my extant species, i.e. the tip labels, the same colours as my pie charts, such that all species in state 1 are e.g. blue, species in state 2 red and species in

Hi everybody, I'm mirroring some websites with rsync (daemon on the source), and I noticed in the destination host some error messages 'send_files failed to open filenames', Actually, the concerned files are deleted on the source host, but I would like to ask : * why does rsync say these (in which phase) ? * is these messages harmful or not ? Here below my rsync command and some

Background: OS: Linux Mandrake 10.1 release: R 2.0.0 editor: GNU Emacs 21.3.2 front-end: ESS 5.2.3 --------------------------------- Colleagues I am having some trouble extracting results from the function by, used to average variables in a data.frame first by one factor (depth) and then by a second factor (station). The real data.frame is quite large > dim(data.2001) [1] 32049 11 Here is a

Hi all, I am using R-3.0.1 under Linux platform to embed R into my C++ code. I am facing an error while executing more than 1 R-expressions parallelly. I am executing round(X) and abs(X) parallelly on a set of 50 input rows which resulted in segmentation fault after getting the following errors. Error: unprotect_ptr: pointer not found Error: argument to 'findVar' is not an environment

Hi, my call of optim() with the L-BFGS-B method ended with the following error message: ERROR: ABNORMAL_TERMINATION_IN_LNSRCH Further tracing shows: Line search cannot locate an adequate point after 20 function and gradient evaluations final value 0.086627 stopped after 7 iterations Could someone pls tell me whether it is possible to increase the limit of 20 evaluations? Is it even worth